The document summarizes a lecture on cooperation in multi-agent systems. It discusses different types of cooperation including emergent cooperation without explicit communication, and cooperation with explicit communication like deliberative cooperation using partial global planning and negotiation techniques like the contract net protocol. It provides examples of how distributed vehicle monitoring problems can be solved using partial global planning where agents generate and optimize partial global plans by exchanging local plans.
This lecture discusses implicit cooperation in multi-agent systems through indirect means such as the environment or societal structures. It begins by defining implicit cooperation as agents behaving in a socially coordinated way to solve problems without direct communication. Examples given include football where simple signals work better than explanations, and reactive robots that are too simple for complex plans. The lecture then covers options for implicit cooperation such as observing others' actions and imposing organizational structures. A key example of implicit coordination is ant-based routing algorithms, where ants modify pheromone probabilities to indirectly guide other ants along shortest paths. The lecture concludes by discussing reasoning about other agents through modeling and about society through mechanisms like social laws, power relations, and electronic institutions that define norms to shape
1) The document discusses 8 key properties that an intelligent agent should have: flexibility, reactivity, proactiveness, social ability, rationality, reasoning capabilities, learning, and autonomy.
2) Reactivity means an agent can respond to changes in its environment. Proactiveness means an agent can exhibit goal-directed behavior by taking initiative.
3) Social ability allows an agent to interact and cooperate with other agents via communication. Rationality means an agent will act to achieve its goals based on its beliefs.
This lecture discusses agent communication in multi-agent systems. It covers blackboard systems where agents share information on a common blackboard, and message passing where agents directly communicate messages. The lecture also discusses speech acts which describe the intentions behind agent communications, such as requests, queries, and informs. Standards like FIPA help agents from different systems understand each other's communications.
This presentation deals with Multi Agent Systems and their application in industry and research. The presentation has beenmade by Zahia Guessoum (www-poleia.lip6.fr/~guessoum) Maître de Conférence at the Université Pierre et Marie Curie and me.
Explain Communication among agents in Artificial IntelligenceGurpreet singh
Agents in a multi-agent system can communicate and cooperate to solve problems. They communicate through various methods like point-to-point, broadcast, or mediated communication to coordinate actions and share information. Common communication approaches include blackboard-based communication where agents access a shared information space, and message passing where agents directly exchange varied information to facilitate cooperation.
The document provides an overview of distributed artificial intelligence and multi-agent systems. It discusses topics such as the definition of DAI, types of multi-agent systems, interaction among agents, the Agent Communication Language KQML, basic models of communication, and the definition of an agent. It also covers concepts like reactive agents, cognitive agents, classification of agents, and applications of DAI.
Multi-agent systems can be viewed as a software architecture style consisting of autonomous components called agents. The agents interact through message passing according to a predefined protocol. There are different organizational styles for multi-agent systems including hierarchical, flat, subsumption, and modular organizations. Effective multi-agent systems require specially designed communication protocols that fit the agent architecture, organization, and tasks. Standard communication languages and protocols are increasingly used to facilitate conversations between agents from different systems.
How women think robots perceive them – as if robots were men Matthijs Pontier
In previous studies, we developed an empirical account of user engagement with software agents. We
formalized this model, tested it for internal consistency, and implemented it into a series of software agents to
have them build up an affective relationship with their users. In addition, we equipped the agents with a module
for affective decision-making, as well as the capability to generate a series of emotions (e.g., joy and anger). As
follow-up of a successful pilot study with real users, the current paper employs a non-naïve version of a Turing
Test to compare an agent’s affective performance with that of a human. We compared the performance of an
agent equipped with our cognitive model to the performance of a human that controlled the agent in a Wizard
of Oz condition during a speed-dating experiment in which participants were told they were dealing with a
robot in bot h conditions. Participants did not detect any differences between the two conditions in the
emotions the agent experienced and in the way he supposedly perceived the participants. As is, our model can
be used for designing believable virtual agents or humanoid robots on the surface level of emotion expression.
This lecture discusses implicit cooperation in multi-agent systems through indirect means such as the environment or societal structures. It begins by defining implicit cooperation as agents behaving in a socially coordinated way to solve problems without direct communication. Examples given include football where simple signals work better than explanations, and reactive robots that are too simple for complex plans. The lecture then covers options for implicit cooperation such as observing others' actions and imposing organizational structures. A key example of implicit coordination is ant-based routing algorithms, where ants modify pheromone probabilities to indirectly guide other ants along shortest paths. The lecture concludes by discussing reasoning about other agents through modeling and about society through mechanisms like social laws, power relations, and electronic institutions that define norms to shape
1) The document discusses 8 key properties that an intelligent agent should have: flexibility, reactivity, proactiveness, social ability, rationality, reasoning capabilities, learning, and autonomy.
2) Reactivity means an agent can respond to changes in its environment. Proactiveness means an agent can exhibit goal-directed behavior by taking initiative.
3) Social ability allows an agent to interact and cooperate with other agents via communication. Rationality means an agent will act to achieve its goals based on its beliefs.
This lecture discusses agent communication in multi-agent systems. It covers blackboard systems where agents share information on a common blackboard, and message passing where agents directly communicate messages. The lecture also discusses speech acts which describe the intentions behind agent communications, such as requests, queries, and informs. Standards like FIPA help agents from different systems understand each other's communications.
This presentation deals with Multi Agent Systems and their application in industry and research. The presentation has beenmade by Zahia Guessoum (www-poleia.lip6.fr/~guessoum) Maître de Conférence at the Université Pierre et Marie Curie and me.
Explain Communication among agents in Artificial IntelligenceGurpreet singh
Agents in a multi-agent system can communicate and cooperate to solve problems. They communicate through various methods like point-to-point, broadcast, or mediated communication to coordinate actions and share information. Common communication approaches include blackboard-based communication where agents access a shared information space, and message passing where agents directly exchange varied information to facilitate cooperation.
The document provides an overview of distributed artificial intelligence and multi-agent systems. It discusses topics such as the definition of DAI, types of multi-agent systems, interaction among agents, the Agent Communication Language KQML, basic models of communication, and the definition of an agent. It also covers concepts like reactive agents, cognitive agents, classification of agents, and applications of DAI.
Multi-agent systems can be viewed as a software architecture style consisting of autonomous components called agents. The agents interact through message passing according to a predefined protocol. There are different organizational styles for multi-agent systems including hierarchical, flat, subsumption, and modular organizations. Effective multi-agent systems require specially designed communication protocols that fit the agent architecture, organization, and tasks. Standard communication languages and protocols are increasingly used to facilitate conversations between agents from different systems.
How women think robots perceive them – as if robots were men Matthijs Pontier
In previous studies, we developed an empirical account of user engagement with software agents. We
formalized this model, tested it for internal consistency, and implemented it into a series of software agents to
have them build up an affective relationship with their users. In addition, we equipped the agents with a module
for affective decision-making, as well as the capability to generate a series of emotions (e.g., joy and anger). As
follow-up of a successful pilot study with real users, the current paper employs a non-naïve version of a Turing
Test to compare an agent’s affective performance with that of a human. We compared the performance of an
agent equipped with our cognitive model to the performance of a human that controlled the agent in a Wizard
of Oz condition during a speed-dating experiment in which participants were told they were dealing with a
robot in bot h conditions. Participants did not detect any differences between the two conditions in the
emotions the agent experienced and in the way he supposedly perceived the participants. As is, our model can
be used for designing believable virtual agents or humanoid robots on the surface level of emotion expression.
