- Understand the general notions of agent and multiagent systems, naming and classifying different properties of agents and environments; - Build agents with a given architecture; - Design a society of agents and use it in the solution of a problem; - Develop solutions, in many applications areas, using an agent-based methodology for problem solving; - Build a society of agents that comunnicate using an agent-based language and using different platforms.
Introduction Intelligent Agents Multiagent Systems Coordination Argumentative Systems Applications of Intelligent Agents Security, Trust and Legal Relations
Individualized teaching: guided study; assignment teaching; teaching per module. Socialized teaching: small groups discussion; guided discussion; brainstorming; lecture. Social and individualized teaching: project; problems; search. Mandatory contact sessions. This curricular unit (CU) is built up from several assessment instruments: practical assignments, both individual and in group, and theoretical/written exams. The final CU grade will result from a weighted average of all assessment instruments used.
Wooldrige M., An Introduction to Multiagent Systems, John Wiley & Sons, ISBN 0 47149691X, 2002; d'Inverno M., Luck M., Understanding Agent Systems, Springer, ISBN: 978-3540407003, 2003; Bellifemine F., Caire G., Greenwood D., Developing Multi-Agent Systems with JADE, John Wiley & Sons, ISBN: 978-0470057476, 2007;
- Identify, describe and define the basic concepts regarding data systems, analytical processing, data mining, knowledge extraction and machine learning. - Use, classify and assess existing or to develop applications and frameworks. - Select the adequate methodologies and use software and frameworks to solve real world problems, to data analysis, data mining, knowledge extraction and decision making. - To know and to be able to implement the main algorithms related to data mining techniques, knowledge extraction and autonomous learning.
Machine Learning Systems - Artificial Neural Networks - Genetic Algorithms and Genetic Computation - Particle Swarm - Case Based Reasoning Knowledge Extraction - Basic Cooncepts - Objectives - Methodologies - Techniques - Tools Applications and Frameworks
Individualized teaching: guided study; assignment teaching; teaching per module. Socialized teaching: small groups discussion; guided discussion; brainstorming; lecture. Social and individualized teaching: project; problems; search. Mandatory contact sessions. This curricular unit (CU) is built up from several assessment instruments: practical assignments, both individual and in group, and theoretical/written exams. The final CU grade will result from a weighted average of all assessment instruments used.
Han J., Kamber M., "Data Mining: Concepts and Techniques", Morgan Kaufmann, 2001; Haykin S., "Neural Networks: A Comprehensive Foundation", 2nd Edition, Prentice-Hall, New Jersey, 1999; Goldberg D., "Genetic Algorithms in Search, Optimization, and Machine Learning", Addison Wesley, 1989.
- Identify, describe and define the basic concepts regarding Evolutionary Computation (e.g., Virtual Minds), Collective Intelligence (e.g., Particle Swarm) and Soft Computing (e.g., Artificial Neural Networks, Genetic and Evolutionary Programming). - Select the best methodologies to problem solving, as well as the best computational paradigms that fit best to problem solving.
Virtual Minds Artificial Neural Networks Genetic Algorithms Genetic Programming Evolutionary Strategies Evolutionary Programming Artificial Life Systems Particle Swarm
Individualized teaching: guided study; assignment teaching; teaching per module. Socialized teaching: small groups discussion; guided discussion; brainstorming; lecture. Social and individualized teaching: project; problems; search. Mandatory contact sessions. This curricular unit (CU) is built up from several assessment instruments: practical assignments, both individual and in group, and theoretical/written exams. The final CU grade will result from a weighted average of all assessment instruments used.
De Jong K., Evolutionary Computation: A Unified Approach, MIT Press, 2006; Eiben A.E. , Smith J.E., Introduction to Evolutionary Computing, Springer-Verlag, New York, 2003; Engelbrecht A., Computational Intelligence: An Introduction, Wiley & Sons. ISBN 0-470-84870-7, 2007.
- Identify, describe and define the basic concepts regarding Autonomous Systems and Robotic Agents. - Build Autonomous Systems making use of the notion of Intelligent Agents. - Know and be able to use knowledge gathered from sensors. - Know and be able to use the actuators enabling mobility and navigation. - Study and develop coordination methods and interaction with the environment. - Use simulation and complexity analysis to apply to problem solving.
Autonomous Robotic Systems Control Architectures for Autonomous Systems Sensors and Actuators Perception, Mobility and Navigation Multiagent Robotic Systems Coordination and Interaction Autonomous Systems Applications and Frameworks Simulation
Individualized teaching: guided study; assignment teaching; teaching per module. Socialized teaching: small groups discussion; guided discussion; brainstorming; lecture. Social and individualized teaching: project; problems; search. Mandatory contact sessions. This curricular unit (CU) is built up from several assessment instruments: practical assignments, both individual and in group, and theoretical/written exams. The final CU grade will result from a weighted average of all assessment instruments used.
Arkin R., "Behavior Based Robotics", The MIT Press, 1998; LaValle S., "Planning Algorithms", Cambridge University Press, 2006; Mitchell H.B., “Multi-Sensor Data Fusion: An Introduction”, Springer, 2007.