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
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 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.
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 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.
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.
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.
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 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.
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 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.
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.
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.
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.
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.
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].
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.
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.
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 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.
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.
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.
This chapter introduces agent-oriented programming (AOP) as a new programming paradigm based on a cognitive view of computation. AOP models agents as having mental states consisting of beliefs, capabilities, commitments, and choices. Agents communicate through speech acts like informing, requesting, and promising. Two example scenarios illustrate AOP concepts like reasoning about beliefs and coordinating commitments between agents. The chapter outlines the key components of an AOP system, including a language for describing mental states, an agent programming language, and a way to turn devices into programmable agents.
The document discusses using patterns and social network analysis to manage competencies in collaborative networks. It proposes modeling a collaborative network using i* modeling to define actors, their intentions, and dependencies. Social network analysis measures like closeness and betweenness are then used to define patterns of interactions. These patterns and the network model can be used to extract competencies of members and identify competencies needed to bridge gaps between intentions and reality. The approach aims to fill the gap between intended and actual collaboration. Future work should consider contextual factors and empirical pattern evaluation.
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.
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.
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.
MAS course at URV. Lecture 4, agent types (specially interface agents, information agents, hybrid systems, agentification). Based on diverse resources.
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.
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.
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 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.
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.
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].
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.
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.
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 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.
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.
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.
This chapter introduces agent-oriented programming (AOP) as a new programming paradigm based on a cognitive view of computation. AOP models agents as having mental states consisting of beliefs, capabilities, commitments, and choices. Agents communicate through speech acts like informing, requesting, and promising. Two example scenarios illustrate AOP concepts like reasoning about beliefs and coordinating commitments between agents. The chapter outlines the key components of an AOP system, including a language for describing mental states, an agent programming language, and a way to turn devices into programmable agents.
The document discusses using patterns and social network analysis to manage competencies in collaborative networks. It proposes modeling a collaborative network using i* modeling to define actors, their intentions, and dependencies. Social network analysis measures like closeness and betweenness are then used to define patterns of interactions. These patterns and the network model can be used to extract competencies of members and identify competencies needed to bridge gaps between intentions and reality. The approach aims to fill the gap between intended and actual collaboration. Future work should consider contextual factors and empirical pattern evaluation.
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.
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.
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.
MAS course at URV. Lecture 4, agent types (specially interface agents, information agents, hybrid systems, agentification). Based on diverse resources.
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.
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.
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 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.
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
Poster presented at the 2014 European Conference on Artificial Intelligence - Unsupervised semantic clustering of Twitter hashtags - automatic topic detection in Twitter
Antihistamines block the effects of histamine released during allergic reactions, reducing symptoms like dilated blood vessels and permeability, but can cause drowsiness, dizziness, and dry mouth. Decongestants reduce stuffiness by acting on the respiratory system with possible side effects of dizziness and dryness. Mucolytics thin mucus making it easier to cough up by liquefying it, though may lead to upset stomach, vomiting, or rash.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
The West And The World Social Blogger RafaelDaniel
The document discusses the rise of the West and its impact on global trade networks between the 15th-19th centuries. European maritime powers like Portugal, Spain, France and Britain established colonial outposts around the world, disrupting existing trade routes and establishing new economic systems centered around raw materials extracted from colonies. This colonial expansion connected most of Afro-Eurasia and the Americas into a single global economy dominated by Western European interests for the first time in history. While indigenous cultures survived, colonialism profoundly affected civilizations and established Western political and economic hegemony that still shapes the modern world order.
Iceberg, Dead Ahead - Lessons From Aviation DisastersMike Walsh
What can a real world disaster teach us about running better projects, changing our attitudes and putting our best foot forward as professionals and technologists? Quite a lot actually. In this very interactive presentation, I discuss attitudes and actions that lead to disaster, how we can find them in ourselves and some tools and techniques for avoiding them.
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.
The document discusses autonomic multi-agent systems and self-awareness. It covers:
1) The objectives of understanding fundamental properties of autonomic systems and how agents can use environmental awareness for self-organization.