This document provides an introduction to software agents, discussing key dimensions of agenthood including autonomy, intelligence, and sociality. It describes how agents can exhibit these dimensions through internal components like beliefs, goals, and plans. Autonomous agents require an internal state and ability to initiate behaviors. Intelligent agents may use reasoning, learning, and decision-making. Social agents can communicate and interact with other agents through models of other agents and capabilities like negotiation. Mobility refers to agents' ability to change locations physically or between execution environments. The document outlines common software constructs used to facilitate these dimensions in agent architectures.
Exploiting incidental interactions between mobile devicesRaúl Kripalani
This document discusses three projects that exploit incidental interactions on mobile devices: 1) Amigo uses Bluetooth to construct a social network representation and associate contacts with calendar events. 2) Co-presence Communities extends Amigo by mining co-presence data to discover recurring group meetings. 3) BluScreen is a public display that uses co-presence data to provide feedback to an agent marketplace allocating presentation time slots.
Presence, a critical feature of interactive media is here described as a neuropsychological phenomenon, evolved from the interplay of our biological and cultural inheritance, whose goal is the enaction of the volition of the self: presence is the non mediated (prereflexive) perception of successful intentions in action.
An agent based approach for building complex software systemsIcaro Santos
1) The document discusses an agent-based approach for developing complex software systems. It argues that agent-oriented approaches are well-suited for building distributed systems due to their ability to model complexity, interactions, and organizational relationships.
2) Complex systems inherently exhibit hierarchy, nearly decomposable subsystems, and changing interactions. An agent-based approach models a system as autonomous agents that can achieve objectives through flexible and decentralized interactions.
3) Key advantages of the agent approach include its use of agents, interactions, and organizations as natural abstractions to represent subsystems, components, and relationships in complex systems. It also allows runtime determination of interactions to reduce coupling between components.
This document discusses human-computer interaction and interaction models. It provides objectives for describing elements of interaction models, identifying how ergonomics influences interaction, how interface styles influence dialog, and identifying interaction paradigms. Models of interaction discussed include Norman's execution-evaluation cycle and Abowd and Beale's framework. Translations between the user, input, system, and output are explained. Examples are given of how to apply these models to understand issues in interaction.
HCI has evolved over time from focusing on system components and tasks to considering socially embedded interactions. Early HCI emphasized usability and enabling human capabilities through technologies like graphical UIs [first sentence]. As computing expanded beyond workplaces, the field incorporated theories of context, activity, and culture to understand user experiences [second sentence]. Modern HCI focuses on designing with users through methods like prototyping and uses a range of qualitative research approaches to study technology use in natural settings [third sentence].
Distributed cognition is an approach that views cognition as extending beyond individuals to include interactions between people and tools or objects in their environment. It recognizes that cognitive processes involve interactions between internal and external representations. Analyzing a distributed cognitive system involves examining how information is propagated through communicative pathways between internal human representations and external artifacts. The DiCoT framework provides dimensions for analyzing physical layout, information flow, and artifacts to understand how a distributed system supports its goals.
This document is a 3-page exam for a Human Computer Interaction course. It contains 4 parts testing students' knowledge of HCI concepts and principles. Part 1 has 6 true/false questions worth 1.5 points each about system design and interface factors. Part 2 contains 8 multiple choice questions worth 2 points each related to HCI influences, usability, and interaction terms. Part 3 requires discussing the importance of HCI for e-business systems, describing 4 interaction styles, explaining human characteristics for design, and differentiating between slips and mistakes as human errors. The exam is out of a total of 35% and covers a range of foundational HCI topics.
The document discusses the history and evolution of paradigms in human-computer interaction (HCI). It describes several paradigm shifts in interactive technologies including: batch processing, time-sharing, interactive computing, graphical displays, personal computing, the World Wide Web, ubiquitous computing. Each new paradigm created a new perception of the human-computer relationship.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Self-talk discrimination in Human-Robot Interaction Situations For Engagement...Jade Le Maitre
This document describes a study on developing a metric to characterize engagement in human-robot interaction situations for cognitive stimulation exercises with elderly users. The researchers designed a triadic situation involving a user, a computer providing exercises, and a robot providing encouragement. They analyzed social signals like self-talk and system-directed speech during wizard-of-oz experiments. An automatic recognition system was developed to detect these dialogue acts, achieving 71% accuracy. The durations of detected acts were combined to estimate an "Interaction Effort" measure of user engagement during exercises. The measure effectively captured engagement levels of elderly patients in cognitive stimulation tasks.
A participatory modelling method for co-designing a shared representation of ...ILRI
This document discusses participatory modeling methods for developing a shared representation or model of a system among stakeholders. It describes the participatory modeling approach known as "ComMod", which involves stakeholders in iteratively designing, testing, and refining conceptual models through tools like role-playing games and agent-based simulations. ComMod has been applied to over 30 cases of natural resource management issues to help facilitate discussion, improve dialogue, and enable co-design of solutions among conflicting stakeholders by developing a common representation they all engage with. Qualitative modeling methods were tested early in a ComMod process to model avian influenza surveillance and control systems in Laos by identifying key stakeholders and mapping interactions between variables.
The document provides an introduction to human-computer interaction (HCI). It defines HCI as the study of the interaction between humans and computers, including the design and evaluation of interactive systems. The document discusses why HCI is important, focusing on creating usable, intuitive systems. It also outlines some of the historical roots of HCI in fields like computer graphics, operating systems, and cognitive psychology. Finally, it discusses potential future developments in HCI, such as ubiquitous computing, mixed media interfaces, and more natural human-computer interaction.
This document proposes a model for a social chatbot agent that can be used in serious games for training communicative skills. It discusses the limitations of current scripted approaches and proposes a social practice theory-based architecture that incorporates social intelligence into conversational agents. The architecture defines the agent's identity through beliefs, social practices knowledge, dialogue state, and rules for generating plans. It also enhances the AIML language to support modeling of social practices for more natural conversation management.
ARTIFICIAL INTELLIGENCE, COGNITIVE TECHNOLOGIES AND DIGITAL LABOREmmanuel Gillain
bring a simple and concise summary of what the cognitive technologies enabling “Digital Labor” mean in order to raise the awareness level amongst the non technical people that care about the technology impacts on business, economy and society.
This document discusses personal learning environments (PLEs) that are widget-based. It describes how PLEs are a network of people, artifacts, and tools that positively influence the development of an individual's competencies. The document outlines four types of competencies - social, professional, methodological, and personal. It also discusses the roles of planning, reflection, monitoring, acting, and interacting in competency development. The document advocates for qualitative interviews to understand PLE use and provides examples of widget-based applications that could be incorporated into a PLE.
USER EXPERIENCE AND DIGITALLY TRANSFORMED/CONVERTED EMOTIONSIJMIT JOURNAL
The document describes a new model called Measuring User Experience using Digitally Transformed/Converted Emotions (MUDE) which measures two metrics of user experience (satisfaction and errors) using facial expressions and gestures captured by an Intel interactive camera. An experiment was conducted with 70 participants who used a software application while their facial expressions and gestures were recorded. The results from the camera were then compared to responses from a System Usability Scale questionnaire to determine if attitudes towards usability matched between the two methods. The study found consistency between the camera-captured emotions and questionnaire responses regarding usability. The MUDE model provides a new approach to evaluating user experience based on digitally measuring emotions expressed during interaction.
Industrial applications of multi-agent systems was discussed. Key points included:
- Agent technology has been adopted in domains like manufacturing control, production planning, logistics, and supply chain integration where distributed control and open systems are needed.
- Main bottlenecks to adoption are awareness, risk, and lack of mature tools. Common agent concepts used include coordination, negotiation, distributed planning, and interoperability.
