MSc by Research in Computer Science

Intelligent Agents and Multi-agent Systems Strand

Agent-based systems are one of the most vibrant and important areas of research and development of complex software systems operating in dynamic and open environments. Such systems can be composed of heterogeneous entitities that must interact with one another, span organisational boundaries, and operate effectively within rapidly changing circumstances. To design and implement such complex systems, there is therefore an increasing need for improvements on traditional computing models and paradigms.

Aims

This course provides a modern overview of intelligent agents and multi-agent systems, and an introduction to several active research areas that describe models of agency and their associated computational counterparts.

Objectives

At the end of this course the student should have a good theoretical understanding of multi-agent systems, and should be able to make practical use of several key multi-agent systems technologies. More specifically, students will be able to:

• understand the notion of an agent, how agents are distinct from other software paradigms (e.g., objects), and understand the characteristics of applications that lend themselves to an agent-oriented solution;
• design the key features associated with constructing agents capable of intelligent autonomous action, and evaluate the main approaches taken to developing such agents;
• understand the key issues in designing societies of agents that can effectively cooperate in order to solve problems, including an understanding of the key types of multi-agent interactions possible in such systems;
• provide agent-based solutions to main application areas and be able to demonstrate how to develop a meaningful agent-based system using a contemporary agent development platform.

Students will have an overview of the key current research areas.

Course structure

The course will consist of the following parts.

1. Introduction: what is an agent: agents and objects; agents and expert systems; agents and distributed systems; typical application areas for agent systems.
2. Intelligent Agents: study of abstract architectures for agents; tasks for agents; the design of reasoning agents; logic-based agent capabilities (reactivity, temporal reasoning, planning and learning).
3. Multi-Agent Systems: classifying multi-agent interactions -- cooperative versus noncooperative; zero-sum and other interactions; what is cooperation? how cooperation occurs -- the Prisoner's dilemma and Axelrod's experiments; interactions between self-interested agents: auctions systems; negotiation; argumentation.
4. Social Agency: interaction languages and protocols: speech acts, KQML/KIF, the FIPA framework; ontologies, coordination languages; interactions between benevolent agents: cooperative distributed problem solving (CDPS), partial global planning; coherence and coordination; norm-based multi-agent systems.
5. Platforms and applications of intelligent agents and multi-agent systems.

Supervisor

Kostas Stathis

References

Mostly students will be referring to the books:

• Readings in Agents, Micahel Huhns and Munindar Singh
• An Introduction to Multi-Agent Systems, Michael Wooldridge (2nd Edition)
• Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations, Yoav Shoham and Kevin Leyton-Brown
• Computational Logic and Human Thinking: How to be Artificially Intelligent, Bob Kowalski

Specialised Research papers will be covered as and when they are appropriate.