Artificial Intelligence Master URV-UPC-UB
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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.
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@INPROCEEDINGS{9476446, author={Pontes Miranda, James William and Cristiane Gratão de Souza, Rogéria}, booktitle={2021 16th Iberian Conference on Information Systems and Technologies (CISTI)}, title={OntoDRE: An ontology for the requirements engineering decision process}, year={2021}, volume={}, number={}, pages={1-6}, doi={10.23919/CISTI52073.2021.9476446}}
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TO CITE:
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BiBTex:
@INPROCEEDINGS{9476446, author={Pontes Miranda, James William and Cristiane Gratão de Souza, Rogéria}, booktitle={2021 16th Iberian Conference on Information Systems and Technologies (CISTI)}, title={OntoDRE: An ontology for the requirements engineering decision process}, year={2021}, volume={}, number={}, pages={1-6}, doi={10.23919/CISTI52073.2021.9476446}}
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UbiPOL aims to develop an ubiquitous participation platform to increase citizen awareness and participation in the policy making process. The platform will provide citizens with relevant policy information and opinions during their daily lives to engage them more in the process. It will also provide policymakers tools to observe citizen reactions and feedback throughout the process. A consortium of universities and companies will work to develop the platform, services, and applications and validate them through field trials. The goal is to improve citizen empowerment, engagement, trust and provide more efficient feedback to policymakers.
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Team KameRider is an international collaborative robotics team focused on service robotics research and development (R&D) for home applications. The team includes researchers from Monash University, Nankai University, Universiti Tenaga Nasional, and other institutions. Their work involves developing robot platforms for tasks like navigation, vision, manipulation, and human-robot interaction. They participate in the RoboCup@Home competition to evaluate their systems in realistic home environments. The team has placed highly in past competitions and works to advance service robotics through education initiatives.Shaping our AI (Strategy)?
This document discusses shaping strategies for artificial intelligence (AI). It provides examples of AI research centers in Japan, Germany, and the US and their focus areas. It also summarizes sample projects from each center related to areas like computer vision, healthcare, manufacturing, and transportation. Finally, it outlines AIRC's approach in Japan of creating working groups to match AI technologies and data with needs and pilot real-world applications.
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Poster presented at the 2014 European Conference on Artificial Intelligence - Unsupervised semantic clustering of Twitter hashtags - automatic topic detection in Twitter
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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.
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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.
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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.
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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.
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MAS course Lect13 industrial applications
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.
MAS course - Lect12 - URV health care applications
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.
MAS Course - Lect10 - coordination
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
MAS course - Lect 9
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.
Lect 8-auctions
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.
Lect7MAS-Coordination
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.
Lect6-An introduction to ontologies and ontology development
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
Lecture 5 - Agent communication
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.
Lecture 4- Agent types
MAS course at URV. Lecture 4, agent types (specially interface agents, information agents, hybrid systems, agentification). Based on diverse resources.
Agent properties
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.
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Artificial Intelligence Master URV-UPC-UB
- 1. S1 OBL (30 ECTS) Intro. to Multi-Agent Systems (5 ECTS, URV) Computational Intelligence (5 ECTS, UPC) MultiAgent Systems (13) Multiagent Sys. Design (4, UPC) *Nor.&Dyn.Virt. Worlds (4.5, UB) Self-Org. Agent Sys. (4.5, UB) S2/*S3 OPT (39 ECTS) Planning & Approximate Reasoning (5 ECTS, URV) Intro. to Natural Language Processing (5 ECTS, UPC) Human-Comp. Interact. (14) Adv. Nat. Lang. Proc. (5, UPC) *Cog. Int.with Robots (4.5,UPC) Human-Comp. Inter. (4.5, UPC) Knowledge Eng. & Machine Learning (20) *Advanced Machine Learning Tech. (5, UPC) Intelligent Decision Support Systems (4.5, UPC) *Knowledge Represent. & Engineering (6, URV) Multi-Criteria Decision Support Sys. (4.5, URV) Vision, Perception & Robotics (13) *Advanced Artificial Vision (4.5,URV) Cooperative Robotics (4.5, URV) Object Recognition (4, UB) S3 OBL (21 ECTS) Intro. to Machine Learning (5 ECTS, UB) Computational Vision (5 ECTS, UB) Comput. Intelligence (13.5) Adv.Top. in Comp.Int. (4, UPC) Complex Networks (5, URV) *Int. D.An.&D.Min. (4.5, UPC) Modelling. Reasoning. & Problem Solving (19) *Constraint Processing & Programming (4.5, UPC) Logics for Artificial Intelligence (6, URV) Minds, Brains & Machines (4, UB) Probabilistic Graphical Models (4.5, UB) Professional Practice (9) *Professional Practice in AI (3, UPC) Artificial Intelligence Seminar (3, URV) *Intelligent System Project (3, UPC) Intelligent Data Analysis Applications in Business (3, UB) Master Thesis (18 ECTS)