- Examples of deployed systems include control of engine assembly plants, production planning systems, logistical routing of transport orders, and supply chain integration platforms. Future challenges include greater integration with hardware.
This document provides an overview of different agent architectures, including reactive, deliberative, and hybrid architectures. It discusses key concepts like the types of environments agents can operate in, including accessible vs inaccessible, deterministic vs non-deterministic, episodic vs non-episodic, and static vs dynamic environments. Reactive architectures are focused on fast reactions to environmental changes with minimal internal representation and computation. Deliberative architectures emphasize long-term planning and goal-driven behavior using symbolic representations. Rodney Brooks proposed that intelligence can emerge from the interaction of simple agents following stimulus-response rules, without complex internal models, as seen in ant colonies.
This document provides an introduction to software agents, discussing key dimensions of agenthood including autonomy, intelligence, and sociality. It describes how agents can exhibit these dimensions through internal components like beliefs, goals, and plans. Autonomous agents require an internal state and ability to initiate behaviors. Intelligent agents may use reasoning, learning, and decision-making. Social agents can communicate and interact with other agents through models of other agents and capabilities like negotiation. Mobility refers to agents' ability to change locations physically or between execution environments. The document outlines common software constructs used to facilitate these dimensions in agent architectures.
Exploiting incidental interactions between mobile devicesRaúl Kripalani
This document discusses three projects that exploit incidental interactions on mobile devices: 1) Amigo uses Bluetooth to construct a social network representation and associate contacts with calendar events. 2) Co-presence Communities extends Amigo by mining co-presence data to discover recurring group meetings. 3) BluScreen is a public display that uses co-presence data to provide feedback to an agent marketplace allocating presentation time slots.
Presence, a critical feature of interactive media is here described as a neuropsychological phenomenon, evolved from the interplay of our biological and cultural inheritance, whose goal is the enaction of the volition of the self: presence is the non mediated (prereflexive) perception of successful intentions in action.
An agent based approach for building complex software systemsIcaro Santos
1) The document discusses an agent-based approach for developing complex software systems. It argues that agent-oriented approaches are well-suited for building distributed systems due to their ability to model complexity, interactions, and organizational relationships.
2) Complex systems inherently exhibit hierarchy, nearly decomposable subsystems, and changing interactions. An agent-based approach models a system as autonomous agents that can achieve objectives through flexible and decentralized interactions.
3) Key advantages of the agent approach include its use of agents, interactions, and organizations as natural abstractions to represent subsystems, components, and relationships in complex systems. It also allows runtime determination of interactions to reduce coupling between components.
This document discusses human-computer interaction and interaction models. It provides objectives for describing elements of interaction models, identifying how ergonomics influences interaction, how interface styles influence dialog, and identifying interaction paradigms. Models of interaction discussed include Norman's execution-evaluation cycle and Abowd and Beale's framework. Translations between the user, input, system, and output are explained. Examples are given of how to apply these models to understand issues in interaction.
HCI has evolved over time from focusing on system components and tasks to considering socially embedded interactions. Early HCI emphasized usability and enabling human capabilities through technologies like graphical UIs [first sentence]. As computing expanded beyond workplaces, the field incorporated theories of context, activity, and culture to understand user experiences [second sentence]. Modern HCI focuses on designing with users through methods like prototyping and uses a range of qualitative research approaches to study technology use in natural settings [third sentence].
Distributed cognition is an approach that views cognition as extending beyond individuals to include interactions between people and tools or objects in their environment. It recognizes that cognitive processes involve interactions between internal and external representations. Analyzing a distributed cognitive system involves examining how information is propagated through communicative pathways between internal human representations and external artifacts. The DiCoT framework provides dimensions for analyzing physical layout, information flow, and artifacts to understand how a distributed system supports its goals.
This document is a 3-page exam for a Human Computer Interaction course. It contains 4 parts testing students' knowledge of HCI concepts and principles. Part 1 has 6 true/false questions worth 1.5 points each about system design and interface factors. Part 2 contains 8 multiple choice questions worth 2 points each related to HCI influences, usability, and interaction terms. Part 3 requires discussing the importance of HCI for e-business systems, describing 4 interaction styles, explaining human characteristics for design, and differentiating between slips and mistakes as human errors. The exam is out of a total of 35% and covers a range of foundational HCI topics.
The document discusses the history and evolution of paradigms in human-computer interaction (HCI). It describes several paradigm shifts in interactive technologies including: batch processing, time-sharing, interactive computing, graphical displays, personal computing, the World Wide Web, ubiquitous computing. Each new paradigm created a new perception of the human-computer relationship.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Self-talk discrimination in Human-Robot Interaction Situations For Engagement...Jade Le Maitre
This document describes a study on developing a metric to characterize engagement in human-robot interaction situations for cognitive stimulation exercises with elderly users. The researchers designed a triadic situation involving a user, a computer providing exercises, and a robot providing encouragement. They analyzed social signals like self-talk and system-directed speech during wizard-of-oz experiments. An automatic recognition system was developed to detect these dialogue acts, achieving 71% accuracy. The durations of detected acts were combined to estimate an "Interaction Effort" measure of user engagement during exercises. The measure effectively captured engagement levels of elderly patients in cognitive stimulation tasks.
A participatory modelling method for co-designing a shared representation of ...ILRI
This document discusses participatory modeling methods for developing a shared representation or model of a system among stakeholders. It describes the participatory modeling approach known as "ComMod", which involves stakeholders in iteratively designing, testing, and refining conceptual models through tools like role-playing games and agent-based simulations. ComMod has been applied to over 30 cases of natural resource management issues to help facilitate discussion, improve dialogue, and enable co-design of solutions among conflicting stakeholders by developing a common representation they all engage with. Qualitative modeling methods were tested early in a ComMod process to model avian influenza surveillance and control systems in Laos by identifying key stakeholders and mapping interactions between variables.
The document provides an introduction to human-computer interaction (HCI). It defines HCI as the study of the interaction between humans and computers, including the design and evaluation of interactive systems. The document discusses why HCI is important, focusing on creating usable, intuitive systems. It also outlines some of the historical roots of HCI in fields like computer graphics, operating systems, and cognitive psychology. Finally, it discusses potential future developments in HCI, such as ubiquitous computing, mixed media interfaces, and more natural human-computer interaction.
This document proposes a model for a social chatbot agent that can be used in serious games for training communicative skills. It discusses the limitations of current scripted approaches and proposes a social practice theory-based architecture that incorporates social intelligence into conversational agents. The architecture defines the agent's identity through beliefs, social practices knowledge, dialogue state, and rules for generating plans. It also enhances the AIML language to support modeling of social practices for more natural conversation management.
ARTIFICIAL INTELLIGENCE, COGNITIVE TECHNOLOGIES AND DIGITAL LABOREmmanuel Gillain
bring a simple and concise summary of what the cognitive technologies enabling “Digital Labor” mean in order to raise the awareness level amongst the non technical people that care about the technology impacts on business, economy and society.
This document discusses personal learning environments (PLEs) that are widget-based. It describes how PLEs are a network of people, artifacts, and tools that positively influence the development of an individual's competencies. The document outlines four types of competencies - social, professional, methodological, and personal. It also discusses the roles of planning, reflection, monitoring, acting, and interacting in competency development. The document advocates for qualitative interviews to understand PLE use and provides examples of widget-based applications that could be incorporated into a PLE.
