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88
Nonmonotonic Causal Theories
 ARTIFICIAL INTELLIGENCE
, 2004
"... The nonmonotonic causal logic defined in this paper can be used to represent properties of actions, including actions with conditional and indirect effects, nondeterministic actions, and concurrently executed actions. It has been applied to several challenge problems in the theory of commonsense kno ..."
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Cited by 274 (31 self)
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The nonmonotonic causal logic defined in this paper can be used to represent properties of actions, including actions with conditional and indirect effects, nondeterministic actions, and concurrently executed actions. It has been applied to several challenge problems in the theory of commonsense knowledge. We study the relationship between this formalism and other work on nonmonotonic reasoning and knowledge representation, and discuss its implementation, called the Causal Calculator.
Commitment machines
 In Proceedings of the 8th International Workshop on Agent Theories, Architectures, and Languages (ATAL01
, 2002
"... Abstract. We develop an approach in which we model communication protocols via commitment machines. Commitment machines supply a content to protocol states and actions in terms of the social commitments of the participants. The content can be reasoned about by the agents thereby enabling flexible ex ..."
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Cited by 104 (23 self)
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Abstract. We develop an approach in which we model communication protocols via commitment machines. Commitment machines supply a content to protocol states and actions in terms of the social commitments of the participants. The content can be reasoned about by the agents thereby enabling flexible execution of the given protocol. We provide reasoning rules to capture the evolution of commitments through the agents ’ actions. Because of its representation of content and its operational rules, a commitment machine effectively encodes a systematically enhanced version of the original protocol, which allows the original sequences of actions as well as other legal moves to accommodate exceptions and opportunities. We show how a commitment machine can be compiled into a finite state machine for efficient execution, and prove soundness and completeness of our compilation procedure. 1
Extending Classical Logic with Inductive Definitions
, 2000
"... The goal of this paper is to extend classical logic with a generalized notion of inductive definition supporting positive and negative induction, to investigate the properties of this logic, its relationships to other logics in the area of nonmonotonic reasoning, logic programming and deductiv ..."
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Cited by 69 (46 self)
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The goal of this paper is to extend classical logic with a generalized notion of inductive definition supporting positive and negative induction, to investigate the properties of this logic, its relationships to other logics in the area of nonmonotonic reasoning, logic programming and deductive databases, and to show its application for knowledge representation by giving a typology of definitional knowledge.
Missionaries and Cannibals in the Causal Calculator
"... A knowledge representation formalism... ..."
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Inductive Situation Calculus
 Artificial Intelligence
, 2004
"... see [2]. Temporal reasoning has always been a major test case for knowledge representation formalisms. In this paper, we develop an inductive variant of the situation calculus using the Logic for NonMonotone Inductive Definitions (NMID). This is an extension of classical logic that allows for unifo ..."
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Cited by 35 (23 self)
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see [2]. Temporal reasoning has always been a major test case for knowledge representation formalisms. In this paper, we develop an inductive variant of the situation calculus using the Logic for NonMonotone Inductive Definitions (NMID). This is an extension of classical logic that allows for uniform representation of various forms of definitions, including monotone inductive definitions and nonmonotone forms of inductive definitions such as iterated induction and induction over wellfounded posets [1]. Here, we demonstrate an application of NMIDlogic. The aim is twofold. First, we illustrate the role of NMIDlogic and nonmonotone inductive definitions for knowledge representation by presenting a variant of the situation calculus which we call inductive situation calculus. We show that ramification rules can be naturally modeled through a nonmonotone iterated inductive definition. Second, we illustrate the use of our recently developed modularity techniques for NMIDlogic in order to translate a theory of the inductive situation calculus into a classical logic theory of Reiter’s situation calculus [3].
Describing additive fluents in action language C
 in: Proc. IJCAI03
, 2003
"... An additive fluent is a fluent with numerical values such that the effect of several concurrently executed actions on it can be computed by adding the effects of the individual actions. We propose a method for describing effects of actions on additive fluents in the declarative language +. An implem ..."
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Cited by 27 (7 self)
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An additive fluent is a fluent with numerical values such that the effect of several concurrently executed actions on it can be computed by adding the effects of the individual actions. We propose a method for describing effects of actions on additive fluents in the declarative language +. An implementation of this language, called the Causal Calculator, can be used for the automation of examples of commonsense reasoning involving additive fluents. 1
Reasoning About Actions in a Probabilistic Setting
 In Proceedings AAAI2002
"... In this paper we present a language to reason about actions in a probabilistic setting and compare our work with earlier work by Pearl and (also briefly with) representations used in probabilistic planning. The main feature of our language is its use of static and dynamic causal laws, and use o ..."
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Cited by 25 (3 self)
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In this paper we present a language to reason about actions in a probabilistic setting and compare our work with earlier work by Pearl and (also briefly with) representations used in probabilistic planning. The main feature of our language is its use of static and dynamic causal laws, and use of unknown (or background) variables  whose values are determined by factors beyond our model  in incorporating probabilities. We also incorporate probabilities into reasoning with narratives. 1
Updating action domain descriptions
 in Proc. IJCAI
, 2005
"... How can an intelligent agent update her knowledge base about an action domain, relative to some conditions (possibly obtained from earlier observations)? We study this question in a formal framework for reasoning about actions and change, in which the meaning of an action domain description can be r ..."
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Cited by 24 (5 self)
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How can an intelligent agent update her knowledge base about an action domain, relative to some conditions (possibly obtained from earlier observations)? We study this question in a formal framework for reasoning about actions and change, in which the meaning of an action domain description can be represented by a directed graph whose nodes correspond to states and whose edges correspond to action occurrences. We define the update of an action domain description in this framework, and show among other results that a solution to this problem can be obtained by a divideandconquer approach in some cases. We also introduce methods to compute a solution and an approximate solution to this problem, and analyze the computational complexity of these problems. Finally, we discuss techniques to improve the quality of solutions. 1
Expressive policy analysis with enhanced system dynamicity
 in ASIACCS, 2009
"... Despite several research studies, the effective analysis of policy based systems remains a significant challenge. Policy analysis should at least (i) be expressive (ii) take account of obligations and authorizations, (iii) include a dynamic system model, and (iv) give useful diagnostic information. ..."
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Cited by 18 (5 self)
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Despite several research studies, the effective analysis of policy based systems remains a significant challenge. Policy analysis should at least (i) be expressive (ii) take account of obligations and authorizations, (iii) include a dynamic system model, and (iv) give useful diagnostic information. We present a logicbased policy analysis framework which satisfies these requirements, showing how many significant policyrelated properties can be analysed, and we give details of a prototype implementation.