A Framework for Sequential Planning in Multi-Agent Settings

Understand

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.