USER EXPERIENCE AND DIGITALLY TRANSFORMED/CONVERTED EMOTIONSIJMIT JOURNAL
The document describes a new model called Measuring User Experience using Digitally Transformed/Converted Emotions (MUDE) which measures two metrics of user experience (satisfaction and errors) using facial expressions and gestures captured by an Intel interactive camera. An experiment was conducted with 70 participants who used a software application while their facial expressions and gestures were recorded. The results from the camera were then compared to responses from a System Usability Scale questionnaire to determine if attitudes towards usability matched between the two methods. The study found consistency between the camera-captured emotions and questionnaire responses regarding usability. The MUDE model provides a new approach to evaluating user experience based on digitally measuring emotions expressed during interaction.
Industrial applications of multi-agent systems was discussed. Key points included:
- Agent technology has been adopted in domains like manufacturing control, production planning, logistics, and supply chain integration where distributed control and open systems are needed.
- Main bottlenecks to adoption are awareness, risk, and lack of mature tools. Common agent concepts used include coordination, negotiation, distributed planning, and interoperability.
- Examples of deployed systems include control of engine assembly plants, production planning systems, logistical routing of transport orders, and supply chain integration platforms. Future challenges include greater integration with hardware.
This document provides an overview of different agent architectures, including reactive, deliberative, and hybrid architectures. It discusses key concepts like the types of environments agents can operate in, including accessible vs inaccessible, deterministic vs non-deterministic, episodic vs non-episodic, and static vs dynamic environments. Reactive architectures are focused on fast reactions to environmental changes with minimal internal representation and computation. Deliberative architectures emphasize long-term planning and goal-driven behavior using symbolic representations. Rodney Brooks proposed that intelligence can emerge from the interaction of simple agents following stimulus-response rules, without complex internal models, as seen in ant colonies.
MAS course at URV. Lecture 4, agent types (specially interface agents, information agents, hybrid systems, agentification). Based on diverse resources.
Lect6-An introduction to ontologies and ontology developmentAntonio Moreno
The document provides an overview of ontologies and ontology development:
1. It defines ontologies as explicit specifications of conceptualizations in a domain that define concepts, properties, attributes, and relationships to enable knowledge sharing.
2. Ontology components include concepts, properties, restrictions, and individuals. Ontologies can range from single large ontologies to several specialized smaller ones.
3. OWL is introduced as the standard language for representing ontologies, with features like classes, properties, restrictions, and logical operators.
4. A general methodology for ontology development is outlined, including determining scope, reusing existing ontologies, enumerating terms, and defining classes, properties, and other components in an iterative
Introduction to agents and multi-agent systemsAntonio Moreno
Multi-agent systems course at University Rovira i Virgili. Slides mostly based on those of Rosenschein, from the content of the book by Wooldridge.
Lecture 1-Introduction to agents and multi-agent systems.
This document summarizes different voting mechanisms for cooperation in multi-agent systems, including their properties and issues. It discusses plurality voting, binary voting, Borda voting, and Condorcet voting. For each method, it describes how votes are cast and tallied, desirable properties, and problems like strategic voting, irrelevant alternatives affecting the outcome, and the possibility of circular ambiguities with no consensus winner. It provides examples to illustrate how the outcome can depend on the specific voting protocol used.
Poster presented at the 2014 European Conference on Artificial Intelligence - Unsupervised semantic clustering of Twitter hashtags - automatic topic detection in Twitter
The document summarizes key aspects of negotiation protocols in multi-agent systems, focusing on auctions. It discusses negotiation factors and elements, protocol rules and evaluation criteria. It then provides an overview of common auction types, including English auctions (ascending price, outcry), Dutch auctions (descending price), and sealed-bid auctions. The roles of participants and typical auction processes are also summarized.
1. The document summarizes several projects developed by members of ITAKA involving applications of multi-agent systems.
2. One project involves developing a multi-agent system to provide personalized recommendations of touristic activities to tourists based on their preferences.
3. Another project involves using agents to automatically construct ontologies representing domains of knowledge by discovering relevant terms, resources, and relationships from the web in an unsupervised way.
4. A third project mentioned uses a multi-agent system for distributed task execution, but no details are provided.
MAS course - Lect12 - URV health care applicationsAntonio Moreno
This document summarizes a lecture on applications of multi-agent systems (MAS) in healthcare by the ITAKA research group at URV. It outlines two MAS projects developed by ITAKA: PalliaSys, which used agents to manage data for palliative care patients, and K4Care, a web-based platform for home care services. The lecture discusses the benefits of MAS for distributed, coordinated healthcare problems and describes how the projects implemented agent-based monitoring, decision support, and clinical guideline enactment. It also notes challenges in MAS research and adopting agent technologies in healthcare domains.
This document discusses developing an ontology-based semantic web application for the biological domain. It introduces the need for semantic technologies to help machines better understand and combine biological information from different sources. The document outlines the methodology, which involves defining concepts, properties, and relations in the biological domain to create an ontology. It also discusses implementing a semantic web application using the Jena framework to retrieve and manipulate biological data modeled with ontologies and RDF. The goal is to build a semantic search framework to improve information retrieval for biologists.
Touch screen technology has evolved significantly over the past decades. Early touch screens from the 1960s used capacitive technology, while technologies developed further in the 1990s brought touch screens to smartphones and handheld devices. The main touch screen technologies discussed are resistive, capacitive, acoustic, infrared, optical, and dispersive. Each has its advantages and disadvantages related to factors like cost, durability, optical clarity, and accuracy. Touch screens now have wide applications in areas like public displays, retail, control systems, and assistive technologies. Projected capacitive touch has become the leading technology. Advances continue toward more flexible, transparent, and durable multi-touch screens.
The document announces the IADIS International Conference on Intelligent Systems and Agents 2008, which was held in Amsterdam, The Netherlands from July 22-24, 2008. It includes the table of contents for the conference proceedings, listing papers presented on topics related to intelligent systems and agents. The proceedings contain full papers, short papers, and posters accepted from over 97 submissions from 26 countries on areas such as intelligent systems, agents, multi-agent systems, machine learning, and semantic technologies.
Software Agents for Internet of Things - at AINL 2014Anton Kolonin
The Aigents software platform allows Internet users to receive timely and personalized information from across the web and their communities through intelligent agents. These agents utilize distributed semantic search and learn from user feedback to deliver precisely relevant results while connecting users within social networks. The platform is available via various devices and as free or paid services hosted by communities and businesses.
Electronic Negotiation and Mediation SupportMatteo Damiani
This document provides an overview and summary of electronic negotiation and mediation support systems. It begins by defining different types of negotiation support systems, including negotiation support systems (NSS), electronic negotiation systems (ENS), electronic negotiation tables (ENT), and negotiation software agents (NSA). It then categorizes these systems based on their level of intelligence, degree of intervention, role in the negotiation process, and which negotiation phases they support. The document summarizes several key studies that have empirically compared different negotiation support systems and their impact. It finds that while NSS generally improve outcomes, the research results are inconsistent and more rigorous empirical frameworks are needed.
T9. Trust and reputation in multi-agent systemsEASSS 2012
The credibility model in ReGreT evaluates the credibility of witnesses in two ways:
1. Direct trust in the witness - The trust that the agent has directly in the witness based on its past interactions. This is calculated using the direct trust model.
2. Reliability of the witness' reputation value - This measures how reliable or volatile the reputation values provided by the witness tend to be. It is calculated based on the number of outcomes the witness has observed and the deviation in its ratings.
The credibility model combines these two factors - direct trust and reliability - to get an overall credibility value for each witness. This credibility value is then used to weight the reputation values provided by each witness. Witnesses with higher credibility will have
This document provides an overview of a presentation on automated negotiation given by Takayuki Ito from Nagoya Institute of Technology in Japan. The presentation covered four parts: an introduction to automated negotiation, bargaining approaches using game theory, multi-issue negotiation using heuristic approaches, and an automated negotiation agent competition. Ito discussed key concepts in negotiation like negotiation protocols, strategies, preferences, and Pareto optimality. For bargaining approaches, he summarized Nash's cooperative bargaining solution and Rubinstein's alternating offers model in non-cooperative games.
