Goal
Upon successful completion of the course, the student will be able to:
- Recall, understand and explain fundamental concepts regarding intelligent agents, multi-agent systems, to recognize the particular characteristics of their environment and agents
- Decide how the characteristics and requirements of the problem determine the design, required functionality, and the selection of appropriate techniques for implementing agents and interactions between multiple agents
- Know, understand, design, analyze, evaluate algorithms related to distributed problem solving, distributed optimization, achieving goals through competitiveness or agent coordination and cooperation
- Know, understand, design, analyze, evaluate algorithms related to multi-agent distributed learning.
- Select, develop, adapt / evolve, creatively evaluate algorithms related to distributed problem solving, distributed optimization, negotiation, learning in multi-agent environments, possessing fundamental knowledge about their design
- Communicate ideas related to agents and multi-agent systems in a clear, concise and formal manner, in writing and orally
- The aim is students to be able to build and evaluate agent and multi-agent systems in predefined environments, and to explain-justify their operation.
Also, the course targets to the following general competencies:
- Ability to organize and plan work and manage time
- Ability to communicate orally and in writing
- Ability to solve problems
- Ability to develop critical thinking and capacity for creative approaches
- Ability to work in a team
- Ability of interdisciplinary approaches
- Ability to apply theoretical knowledge in practice
- Ability to evaluate algorithms, analyze and explain results, and further research
- Ability to adapt methods and techniques to new situations and conditions
- Ability to generate new ideas – Creativity
Contents
- Introductory aspects for intelligent agents and main architecture paradigms, and multiagent systems
- Multiagent interactions: Preferences and utilities
- Non-cooperative game theory, normal form games, examples of games, solution concepts and equilibria
- Distributed problem solving: Constraint satisfaction
- Distributed problem solving: optimization
- Markovian Decision Processes
- Auctions
- Negotiation/Argumentation
- Agents’ Communication
- Learning and Teaching in multiagent settingsFictitious play
- Rational Learning
- Reinforcement Learning
- No-regret learning, Targeted learning, evolutionary learning
Bibliography
- Michael Wooldridge, Introduction to MultiAgent Systems (Ελληνική έκδοση «Εισαγωγή στα Πολυπρακτορικά συστήματα», εκδόσεις Κλειδάριθμος), 2008.
- Yoav Shoham, Kevin Leyton-Brown Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations, Cambridge University Press, 2009.
- Gerhard Weiss, Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, MIT Press, 2000.
- John Miller Scott Page, Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Princeton Studies in Complexity) , Princeton University Press, 2007.
- David Easley, Jon Kleinberg, Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Cambridge University Press, 2010.
- Sutton, R.S. and Barto, A.G., Introduction to reinforcement learning (Vol. 135). Cambridge: MIT press, 1998.
Relevant Research Journals:
- Autonomous Agents and Multi-Agent Systems , Springer, ISSN: 1387-2532
- IEEE Distributed Systems, ISSN: 1541-4922