2) An overview of multi-agent systems, autonomic systems, and representative approaches like dynamic norm-governed systems.
3) How awareness can enable self-healing through maintaining congruence between rules and system state.
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.
Network and spatial analysis for forest governanceCIFOR-ICRAF
Presented by Dr. Matt Hamilton, The Ohio State University, USA and Dr. Caleb Gallemore, Lafayette College, USA, on 10 November 2020 at "International workshop: Enhancing wetland management and sustainable development"
Contribution Of Sociotechnical Systems Theory Concepts To A Framework Of Terr...Myra Frazier
This document discusses key concepts from socio-technical systems theory and their relevance to a framework for territorial intelligence. It outlines several systems concepts including open and closed systems, bounded rationality, learning and adaptation, emerging properties, and types of decision making processes. These concepts can help define a territory as a system and aid in organizational assessment and collective decision making for territorial intelligence and sustained development.
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.
The document discusses modelling large complex systems using multi-agent technology. It describes complex systems as consisting of many interdependent and autonomous components that exhibit emergent and unpredictable behavior from nonlinear interactions. The only technology capable of accurately modelling complex systems is said to be multi-agent systems, where software agents interact and self-organize to produce intelligent emergent behavior. Several examples are given of multi-agent models being used successfully in commercial applications.
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.
Coordination of Complex Socio-technical Systems: Challenges and OpportunitiesStefano Mariani
The issue of coordination in Socio-Technical Systems (STS) mostly stems from “humans-in-the-loop”: besides software-software we have software-human interactions to handle, too. Also, a number of peculiarities and related engineering challenges makes a socio-technical gap easy to rise, in the form of a gap between what the computational platform provides, and what the users are expecting to have. In this paper we try to shed some light on the issue of engineer- ing coordination mechanisms and policies in STS. Accordingly, we highlight the main challenges, the opportunities we have to deal with them, and a few selected approaches for specific STS application domains.
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
Explains in short what is Systems Thinking, and its basic concepts. This PPT shows what is a System, its characteristics and what Systems thinking can do for us.
Swarm intelligence is an artificial intelligence technique inspired by the collective behavior of decentralized and self-organized systems found in nature, such as ant colonies and bird flocks. Two common swarm intelligence algorithms are ant colony optimization and particle swarm optimization. Ant colony optimization is based on the behavior of real ant colonies and can be used to find approximate solutions to difficult optimization problems. Particle swarm optimization is a population-based stochastic optimization technique inspired by swarming behavior in nature, such as bird flocking. It searches for optimal solutions within a problem space by updating the movement of individual particles based on their own experiences and those of neighboring particles.
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.
The social network analysis (SNA), branch of complex systems can be used in the construction of multiagent
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1. LECTURE 10:
Cooperation in MAS (IV):
implicit methods
Artificial Intelligence II – Multi-Agent Systems
Introduction to Multi-Agent Systems
URV, Winter-Spring 2010
2. Outline of the lecture
Implicit cooperation in MAS
Indirect cooperation through the
environment
Societal views of MAS
Electronic institutions
Organizational structures
3. Coordination [recall past lectures]
An activity is a set of potential operations an actor
(an agent playing a certain role) can perform, with
the aim of achieving a given goal or set of goals
Coordination could be defined as the process of
managing dependencies between activities. By
such process an agent reasons about its local
actions and the foreseen actions that other
agents may perform, with the aim to make the
community to behave globally in a coherent
manner
4. Cooperation hierarchy [last lectures]
MAS
Independent Cooperative
Self-interested Benevolent
Discrete Emergent With Without
communication - communication -
Explicit Implicit
Reactive
systems
Deliberative Negotiators
Partial Global Auctions
Planning
Coalition Voting
formation Contract Net
5. Implicit cooperation
A group of distributed cooperative agents
behaves in a socially coordinated way in the
resolution of a global problem without an
explicit exchange of communication
messages
In many cases the environment acts as the
(indirect) interaction mechanism
6. Motivation (I)
Cases in which explicit coordination cannot be
applied:
Speed: it takes too long to communicate with others – by
then the opportunities are missed
E.g. Football game – simple signals may work, but
lengthy explanations don't...