- The document is a slide presentation on semantic analysis in language technology that discusses the semantic web and ontologies. It provides examples of question answering systems like START, Siri, and IBM Watson and discusses the evolution of the web from Web 1.0 to Web 2.0 to the proposed Web 3.0. It also introduces key concepts like ontologies, semantic metadata, and the role of semantics in allowing machines to process information.
Presentation to the European Union\'s Alcohol & Health Forum\'s Marketing & Communications taskforce, outlining how Diageo applies the same rigour of self-regulation to new/digital media as more traditional advertising. (May 2008)
This document discusses multi-agent systems and provides an overview of key research directions. It defines agents and multi-agent systems, including cooperative and competitive systems. Important topics in multi-agent systems research are discussed such as distributed problem solving, organizational structures, communication limitations, and learning in multi-agent systems. Overall, the document outlines the diverse field of multi-agent systems and highlights open research questions.
This document discusses how complex cognition and behavior can emerge from the interaction of multiple simple agents or components, without centralized control. It provides examples from cognitive science theories that posit intelligent behavior results from interactions among many simple processes, like Minsky's "Society of Mind" theory. The document defines agents as autonomous entities that can be software, robots, or people. It describes how complex behaviors can emerge from the interactions between agents in a "society of agents", whether they are homogeneous or heterogeneous. The interactions can occur through an environment, by sensing each other, or through communication.
Dr. Sara Manzoni's lecture discusses interactions in multi-agent systems. She defines a multi-agent system as a modeling approach that considers simple or complex activities as the result of interactions between autonomous agents. She describes how to model a problem as a structured set of interacting agents that can act, interact, perceive their environment, and pursue objectives using their skills and resources. The lecture covers designing multi-agent systems by modeling the agents, organization, interactions, and environment. It also discusses different types of interactions that can occur based on the compatibility of agent goals, availability of resources, and skills. Finally, the lecture presents models for direct and indirect agent interactions, including examples like KQML and blackboard systems.
(4) Essay «About Networks, Networked And Network Centric Organizations»Vadim Salnikov
Day after day you take part and control numerous different projects. You attain your goals. But what if we deflect our attention away from the ordinary project, and reflect on and dream all together about what projects will look like in the nearest future. How will the activity of professional category of managers, including you, change?
If you are interested so far, the next real stage of management progress will be networked and network-centric organizations management…
The document discusses intelligent agents and related concepts. It begins by defining agents based on general definitions from dictionaries and literature. It then provides more specific definitions of intelligent agents from various sources. The document distinguishes between weak and strong notions of agency. It also compares and contrasts agents with objects and expert systems. The document discusses different types of environments that agents can operate in and how this impacts agent design. It concludes by discussing distributed artificial intelligence and multi-agent systems at a high level.
This document provides an overview of agents and multi-agent systems. It discusses key trends in computer science like ubiquity, intelligence, delegation, and human-orientation that have led to the emergence of multi-agent systems. The document outlines challenges in agent technology like developing reasoning capabilities for agents and ensuring user confidence and trust. It also discusses objections to multi-agent systems regarding whether it is just distributed systems or artificial intelligence.
This document discusses various real-world applications of multiagent systems:
1) Multiagent systems are used in movie special effects to simulate large crowds and battles involving thousands of characters.
2) They are used to model transportation systems and simulate traffic, with each vehicle represented as an autonomous agent.
3) Multiagent systems are applied to logistics planning and scheduling, such as modeling production in a factory where each job is handled by an intelligent software agent.
Why do learners cooperate? hints from network sciences on motivation for coll...Fabio Nascimbeni
This document discusses how insights from network science can help explain learner motivation for collaborative learning. It notes that network thinking is becoming more important in many fields, including education. Adopting a collaborative approach has costs, but humans tend to use strategies like direct and indirect reciprocity to encourage cooperation. Four conditions support collaborative learning: confidence, commitment, allowing for divergence, and decentralization. The role of "collaboration dynamisers" who weave networks is also important. Network analysis methods can help measure new things, reveal motivational patterns, improve support, and increase network thinking in education.
This document summarizes a presentation on how social networks impact organizational learning. It discusses two key concepts - organizational learning, which is how organizations acquire, retain, and apply knowledge, and social networks, which embody past knowledge and shape future knowledge transfer. The presentation examines the relationship between these two concepts, exploring how networks impact knowledge creation, retention, and transfer within organizations. It uses a case study of student company networks and learning to empirically test these relationships.
This document discusses structured dialogic design (SDD) as a methodology for facilitating large group collaboration and decision making around complex problems. It outlines some key challenges with large group work, including complexity, lack of shared understanding, and limited cognitive abilities. SDD provides a structured process and graphic tools to help large groups unpack complexity, build shared understanding, and make informed decisions through techniques like clustering observations, identifying influence relationships, and developing action plans. The document includes examples of SDD being used to address barriers to public participation in broadband access.
Agent-Based Modeling for Sociologists is a crash course on how to build ABM in the social sciences. This presentation has an introduction to OOP and then discusses three models in details, along with their NetLogo implementation
An ecosystem consists of projects that interact and collaborate with each other through shared goals, team members, customers, technology or value chains. Projects within an ecosystem maintain autonomy but also benefit from synergies. Modern communication technologies have reduced barriers to collaboration, making it more efficient to organize resources through collaboration to achieve larger goals. Ecosystems can support groups of organizations in learning from each other through shared infrastructure and exploring mutually beneficial ways of working together at low cost. This increased success and reduced risk for investors.
Stigmergic Economy And Large-Scale, Decentralized Networks - Matan FieldNetwork Society Research
This document discusses the evolution of large-scale coordination from rigid hierarchies to decentralized networks. It proposes a new stigmergic model of coordination based on indirect and spontaneous coordination between agents through signals left in the environment. This would allow for distributed, global coordination without the need for centralized control. The document outlines a new protocol called BACKFEED that could support this type of decentralized collaborative organization through a distributed consensus mechanism, reputation system, and value distribution model to incentivize cooperation.
The document discusses cooperative distributed problem solving (CDPS) in multi-agent systems. It defines CDPS as agents with distinct but related expertise working together to solve problems that no single agent could solve alone. There are three main stages to CDPS: 1) problem decomposition, 2) sub-problem solution, and 3) solution synthesis. Task and result sharing are important aspects of CDPS that allow agents to divide work and integrate solutions. The Contract Net protocol is used for efficient task sharing, while result sharing involves iterative information exchange to progressively refine solutions. Coordination and coherence are important metrics for effective CDPS.
Memetic Governance. Seminar ECCO, VUB. University of Brussels 2011Øyvind Vada
Øyvind Vada’s work is about how governance can be executed in a world where the public, private and third sectors are changing rapidly due to globalization and increased complexity. How we, as individuals, think, talk, decide and act together in all types of social systems, both locally and globally, is a function of a more and more interwoven world. Classical reductionist and hierarchical approaches to governance tend to fail due to these changes.
To reduce the gap between governance theory and governance practice, Vada argues that there is a need for new approaches that embrace complexity. He has developed a memetic approach for doing so, taking into account that we as individuals belong to different formal and informal social systems. These systems can be regarded as combinations of hierarchies, networks and markets.
Individuals and groups of individuals in social systems are, in Vada’s approach, treated as agents. As agents, we are free and goal-directed entities that maximize utility, benefit and/or fitness. We often have local and limited knowledge, and cannot always foresee effects of our individual actions on larger collective wholes.
