Corpus ID: 51874032

Satisficing Models of Bayesian Theory of Mind for Explaining Behavior of Differently Uncertain Agents: Socially Interactive Agents Track

@inproceedings{Pppel2018SatisficingMO,
  title={Satisficing Models of Bayesian Theory of Mind for Explaining Behavior of Differently Uncertain Agents: Socially Interactive Agents Track},
  author={Jan P{\"o}ppel and Stefan Kopp},
  booktitle={AAMAS},
  year={2018}
}
  • Jan Pöppel, Stefan Kopp
  • Published in AAMAS 2018
  • Computer Science
  • The Bayesian Theory of Mind (ToM) framework has become a common approach to model reasoning about other agents’ desires and beliefs based on their actions. Such models can get very complex when being used to explain the behavior of agents with different uncertainties, giving rise to the question if simpler models can also be satisficing, i.e. sufficing and satisfying, in different uncertainty conditions. In this paper we present a method to simplify inference in complex ToM models by… CONTINUE READING
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