An analysis of first-order logics of probability
Artificial Intelligence
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
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Reasoning about noisy sensors and effectors in the situation calculus
Artificial Intelligence
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Ambient intelligence
Reasoning about actions with sensing under qualitative and probabilistic uncertainty
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Game-theoretic agent programming in Golog under partial observability
KI'06 Proceedings of the 29th annual German conference on Artificial intelligence
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Artificial intelligence
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UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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We present a formal framework for treating both incomplete information in the initial database and possible failures during an agent's execution of a course of actions. These two aspects of uncertainty are formalized by two different notions of probability. We introduce also a concept of expected probability, which is obtained by combining the two previous notions. Expected probability accounts for the probability of a sentence on the hypothesis that the sequence of actions needed to make it true might have failed. Expected probability leads to the possibility of comparing courses of actions and verifying which is more safe.