In general, very dynamic environments
Security: not wanting others to know
what your plans are
7. Motivation (II)
Complexity: some agents may be too simple to
deal with the complexity of generating and
understanding complex plans
Reactive rule-based robots
Complexity of Partial Global Planning or coalition
formation
Lack of a communication channel: there may
actually be no way to communicate
Physical robots with limited communication range
8. Options for implicit cooperation
Observe the behaviour of the other
agents, and react accordingly
Indirect cooperation through the effects on
the environment of the actions of each
agent
Imposing a structure on the MAS
9. Emergent Coordination [recall past lectures]
Coordination in cases where:
There is no communication between agents
There is no mechanism for enforcing a-priori
social rules / laws
Agents have their own agenda/goals
The resulting coordination is emergent
and cannot be said to be based on joint
plans or intentions
10. Basic difference
Emergent coordination: agents are self-
interested, they do not care about the other
agents in the system, there isn’t any high
level design of the emergent behaviour
Implicit coordination (also giving rise to
emergent coordinated global behaviour):
although agents do not communicate with
each other, the designer of the system
intends to provoke the emergence of the
socially intelligent problem solving activities
11. Implicit coordination example:
Network Routing
Network Routing problems
are challenging. Solutions
need to be:
Dynamic
Robust
Network of N nodes, L links.
Traffic flows as packets
traverse the network
There are protocols that
compute cumulative shortest
path measures
13. Network Ants
Ants randomly explore
the network until they
find a specific node
They mark the traversed
paths with “pheromone”
Ants seeking
destinations follow
pheromone trails
Pheromones degrade
over time
Robust
Stable
Gradual Change
14. Pheromone tables
Each node contains a table of probabilities
(pheromone table) for each possible
destination in the network
In a 30-nodes network, each node keeps 29
tables
The entries on the tables are the probabilities
which influence the ants’ selection of the next
node on the way to their destination node
Pheromone laying = updating probabilities
15. Pheromone tables example
A network with 6 nodes,
node 1 is connected with
nodes 2, 4 and 5. Next node
The pheromone tables in
2 4 5
node 1 would look like this:
For instance, if an ant 2 0.90 0.05 0.05
arrives at node 1 and wants
to go to node 3, the most 3 0.25 0.60 0.15
probable route is through Destination 4 0.10 0.85 0.05
node 4 (but it may also node 5 0.10 0.10 0.80
decide to go through nodes
2 or 5) 6 0.40 0.30 0.30
16. Simulation (I)
At each step, ants can be launched from any
node in the network, with a random
destination node
Ants move from node to node, selecting the
next node to move to according to the
probabilities in the pheromone tables for their
destination node
Pheromone tables are initialized with random
values
17. Simulation (II)
When ants arrive at a node, they update the
probabilities of that node’s pheromone table
entries corresponding to their source node
They alter the table to increase the probability
pointing to their previous node
Ants moving away from their source node
can only directly affect those ants for which it
is the destination node
18. Pheromone laying example
An ant has to go from node 3 to node 2; in
the way, it travels from node 4 to node 1
First, it modifies the table in node 1 corresponding
to node 3, increasing the probability of selecting
the link to node 4
After that, it selects the next node randomly
according to the probabilities of the table in node
1 corresponding to node 2
3 … 4 1 … 2
19. Increasing/decreasing pheromones
Pheromones are increased with the following
formula
p = (p_old + Δ(p)) / (1 + Δ(p))
As all the entries must add up to 1, the other
entries have to be decreased as follows
p = p_old / (1 + Δ(p))
Note that probabilities may never be 0
20. Basic ideas of implicit cooperation
Agents do not talk to each other directly
Agents can modify the environment, and
these modifications influence the behaviour
of the other agents in the system
All the agents contribute towards a useful
global behaviour of the community
21. Reasoning mechanisms for coordination
Thinking about individual agents
Methods that allow building a model of the
other agents of the system
Thinking about the whole agent’s society
Methods that try to impose some kind of
rules/laws/structure/organisation in the multi-
agent system
22. Agent Modelling (I)
Even if you cannot talk to the other
agents you may still want to reason about
them
Main methods:
Recursive Modelling Methods
Assume the others have a similar structure to you
– and may have a model of you...
