Intelligent Agents Systems

Learning Outcomes

Upon completion of the course, students:

• Know basic concepts of multi-agent systems and their relationship with Artificial Intelligence, Distributed Systems, Philosophical considerations, Social theories, Psychology, Linguistics and Economics.
• Can analyze the components of the basic architectures used to develop multi-agent systems (Logical Agents, Reasoning Agents).
• Can analyze agent interactions with elements of game theory.
• Can implement the basic mechanisms for negotiation between agents – auctions, negotiation for the division of tasks and resources, logical reasoning.
• Can analyze agent communication based on speech act theory.

Indicative Module Content

• Intelligent agents and multi-agent systems: Intelligent Software Agents, Agents and Objects, Agents and Expert Systems, Multi-Agent Environments.
• Agent Interactions: Types of Interactions, Communication Techniques, Strategies and Equilibria.
• Game Theory, Negotiation and Agreement Making, Cooperative Frameworks.
• Decision Making Theory: Knowledge Modelling, Logic, Learning Techniques (supervised / unsupervised). Distributed decision-making techniques.
• Optimization Techniques and Synchronization: Types and techniques of synchronization and coordination, Application of optimization methods, multi-criteria optimization techniques,
• Application of Computational Intelligence Techniques (Fuzzy Logic, Genetic Algorithms, Neural Networks, etc.),
• Applications of Swarm Theory (Particle Swarm Optimization, Ant Colony Optimization, Artificial Bee Colony, etc.)