A Framework for Sequential Planning in Multi-Agent Settings

A Framework for Sequential Planning in Multi-Agent Settings

This paper extends the framework of partially observable Markov decision processes (POMDPs) to multi-agent settings by incorporating the notion of agent models into the state space. Agents maintain beliefs over physical states of the environment and over models of other agents, and they use Bayesian update to maintain their beliefs over time. The solutions map belief states to actions. Models o...

A Framework for Optimal Sequential Planning in Multiagent Settings

Introduction Research in autonomous agent planning is gradually moving from single-agent environments to those populated by multiple agents. In single-agent sequential environments, partially observable Markov decision processes (POMDPs) provide a principled approach for planning under uncertainty. They improve on classical planning by not only modeling the inherent non-determinism of the probl...

A Framework for Multi-Agent Planning

We introduce a computational framework, consisting of resources, skills, goals and services to represent the plans of individual agents and to develop models and algorithms for cooperation processes between a collection of agents.

Multi-agent Cooperation in a Planning Framework

Mathijs de Weerdt1 Andr e Bos1 Hans Tonino Cees Witteveen Delft University of Te hnology email: fM.M.deWeerdt, A.Bos, J.F.M.Tonino, C.Witteveeng ITS.TUDelft.NL Abstra t The promise of multi-agent systems is that multiple agents an solve problems more eÆ iently than single agents an. In this paper we propose a method to implement ooperation between agents in the planning phase, in order to a hie...

A Framework for a Multi-Agent Planning Support System