Try to deduce their beliefs/desires/intentions from
their actions on the environment
23. Agent modelling (II)
Plan Recognition
Analyse the sequences of activities of other agents
and try to discover their plans (and, from them,
identify the potential end goals of their actual
actions)
Game Playing / Game Tree Search:
Modelling opponents
For example, using minimax search
[Recall Game Theory in Artificial Intelligence]
24. Thinking about Society
Common approaches include:
Social Laws: global rules which agents follow and
lead to “coherent behaviour”, either instilled in the
agent or communicated when entering the
environment (e.g. - “driving on the right hand side”)
Social Power Relations: a theory of dependence
relations, in particular to model goal adoption (e.g.
carrying out work on behalf of a superior)
Electronic Institutions
Organizational structures
25. Institutions as Social Structures
Social Structures define a social level to
enhance coordination by means of
interaction patterns
Institutions are a kind of social structure
where a corpora of constraints shape the
behaviour of the members of a group
26. Institution components
The definition of a (human) Institution
usually includes:
Norms about the interactions
Conventions: acceptable (and unacceptable)
actions within the institution
Procedures and protocols to be followed
27. e-Institutions
An e-Institution is the computational model
of an institution through
The specification of the institution’s norms in
some suitable formalism
The formal specification of the institution’s
admissible procedures and protocols, which
follow the established conventions
28. E-Institutions and MAS
In the context of MAS, e-institutions:
reduce uncertainty of other agents’ behaviour
reduce misunderstanding in interaction
allow agents to foresee the outcome of an
interaction
simplify the decision making process (by reducing
the possible actions)
Agent behaviour guided by Norms
29. Why a Language for Norms?
Laws,
Laws, [Natural Language]
regulations
regulations
too abstract and
vague
Language for norms
Language for norms [Formal Language]
more concrete (Formal & Computational)
(Formal & Computational)
Electronic Institutions
Normative Agents
Norms in Norm enforcement
deliberation
cycle mechanisms
30. Influence of norms in the BDI deliberation cycle
input
Agent sensors
perception E
state
How is the N
world now? V
I
What if I perform
R
O
KB action A?
N
M
Which action do E
I choose? N
T
goals
actuators
norms
(obligations,
permissions...) action
31. AMELI (I)
AMELI is an institution middleware that is based
in a formal electronic institution specification tool
(ISLANDER), developed at IIIA
The ISLANDER framework is composed of:
A Dialogical Framework
Linguistic and social structure (roles) to give meaning to
agent interactions, communication language
A Performative Structure
scenes and relationships between scenes (e.g.
precedence)
Rules
Conventions to be followed, social commitments
32. AMELI (II)
Two hypotheses:
All agent actions are messages, observable
by the e-institution
An agent should never break the norms
36. Objectives of the AMELI middleware
Mediate and facilitate agent communication within
conversations (scenes)
Coordinate and enforce:
To guarantee the correct evolution of each conversation
(preventing errors made by the participating agents by
filtering erroneous illocutions, thus protecting the
institution)
To guarantee that agents’ movements between scenes
comply with the specification
To control which obligations participating agents acquire
and fulfil
37. GOVERNORS
A1 ... Ai ... An Agents
Layer
Public
Institution
G1 ... Gi ... Gn
Specification AMELI
(XML Social
Layer
Private
format) ...
