Autonomous agents become ubiquitous to help people manage the complexities of the modern world. Our goal is to make such agents behave intelligently. We have a long-standing line of research in distributed constraint optimization to solve problems of coordination among distributed agents, and we have recently developed even more efficient techniques using sampling and for learning to coordinate the use of resources through a decentralized protocol.
Reinforcement learning is the problem of learning how to act within an unknown environment through interaction and limited reinforcement. It is one of the most general problems in AI, with applications such as game playing, resource management, optimisation and optimal control. We are interested in deriving computationally efficient algorithms for reinforcement learning, particularly for large environments or problems with multiple agents.
Using computational game theory, we analyze selfish behavior of intelligent agents in strategic environments, and design mechanisms that incentivize truthful behavior in various settings, including auctions and truthful information elicitation.
Service-oriented computing is a way to implement systems of multiple agents. In the SOSOA project, we are developing new techniques for automated service composition. See here for earlier work in the area of web services and agents.