Approximate state estimation in multiagent settings with continuous or large discrete state spaces

ABSTRACT

We present a new method for carrying out state estimation in multi-agent settings that are characterized by continuous or large discrete state spaces. State estimation in multiagent settings involves updating an agent's belief over the physical states and the space of other agents' models. We factor out the models of the other agents and update the agent's belief over these models, as exactly as possible. Simultaneously, we sample particles from the distribution over the large physical state space and project the particles in time.

REFERENCES

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence

General Terms:
Algorithms, Performance

Keywords:
continuous state spaces, multiagent state estimation, particle filters