IM SM1 ... S Mm TM1 ... T Mk
-
-
Communication Layer
INSTITUTION SCENE TRANSITION
MANAGER MANAGERS MANAGERS
38. AMELI – Agents in Social Layer
An institution manager that starts the
institution, authorises agents to enter, and
controls the creation of scenes
Scene managers responsible for governing
scenes (one for scene)
Transition managers control agents’
movements between scenes (one for
transition)
Governors mediate the interaction of an agent
with the rest of the agents within the
institution and control the agents’ obligations
(one for participating agent)
39. Organizational Structures
A pattern of information and control
relationships between individuals
Responsible for shaping the types of
interactions among the agents
Aids coordination by specifying which
actions an agent will undertake
Social structure-based methods impose
restrictions or norms on the behaviour of
agents in a certain environment
40. Sociology and Societies
Sociology is a discipline that results from an
evolution of Philosophy in order to describe
the interactions that arise among the members
of a group, and the social structures that are
established
The aim of any society is to allow its members
to coexist in a shared environment and pursue
their respective goals in the presence and/or in
co-operation with others
This can also be applied to digital societies
composed by computational entities (agent
societies)
41. Organizational studies (I)
Organizational studies, organizational
behaviour, and organizational theory are
related terms for the academic study of
organizations
They have been examined using the
methods of economics, sociology, political
science, anthropology and psychology
42. Organizational studies (II)
Concepts, abstractions and techniques coming from
organizational theories and organizational design
have been used in MAS
Organization theory is a descriptive discipline, mainly
focusing on describing and understanding organizational
functioning
Organization design is a normative, design-oriented
discipline that aims to produce the frameworks and tools
required to create effective organizations
43. Organization design
Organization design involves the creation of
roles, processes and formal reporting
relationships in an organization
One can distinguish between two phases in an
organization design process:
Strategic grouping, which establishes the overall
structure of the organization (its main sub-units and
their relationships), and
Operational design, which defines the more detailed
roles and processes
44. Social Structures
In open systems, some kind of structure should
be defined in order to ease coordination in a
distributed control scenario
A good option taken from human and animal
interactions is the definition of social structures
Social structures define a social level where
the multi-agent system is seen as a society of
entities in order to enhance the coordination of
agent activities (such as message passing
management and the allocation of tasks and
resources) by defining structured patterns of
behaviour
45. Social Structures - Aim
Social structures reduce the danger of
combinatorial explosion in dealing with the
problems of agent cognition, cooperation and
control, as they impose restrictions to the agents’
actions
These restrictions have a positive effect, as they:
avoid many potential conflicts, or ease their resolution
make easier for a given agent to foresee and model
other agents’ behaviour in a closed environment and fit
its own behaviour accordingly
46. Social Strucs. - Organizational classification
Markets, where agents are self-interested, driven
completely by their own goals. Interaction in
markets occurs through communication and
negotiation
Networks, where coalitions of self-interested
agents agree to collaborate in order to achieve a
mutual goal. Coordination is achieved by mutual
interest, possibly using trusted third parties
Hierarchies, where agents are fully cooperative,
and coordination is achieved through command
and control lines
47. Social Structures
Organizational classification
This classification is useful at the design stage, as
it tries to motivate the choice of one structure
based on its appropriateness for a specific
environment
48. Market structures
They are well-suited for
environments where the
main purpose is the
exchange of some goods
There are agents that
provide services, agents
that require services (and
pay for them), and
intermediate agents
49. Network structures
They are well-suited for
environments where
(dynamic) collaboration
among parties is
needed
There are contracts
established between
the agents of the
system
50. Hierarchies
Hierarchical structures
are well-suited for
environments where the
society’s purpose is the
efficient production of
some kind of results or
goods. Agents are
specialised in concrete
tasks
51. Social abstractions (I) - Role
Roles identify activities and services
necessary to achieve social objectives and
enable to abstract from the specific individuals
that will eventually perform them
From the society design perspective, roles
provide the building blocks for the agent
systems that can perform the role
From the agent design perspective, roles
specify the expectations of the society with
respect to the agent’s activity in the society
52. Social abstractions (II) : Role Dependency
Role dependency between two roles means
that one role is dependent on another role for
the realization of its objectives.
