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2004
Springer

A Framework for Sequential Planning in Multi-Agent Settings

10 years 1 months ago
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 updates to maintain their beliefs over time. The solutions map belief states to actions. Models of other agents may include their belief states and are related to agent types considered in games of incomplete information. We express the agents’ autonomy by postulating that their models are not directly manipulable or observable by other agents. We show that important properties of POMDPs, such as convergence of value iteration, the rate of convergence, and piece-wise linearity and convexity of the value functions carry over to our framework. Our approach complements a more traditional approach to interactive settings which uses Nash equilibria as a solution paradigm. We seek to avoid som...
Piotr J. Gmytrasiewicz, Prashant Doshi
Added 30 Jun 2010
Updated 30 Jun 2010
Type Conference
Year 2004
Where AMAI
Authors Piotr J. Gmytrasiewicz, Prashant Doshi
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