Belief Ascription and Mental-Level Modelling

Ronen I. Brafman, Moshe Tennenholtz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Models of agents that employ formal notions of mental states are useful and often easier to construct than models at the symbol (e.g., programming language) or physical (e.g., mechanical) level. In order to enjoy these benets, we must supply a coherent picture of mental-level models, that is, a description of the various components of the mental level, their dynamics and their interrelations. However, these abstractions provide weak modelling tools unless (1) they are grounded in more concrete notions; and (2) we can show when it is appropriate to use
them. In this paper we propose a model that grounds the mental state of the agent in its actions. We then characterizing a class of
goal-seeking agents that can be modelled as having beliefs.
This paper emphasizes the task of belief ascription. On one level this is the task of deducing an agent`s beliefs, and we look at
assumptions that can help constrain the set of beliefs an agent can be ascribed, showing cases in which it is a singleton. We also
investigate the computational complexity of this task, characterizing a class of agents to whom belief ascription is tractable. But on a deeper level, our model of belief ascription
supplies concrete semantics to beliefs, one that is grounded in an observable notion {action.
Original languageEnglish GB
Title of host publicationProceedings of the 4th International Conference on Principles of Knowledge Representation and Reasoning (KR'94). Bonn, Germany, May 24-27, 1994
EditorsJon Doyle, Erik Sandewall, Pietro Torasso
PublisherMorgan Kaufmann Publishers, Inc.
Number of pages12
StatePublished - 1994


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