Governing organizations includes governing agents. Vada argues that it is possible to design for a desired emergent outcome, where agents interpret predefined memes that influence how they perceive and process themselves, their surroundings and the tasks at hand. Different sets of predefined memes are created as tools and cognitive templates that form and process subjective thoughts, communications and actions, both individually and collectively.
Vada proposes an alternative way of allocating resources and exercising control and coordination in social systems – a new form of governance. He suggests a method where memes are instrumentally infused into social systems through processes where free and bounded rational agents are regarded as participants and players that impact their surroundings based on their own subjective agency. He shows how agents become carriers of shared memes in different arenas for diffusion and adaption. The predefined memes are formed as iconic and discrete models that can be applied to individual day-to-day situations as well as complex collective challenges. In the arenas, memes are woven into active exercises and assignments. Individual agents recognize the value of other agents’ viewpoints, make sense of the social systems they are part of and collectively create solutions that reduce the gap between the system’s strategic intent and its operational success.
The main task of Vada’s work is to merge an improved version of memetics with the intentions of classical governance. He has created a replicable method, which is potentially applicable in all organizations. The method seeks to balance a designed and planned approach to steering and coordination with emergent factors that are always present when human agency takes place.
Collaborative E Learning Assistant NetworkPat Parslow
The document discusses the concept of machine consciousness (MC) and its potential use in eLearning assistants. It proposes that MC is possible if a system is capable of recognizing, classifying, modeling, communicating and predicting. An MC eLearning assistant could hold internal models of learners and itself to adapt instruction and provide a caring experience. However, developing full MC raises ethical issues that would need to be addressed.
Daniel Franc (Google): How To Grow A Global Online CommunityFeverBee Limited
Daniel Franc explains how he began Google's meetup group and gradually grew it into the global phenomenon it is today. Plenty of excellent tips for applying advanced social sciences to build powerful online communities.
Harnessing Collective Intelligence: Shifting Power To The EdgeMike Gotta
Socially-oriented systems create inter-connections across groups and communities that enable workers to leverage the collective intelligence of an organization. Sense-making tools and decision-making systems are more critical than ever before but need to be re-invented for a net-centric environment.
Mdp 511 2012 organizations in development - session 1-2ANDREA_BEAR
The snail pushes through a green night, slowly making its way across the grass and earth. Its movement is described as deliberate and purposeful, though it is unknown what exactly drives its progress. Any traces of its passage would be subtle, like a thin broken trail. Its fury or passion, if any, is slow but persistent. The poem reflects on the snail's mysterious and methodical journey through a nightscape.
This document discusses multi-agent systems and their applications. It provides examples of multi-agent systems for spacecraft control, manufacturing scheduling, and more. Key points:
- Multi-agent systems consist of interacting intelligent agents that can cooperate, coordinate, and negotiate to achieve goals. They offer benefits like robustness, scalability, and reusability.
- Challenges include defining global goals from local actions and incentivizing cooperation. Games like the prisoner's dilemma model social dilemmas around cooperation versus defection.
- The document outlines architectures like the blackboard model and BDI (belief-desire-intention) model. It also provides a manufacturing example using the JADE platform.
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)Antonio Moreno
Presentation at workshop on recommender systems at WI-2014.
Automatic learning of keyword-based preferences through the analysis of the implicit information provided by the interaction of the user.
Automatic and unsupervised topic discovery in social networksAntonio Moreno
Research seminar given at the Poznan University of Technology, Poland, June 2014. The topic was the automatic and unsupervised discovery of topics in social networks.
This document outlines the course requirements for a master's program, including required courses in the first, second, and third semesters totaling 120 ECTS. The first semester (S1) focuses on introductory multi-agent systems and computational intelligence courses totaling 30 ECTS. The second semester (S2/S3) allows for optional courses across areas like human-computer interaction, machine learning, computer vision and robotics, and computational intelligence totaling 39 ECTS. The third semester (S3) requires courses in machine learning, computational vision and computational intelligence totaling 21 ECTS, with a focus on modeling, reasoning, problem solving and professional practice totaling 9 ECTS.
URV Master on Computer Engineering: Computer Security and Inteligent SystemsAntonio Moreno
This document outlines the course requirements for a Master's in Computer Engineering with a specialization in Computer Security and Intelligent Systems. The program is divided into two years totaling 90 credits. The first year consists of 60 credits covering topics like software architecture, cryptography, neural networks, and numerical methods. The second year is 30 credits including a project, knowledge representation, and optional topics in areas such as biometrics, privacy, and multi-agent systems. Students also complete an external practical work and master's thesis.
On the application of multi-agent systems in Health CareAntonio Moreno
1) The document discusses applying multi-agent systems to healthcare, including areas like medical data management, decision support, planning, and remote care.
2) It describes the K4Care project, which developed an agent-based home care system with services like admitting patients, creating care plans, and applying individual intervention plans.
3) The K4Care system uses ontologies and procedural knowledge to coordinate care among actors like doctors and nurses. Agents execute care plans and adapt their behavior based on patient information.
Multi-agent systems applied in Health CareAntonio Moreno
This document discusses the application of multi-agent systems in healthcare. It provides an overview of some projects developed by ITAKA, including a web-based platform for home care services and a system for managing clinical guidelines. It also outlines some research challenges in using agents for healthcare, such as standardization, security, and integration with existing systems. Overall, the document argues that agents are well-suited for coordinating distributed healthcare tasks and knowledge, but challenges remain in adoption due to technical and organizational issues.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
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Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Zilliz
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
1. LECTURE 7:
Cooperation in MAS (I)
Artificial Intelligence II – Multi-Agent Systems
Introduction to Multi-Agent Systems
URV, Winter–Spring 2010
2. Outline of the lecture
Coordination
Kinds of cooperation in MAS
Emergent cooperation
Cooperation with explicit communication
Deliberative
Partial Global Planning
Negotiation
Contract Net
Auctions [specific lecture]
3. What is Coordination?
“Coordination is the process of managing
interdependencies between activities ”
- Malone and Crowston, 1991
Coordination problems occur when:
• An agent has a choice in its actions within some task, and the
choice affects its and other agents’ performance
• The order in which actions are carried out affects performance
• The time at which actions are carried out affects performance
4. Subproblem Interdependencies (I)
Subproblems are the same/overlapping, but
different agents have either alternative
methods or data that can be used to
generate a solution
Subproblems are part of a larger problem in
which a solution to the larger problem
requires that certain constraints exist among
the solutions to its subproblems
5. Subproblem Interdependencies (II)
It is not possible to decompose the problem
into a set of subproblems such that there is a
perfect fit between the location of
information, expertise, processing, and
communication capabilities in the agent
network and the computational needs for
effectively solving each subproblem
Contention for resources among the
subproblems
6. Implications of subproblem interdependencies
It may be impossible to completely solve one
subproblem without first partially solving another
subproblem
Solving or partially solving one subproblem may simply
make it easier to solve another subproblem
Knowing the solution to one subproblem may obviate
the need to solve another
How an agent orders which subgoals to do and when
to communicate the (partial) results of solving a
subgoal can significantly affect global performance
7. Another vision of coordination
Deciding for each agent in the context of other agent
activities
What activities it should do, when it should do them and
how it should do them (planning, scheduling)
What it should communicate, when it should communicate
and to whom (cooperation)
Domain information and Control information
Communication and scheduling of activities are
intimately connected
This is a highly complex computational problem,
especially if optimal solutions are required
8. Many approaches to coordination
Type of information available about static and
dynamic behaviour of agents
Cost of acquiring current state of other agents
Cost of finding out the abilities of other agents
Importance of optimal solution
Cost of computing coordination decisions
Implications of generating non-optimal coordination
Real-time requirements
How long you have to make a decision
There is no one best approach to Coordination
Complex Multi-attributed Optimization Problem
9. Cooperation hierarchy (Franklin)
MAS
Independent Cooperative
Self-interested Benevolent
Discrete Emergent With Without
communication - communication -
Explicit Implicit
Deliberative Negotiators
10. Benevolent Agents
If we “own” the whole system, we can design
agents to help each other whenever asked
In this case, we can assume agents are
benevolent: our best interest is their best
interest
Problem-solving in benevolent systems is
cooperative distributed problem solving
Benevolence simplifies the system design
task enormously!
