Reasoning about action I: a possible worlds approach
Artificial Intelligence
Frames in the space of situations (research note)
Artificial Intelligence
Nonmonotonic reasoning in the framework of situation calculus
Artificial Intelligence - Special issue on knowledge representation
Towards a Conditional Logic of Actions and Causation
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
Reasoning about action in polynomial time
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Reasoning about actions in narrative understanding
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Non-Markovian control in the Situation Calculus
Artificial Intelligence
Applications of action languages in cognitive robotics
Correct Reasoning
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We discuss the persistence of the indirect effects of an action--the question when such effects are subject to the commonsense law of inertia, and how to describe their evolution in the cases when inertia does not apply. Our model of nonpersistent effects involves the assumption that the value of the fluent in question is determined by the values of other fluents, although the dependency may be partially or completely unknown. This view leads us to a new highlevel action language ARD (for Actions, Ramifications and Dependencies) that is capable of describing both persistent and nonpersistent effects. Unlike the action languages introduced in the past, ARD is "non-Markovian," in the sense that the evolution of the fluents described in this language may depend on their history, and not only on their current values.