Skip to main content

Multi-Agent Systems

Carles Sierra, Lluis Godo, Jun Hong, Weiru Liu

The deployment of open distributed systems is increasing rapidly. The advances of network technologies are spawning a surge of application domains: ambient intelligence, cloud computing, service oriented computing, sensor networks, or virtual organisations, just to mention a few. These applications are composed of a wealth of physical devices, software components and, frequently, humans. The overall co-ordination of these elements presents a tremendous challenge because of: the lack of a centralised control, the openness of the system, and the complexity and volatility of the environment. 

We investigate the use of multiagent technologies to model such systems. A multiagent system is a community of autonomous agents that is situated in an environment. The particular technologies we are mostly interested in are:

  1. Electronic Institutions. Communities typically choose to regulate the behaviour of their constituent human, or artificial, agents; for example, a regulation to drive vehicles on the left-side of the road. A multiagent system together with a regulatory mechanism for monitoring, and possibly restricting or policing, agent behaviour that breaches the adopted regulations is known as an electronic institution. In this context, we are interested in the theory of normative systems and in developing support tools for electronic institutions to explore their usage in different application domains.
  2. Agent architectures. Agents participating in electronic institutions are decision makers. The complex decisions they have to face include how to co-ordinate with other agents in order to reach agreements for their mutual benefit. In particular we investigate the use of graded BDI architectures to underpin negotiation and planning algorithms. 
  3. Modelling and reasoning about actions in noisy environments. In many situations, agents have to coordinate, plan and take decisions in noisy environments where only incomplete and uncertain information may be at hand. We aim at investigating (both qualitative and quantitative) models for decision making about actions for planning and negotiation in very poor information scenarios, but still able to produce meaningful outputs.  In particular, foundations for generalized models for uncertainty (e.g., probabilistic information, fuzzy information) are being developed, as well as corresponding decision theories will be investigated.