Reasoning about sensing actions and its application to diagnostic problem solving

Son Cao Tran, University of Texas at El Paso

Abstract

This thesis presents a new approach to reasoning about sensing actions and its application to diagnostic problem solving. ^ We begin with the definition of an action description language AK that allows reasoning about actions and their effects in the presence of incomplete information and sensing actions. To define the semantics of AK , we introduce a notion of a combined state, which plays the same role of state in reasoning about actions when complete information about the environment is available. The semantics of AK is then defined by transition functions, which map pairs of actions and combined states into combined states. We prove the equivalence between AK and other approaches to reasoning about sensing actions such as the situation calculus approach of Scherl and Levesque and the high level action description language approach of Lobo et al. We compute the entailment relation defined by domain descriptions in AK , denoted by ⊨AK , by translating domain descriptions into semantics equivalently extended logic programs. Since the search space associated to ⊨AK is very large, several sound approximations of ⊨AK are proposed. These approximations differ from each other by the number of levels in which reasoning by cases is done. ^ We argue that actions and narratives play an important role in diagnostic problem solving. In formalizing diagnostic problem solving, we extend L , a high-level action description language for specifying and reasoning about narratives, with static causal laws, sensing actions, and observable fluents. We also define a notion of diagnostic plans, whose goal is to single out a diagnosis among multiple alternatives. ^

Subject Area

Computer Science

Recommended Citation

Tran, Son Cao, "Reasoning about sensing actions and its application to diagnostic problem solving" (2000). ETD Collection for University of Texas, El Paso. AAI9971353.
http://digitalcommons.utep.edu/dissertations/AAI9971353

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