Societies establish dependencies and power
relations between roles, indicating relationships
between roles
These relationships describe how actors can interact
and contribute to the realization of the objectives of
each other. That is, an objective of a role can be
delegated to, or requested from, other roles
53. Agent Societies – Characteristics (I)
Role models reflect social competence of agents
Modelled by rights and obligations
Influence agent behaviour
Role models allow to ensure some global system
characteristics while also preserving individual
flexibility
Explicit rights and obligations allow to commit to specific
roles
Roles guarantee global behaviour
Role descriptions are represented by formal models
54. Agent Societies – Characteristics (II)
Interaction models reflect workflows and
business processes
Explicit procedures and access requirements
Scenes descriptions are formally specified, which
allows verification
55. Example of organisation structure
Production of different types of cars
within a factory
It involves several kinds of actors:
engineers, designers, salesmen,
different types of managers
57. Product hierarchy
There is a dedicated team for each product (type
of car) to be produced
Easy coordination within each product team
There may be global inefficiencies
Repetition of design and engineering tasks in different
products
A salesman may be specialised in a single product,
without enough knowledge/abilities to talk to a costumer,
identify his requirements and suggest the best product for
him
There might be a “global manager” trying to provide some
global communication and coordination
It might be a good option if products are quite
different from each other
59. Functional hierarchy (I)
Actors with the same role work together
under the supervision of a manager
A general product manager coordinates all
the activities of all the departments
Firemen/policemen/ambulances in the
practical exercise
60. Functional hierarchy (II)
The specialised actors can work in tasks
reusable in different products (e.g. designing
and engineering the air-conditioning system)
The resources in each department can be
easily shared by its members
Much work concentrated in the global product
manager, who must supervise the work of the
whole system
It can be a good option if the different
products are very interrelated
62. Product and functional hierarchy (I)
There are specialised departments, with a
manager for each of them (department head,
or functional manager)
There is a product manager for each product,
who talks to the functional managers
Functional managers act like brokers
Brokers are in contact with possible ”workers”
and will choose the best for each task
63. Product and functional hierarchy (II)
Few connections and communication messages
are required
Quite similar to the functional model
A lot of work for functional managers
Receive requests from several product managers
Coordinate the work of a team of agents
Identify common subtasks, manage shared resources
The failure of one product manager does not affect
the others
65. Flat structure
There is a product manager for each product, who
talks directly to the low-level workers, without
intermediate steps
A product manager may have to communicate with
many different agents, and these agents have
different abilities/expertise/vocabulary
Furthermore, there may be inefficiencies in the
global behaviour
A designer could have work in 2 products, while another
designer does not have any work
Two engineers could be working in similar problems in two
different products
Difficult to solve even with a high-level global coordinator
66. Organizational Structures - Critique
Useful when there are master/slave
relationships in the MAS.
Control over the slaves actions – mitigates
against benefits of DAI such as reliability,
concurrency
In some cases it presumes that at least
one agent has global overview – an
unrealistic assumption in MAS
67. Summary of Organisations
Focus on a structure / context for
coordination
Consider different types of structures:
Peer systems, markets, hierarchies, etc.
Are concerned with streamlining or “hard-
wiring” certain patterns which help
coordination in distributed problem solving
69. Comments on the practical exercise
Implicit cooperation
The functional organisation of the system has
been chosen by each working group
This structure limits the coordination
possibilities, and determines the communication
flows between the different types of agents
For instance, an ambulance cannot talk directly
with a police car, or team coordinators cannot talk
between them (in principle)
70. Readings for this week
Sections 8.6.3/4 of the book An introduction
to MultiAgent Systems (M. Wooldridge, 2nd
edition)
Article: Ant-based load balancing in
telecommunications networks
Article: The organ allocation process: a
natural extension of the Carrel agent-
mediated electronic institution