[ Example: practical exercise]
11. Self-Interested Agents
If agents represent individuals or
organizations, then we cannot make the
benevolence assumption
E.g. buyers/sellers in e-commerce
Agents will be assumed to act to further
their own interests, possibly at the
expense of others
Potential for conflict
It may complicate the design task
enormously!
12. Cooperation hierarchy (Franklin)
MAS
Independent Cooperative
Self-interested Benevolent
Discrete Emergent With Without
communication - communication -
Explicit Implicit
Deliberative Negotiators
13. Discrete MAS
Independent agents
Each agent pursues its own agenda
The agendas of the agents bear no
relation to one another
For example, one agent can filter e-mail while
another one gathers information from the Web
No cooperation
14. MAS with emergent behaviour
Agents can cooperate with no intention of
doing so
The system can exhibit high-level, complex,
intelligent, coordinated behaviour without
any designed coordination mechanisms,
just as a side effect of the interactions
among agents
Example [recall reactive architectures]
Puck gathering robots – Beckers
15. Puck gathering robots
World with a set of pucks
Set of autonomous, independent robots, that
can pick up pucks and drop them
A simple rule-based individual behaviour of
each robot, without any communication or
coordination with the other robots, can lead to
a complex global system behaviour (putting
all the pucks together in a pile)
16. Behavioural rules (I)
Rule 1 – Pick up pucks
If (there is not a puck in the gripper) & (there
is a puck ahead) then take the puck in the
gripper
Rule 2 – Drop pucks together
If (there is one puck in the gripper) & (there is
a puck ahead) then drop the puck, go
backward for one second and turn at a
random angle
17. Behavioural rules (II)
Rule 3 – Exploration
If there are no pucks ahead then go forward
Rule 4 – Avoid obstacles
If there is an obstacle (wall or other robot)
ahead then avoid the obstacle (turn at a
random angle and go forward)
18.
19.
20. Important Factors
Agents
Don't need to know about each other, don’t
communicate with each other
Don't have special roles
Loosing a few doesn't matter
Environment
Acts as a communication mechanism
Is affected by the actions of all individuals
Result: surprisingly coherent group
(coordinated) action
21. Subsumption as Coordination
Robotics as a Multi-Agent System
Brooks Subsumption Architecture:
Layers of controllers
Each layer creates a competence
Higher layers subsume lower layers
Can be seen, at a high level of abstraction,
as coordination of autonomous entities
Each layer takes its own decisions
22. Intelligence as emergent behaviour
Basic element of human intelligence: neural
activity
Neurons: very small element with very little
computational power
The interaction between an enormous
amount of neurons leads to human-level
complex thought patterns and intelligence !
23. Cooperation hierarchy (Franklin)
MAS
Independent Cooperative
Self-interested Benevolent
Discrete Emergent With Without
communication - communication -
Explicit Implicit
Deliberative Negotiators
24. Cooperative agents
The agendas of the agents include
cooperating with other agents in the system
in some way
Explicitly: intentional sending and receiving of
communicative signals (e.g. via a common
blackboard or via messages)
Implicitly: without explicit messages (e.g.
observing and reacting to the behaviour of the
other agents of the system)
25. Cooperation hierarchy (Franklin)
MAS
Independent Cooperative
Self-interested Benevolent
Discrete Emergent With Without
communication - communication -
Explicit Implicit
Deliberative Negotiators
26. Deliberative agents
Agents with inference and planning
capabilities
Some kind of explicit distributed
planning mechanism, based on
information exchange, addressed to
solve collectively a given problem
27. Partial Global Planning (PGP) –
Durfee, Lesser
Distributed planning technique
Integrates planning and execution
Not the usual Planning-Scheduling-Action cycle
Dynamic domains with unpredictable, unreliable
information
The tasks are inherently distributed; each agent
performs its own task
Initially applied in the Distributed Vehicle
Monitoring (DVM) problem, then extended to be
domain independent
29. Goal in the DVM problem
The agents are not aware of the global state
of the system; however, there is a common
goal: converge on a consistent map of
vehicle movements by integrating the partial
tracks formed by different agents into a
single complete map or into a consistent set
of local maps distributed among agents
Coordination by means of partial plans
exchange
30. Difficulties in DVM (I)
The data sensed in an area cannot be
exhaustively processed in a timely manner
Huge volume of incoming data
Many noisy data generated by
sensors/environment, which should not be
processed
Correlations between data sensed in nearby
locations provide constraints on whether/how
that data should be processed
31. Difficulties in DVM (II)
Sensor overlap implies possible processing
duplication
The same data should not be processed by
different agents
Sensing demands in an area vary heavily
dynamically
The allocation of work should be done in a very
dynamic way, depending on the workload of each
agent at each moment
32. Partial Global Planning phases
1. Create local plans
2. Exchange local plans
3. Generate Partial Global Plans
4. Optimize Partial Global Plans
33. PGP steps (I)
1- Create the local plan of each agent
Each agent represents its own expected activity
(to solve its assigned tasks) with a local
(tentative) plan, at two levels:
higher level – most important steps (actions) to be
followed to solve the problem, abstract plan
lower level –it specifies primitive operations to achieve
the next step in the abstract plan; as the plan is
executed, new details are added incrementally
34. Schematic view of a local plan
Length1 Length2
A4
Local Plan
(Actions) A1 A2 A3
A5
O1 O2 O3
Next operations
(short term details)
35. Local plan characteristics
Local plans may involve alternative actions
depending on the results of previous actions
and changes in the environment [conditional
plans]
See actions A4 and A5 in the previous slide
They have to be dynamically modifiable in an
easy way
36. Plan components
Name
Creation time
Set of objectives to achieve
List of planned actions for data processing
Major steps in the plan
Set of primitive problem-solving operations
(short-term details)
Predictions of how long each action will take
and the expected outcome of each action
Rating (plan importance)
37. Basic components of an action
Preconditions for the action
Results of the action
Set of data to be processed by the action
Set of procedures to be applied to the data
Estimated start time of the action
Estimated end time of the action
Estimated degree of confidence in the result
38. Ordering actions within a plan
Prefer actions that concurrently achieve
multiple goals
Prefer actions expected to require less
resources (especially time)
Prefer actions that will strongly verify or
refute that some goals are worth pursuing
E.g. analysis that confirms that a signal follows
a vehicle trace detected by a neighbour agent
39. PGP steps (II)
2- Exchange plans
The agents exchange
information about their local
plans with other agents
(usually high-level information)
Goals
Long-term strategy
Plan rating
40. Distribution of local plans
Each agent must have knowledge about the
MAS organisational structure, so that it can
infer the role of each agent in the problem
solving process and decide which information
to send to which agents
It would be very expensive and inefficient to
send all the local plans to all the agents in the
system !!!
