A major goal of artificial intelligence is designing agents: systems that perceive their environment and execute actions. In particular, a fundamental question is how to build intelligent agents. When uncertainty and many agents are involved, this question is particularly challenging, and has not yet been answered in a satisfactory way. The need for scalable and flexible multi-agent planning is particularly pressing, given that intelligent distributed systems are becoming ubiquitous in society. For an agent in isolation, planning under uncertainty has been studied using decision-theoretic models such as partially observable Markov decision processes (POMDPs). Such single-agent, centralized methods clearly do not suffice for large-scale multi-agent systems. The project PURE-MAS (“Planning under uncertainty for real-world multi-agent systems on multi-agent techniques”) addressed the issue of the multi-agent decision process.
- PURE-MAS Project
- artificial intelligence