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Probabilistic modal logic
 Proceedings of the 22nd AAAI Conference on Artificial Intelligence. (2007) 489
, 2007
"... A modal logic is any logic for handling modalities: concepts like possibility, necessity, and knowledge. Artificial intelligence uses modal logics most heavily to represent and reason about knowledge of agents about others ’ knowledge. This type of reasoning occurs in dialog, collaboration, and comp ..."
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Cited by 13 (2 self)
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the truth value of queries that are encoded as probabilistic modal logic formulas. We provide an exact algorithm which takes a probabilistic Kripke structure and answers probabilistic modal queries in polynomialtime in the size of the model. Then, we introduce an approximate method for applications
Factored Models for Probabilistic Modal Logic
"... Modal logic represents knowledge that agents have about other agents ’ knowledge. Probabilistic modal logic further captures probabilistic beliefs about probabilistic beliefs. Models in those logics are useful for understanding and decision making in conversations, bargaining situations, and competi ..."
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Cited by 3 (1 self)
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Modal logic represents knowledge that agents have about other agents ’ knowledge. Probabilistic modal logic further captures probabilistic beliefs about probabilistic beliefs. Models in those logics are useful for understanding and decision making in conversations, bargaining situations
Solving Graded/Probabilistic Modal Logic via Linear Inequalities (System Description)
"... Abstract. We present the experience gained from implementing a new decision procedure for both graded and probabilistic modal logic. While our approach uses standard tableaux for propositional connectives, modal rules are given by linear constraints on the arguments of operators. The implementation ..."
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Abstract. We present the experience gained from implementing a new decision procedure for both graded and probabilistic modal logic. While our approach uses standard tableaux for propositional connectives, modal rules are given by linear constraints on the arguments of operators. The implementation
Towards a probabilistic modal logic for semanticbased information retrieval
 In Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval
, 1992
"... Abstract: Semanticbased approaches to Information Retrieval make a query evaluation similar to an inference process based on semantic relations. semanticbased approaches find out hidden semantic relationships between a document and a query, but quantitative estimation of the correspondence between ..."
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Cited by 31 (0 self)
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are strongly related to modal logic, that such an integration is feasible and natural. A new model is developed on the basis of an extended modal logic. It has the advantages of (1) augmenting a semanticbased approach with a probabilistic measurement, and (2) augmenting a probabilistic approach with finer
Proceedings of the TwentyThird AAAI Conference on Artificial Intelligence (2008) Factored Models for Probabilistic Modal Logic
"... Modal logic represents knowledge that agents have about other agents ’ knowledge. Probabilistic modal logic further captures probabilistic beliefs about probabilistic beliefs. Models in those logics are useful for understanding and decision making in conversations, bargaining situations, and competi ..."
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Modal logic represents knowledge that agents have about other agents ’ knowledge. Probabilistic modal logic further captures probabilistic beliefs about probabilistic beliefs. Models in those logics are useful for understanding and decision making in conversations, bargaining situations
Temporal and modal logic
 HANDBOOK OF THEORETICAL COMPUTER SCIENCE
, 1995
"... We give a comprehensive and unifying survey of the theoretical aspects of Temporal and modal logic. ..."
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Cited by 1300 (17 self)
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We give a comprehensive and unifying survey of the theoretical aspects of Temporal and modal logic.
Abduction in Logic Programming
"... Abduction in Logic Programming started in the late 80s, early 90s, in an attempt to extend logic programming into a framework suitable for a variety of problems in Artificial Intelligence and other areas of Computer Science. This paper aims to chart out the main developments of the field over th ..."
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Cited by 616 (76 self)
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Abduction in Logic Programming started in the late 80s, early 90s, in an attempt to extend logic programming into a framework suitable for a variety of problems in Artificial Intelligence and other areas of Computer Science. This paper aims to chart out the main developments of the field over
Dynamic Logic
 Handbook of Philosophical Logic
, 1984
"... ed to be true under the valuation u iff there exists an a 2 N such that the formula x = y is true under the valuation u[x=a], where u[x=a] agrees with u everywhere except x, on which it takes the value a. This definition involves a metalogical operation that produces u[x=a] from u for all possibl ..."
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Cited by 1008 (7 self)
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the effect of the execution of a program on the truth of a formula ', DL uses a modal construct <>', which
Learning probabilistic relational models
 In IJCAI
, 1999
"... A large portion of realworld data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat " data representations. Thus, to apply these methods, we are forced to convert our data into a flat form, thereby losing much ..."
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Cited by 619 (31 self)
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of the relational structure present in our database. This paper builds on the recent work on probabilistic relational models (PRMs), and describes how to learn them from databases. PRMs allow the properties of an object to depend probabilistically both on other properties of that object and on properties of related
The Perceptron: A Probabilistic Model for Information Storage and Organization in The Brain
 Psychological Review
, 1958
"... If we are eventually to understand the capability of higher organisms for perceptual recognition, generalization, recall, and thinking, we must first have answers to three fundamental questions: 1. How is information about the physical world sensed, or detected, by the biological system? 2. In what ..."
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Cited by 1143 (0 self)
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If we are eventually to understand the capability of higher organisms for perceptual recognition, generalization, recall, and thinking, we must first have answers to three fundamental questions: 1. How is information about the physical world sensed, or detected, by the biological system? 2. In what form is information stored, or remembered? 3. How does information contained in storage, or in memory, influence recognition and behavior? The first of these questions is in the
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