E.g. a police car probably doesn’t need to
exchange its plans with all the fire trucks and
ambulances
41. Nodes meta-level organisation
Information that each node must know to
infer the organisational structure
Nodes it has authority over
Nodes that have authority over it
Nodes with equal authority
42. Coordination possibilities
Centralised coordination
A node has authority over all other nodes
Hierarchical coordination
Each node has 1 “boss” and some “subordinate”
nodes
Lateral coordination
All nodes have the same authority
43. PGP steps (III)
3- Generate Partial Global Plans (PGPs)
Each agent models the collective activity of the system,
by combining the received local partial plans into a
Partial Global Plan
Check dependencies between the received information
and its own local plan
Identify when the goals of one or more agents can be
considered subgoals of a single global goal: partial global
goal
Identify opportunities to improve coordination
E.g. two agents having to solve the same subproblem
44. Components of a PGP (I)
Partial global goal: final aim of the
global plan
Plan activity map: plan actions to be
executed concurrently by itself and the
other agents, including costs and
expected results of actions
Initially, it will contain the union of all
the actions of all local plans
45. Criteria for rating the actions in the
Plan Activity Map
The action extends a partial result
E.g. vehicle tracking hypothesis
The action produces a partial result that
might help some other agents in forming
partial results
How long the action is expected to take
46. Components of a PGP (II)
From the modified Plan Activity Map, the
agent builds a Solution Construction Graph:
how the agents should interact, including
specifications about
what partial results to exchange
when to exchange them
who to exchange them with
It must take into account the estimated time
of the actions, the results they will provide,
etc.
47. PGP steps (IV)
4- Optimize Partial Global Plans
Each agent has a Planner Module,
especialised in analyzing the received
information to detect if there are several agents
working on the same goal. This information is
put in the Plan Activity Map, along with the
expected future behaviour and expected
results of the other agents
48. Optimizing Plans
Local plan + Plan Activity Map =>
New modified Local Plan
Optimized using the updated knowledge of the
system
Solution Construction Graph
Concrete details about when to send particular
results to specific agents in the future
49. Possible optimizations of Local Plan
Task reordering
Change the order of the actions in the plan
Task reallocation
Move some actions to nodes without assigned
work
Weight of authority
Change local plan according to the decisions of
the nodes that have more authority
This information is obtained by analyzing the local plans
of nodes with higher authority
50. Benefits of PGP
Systems with highly dynamic behaviour
All plans can be adapted to dynamic changes in
the environment => flexibility
However, if an agent changes its local plan, it has
to inform other agents (e.g. those that were
waiting for a partial result)
Efficiency
If different agents work on the same/similar
subproblems, they will notice that fact in their local
plans and reassign their tasks appropriately
51. Negotiation techniques
Cooperation mechanisms with explicit
information exchange in which there
is some sort of competition between
the agents
Auctions [specific lecture]
English / Dutch / Vickrey / FPSB
Multi-attribute auctions
Combinatorial auctions
Contract Net
Others …
52. Task Sharing and Result Sharing
Two main modes of cooperative problem
solving:
task sharing:
activities are distributed among the agents of
the system
result sharing:
information (partial or final results) is
distributed
53. The Contract Net
A well known task-sharing protocol for task
allocation is the Contract Net:
1. Recognition
2. Announcement
3. Bidding
4. Awarding
5. Expediting
[More general and detailed presentation than in the
lecture about communication protocols]
54. 1-Recognition
In this stage, an agent recognizes
it has a problem it wants help with
An agent has a goal, and either…
realizes it cannot achieve the goal in isolation —
does not have capability
realizes it would prefer not to achieve the goal in
isolation (typically because of solution quality,
deadline, use of resources, etc.)
55. 2-Announcement
In this stage, the agent with the task sends out
an announcement of the task which includes a
specification of the task to be achieved
Specification must encode:
Description of the task itself
Any constraints (e.g. deadlines, quality constraints)
Meta-task information (e.g. preference on attributes)
The announcement is then broadcast
56. 3-Bidding
Agents that receive the announcement
decide for themselves whether they
wish to bid for the task
Factors:
agent must decide whether it is capable of expediting
task
agent must evaluate the cost of making the task and the
benefits it can get from making it
If an agent chooses to bid, then it submits a
tender, detailing the conditions on which it can
execute the task
57. 4-Awarding
The agent that sent the task
announcement must
choose between bids &
decide who to “award the
contract” to
The result of this process is
communicated to the
agents that submitted a bid
58. 5-Expediting
The successful contractor then expedites
the task
That may involve generating further
manager-contractor relationships: sub-
contracting
59. Contract Net
The collection of nodes is the “contract net”
Each node on the network can, at different
times or for different tasks, be a manager or a
contractor
When a node gets a composite task (or for
any reason can’t solve its present task), it
breaks it into subtasks (if possible) and
announces them (acting as a manager),
receives bids from potential contractors, and
then awards the job
60. Issues for Implementing Contract Net
How to…
… specify tasks?
… specify quality of service?
… select between competing offers?
… differentiate between offers based on
multiple criteria?
68. Contract Net node modules
Local database
Knowledge base, info. on the state of negotiations
and the state of the solution of tasks
Interface module
Sends/receives messages, deals with the
communication with the other nodes
Task processor
Executes the tasks assigned to the node
Contract processor
Studies new offered tasks, submits bids,
formalizes contracts
69. Domain-Specific Evaluation
Task announcement message prompts
potential contractors to use domain specific
task evaluation procedures
E.g. identifying important attributes
There is deliberation going on, not just selection —
perhaps no tasks are suitable at present
Manager considers the submitted bids using a
domain specific bid evaluation procedure
70. Efficiency Modifications
Focused addressing — when general
broadcast isn’t required
Agents could automatically learn which are the
most appropriate nodes for common tasks
Directed contracts — when manager already
knows which node is appropriate
For instance when a very similar task has already
been done in the past
The nodes can make proactive offers to
potential managers of the kind of tasks they
are able to execute
72. Features of Contract Net Protocol
Two-way dynamic transfer of information
Mutual selection
Bidders select from among task announcements
Managers select from among bids
Local evaluation
Preserving autonomony and private information
of agents
73. Suitable Applications
Hierarchy of Tasks
Subtasks are large enough (and it’s
worthwhile to spend effort to distribute
them wisely)
Primary concerns are distributed control,
achieving reliability, avoiding bottlenecks
74. Limitations
Other stages of distributed problem solving
are non-trivial:
Problem Decomposition
Solution Synthesis
Computational overhead
Messages
Time – deliberation, analyze offer/bid, wait for
decisions
75.
76. Ideas for the practical exercise (I)
Emergent cooperation
Different firemen could be made to fight
together a certain fire, without explicit
coordination mechanisms
E.g. each fire truck goes to the nearest fire
Reactive agents, rule-based behaviour of each
fireman
Different police cars could be working on the
same road blocking
77. Ideas for the practical exercise (II)
Explicit cooperation
Exchange of messages between
firemen/policemen/ambulances (or
higher-level coordinators) to distribute
fires, form teams, coordinate joint
movements, etc.
Some kind of global planning
78. Ideas for the practical exercise (III)
Negotiation
Different firemen could “negotiate” who fights a
certain fire
Negotiation via auctions or via Contract Net
It could even be a multi-attribute auction,
depending on their distance to the fire, whether
they are already fighting other fires or on their
way to other fires, ...
79. Readings for this week
Chapter 8 of the book An introduction to
MultiAgent Systems (M. Wooldridge), 2nd ed.
Chapter 4 of the book Agentes Software y
Sistemas Multi-Agente (A. Mas)
Paper on cooperation hierarchy (Doran et al.)
Paper on Partial Global Planning (Durfee,
Lesser)
Paper on Contract Net (Smith)