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83
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 101 (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 68 (45 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 36 (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 26 (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
ModularE : An elaboration tolerant approach to the ramification and qualification problems
 In LPNMR
, 2005
"... Abstract. We describe ModularE (ME), a specialized, modeltheoretic logic for narrative reasoning about actions, able to represent nondeterministic domains involving concurrency, static laws (constraints) and indirect effects (ramifications). We give formal results which characterizeME ’s high deg ..."
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Cited by 15 (3 self)
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Abstract. We describe ModularE (ME), a specialized, modeltheoretic logic for narrative reasoning about actions, able to represent nondeterministic domains involving concurrency, static laws (constraints) and indirect effects (ramifications). We give formal results which characterizeME ’s high degree of modularity and elaboration tolerance, and show how these properties help to separate out, and provide a principled solutions to, the endogenous and exogenous qualification problems. We also show how a notion of (micro) processes can be used to facilitate reasoning at the dual levels of temporal granularity necessary for narrativebased domains involving “instantaneous” series of indirect and knockon effects. 1
Ludocore: A Logical Game Engine for Modeling Videogames
 In Proc. of IEEE Conf. on Computational Intelligence and Games (CIG
, 2010
"... Abstract — LUDOCORE is a logical “game engine”, linking game rules as reasoned about by game designers to the formal logic used by automated reasoning tools in AI. A key challenge in designing this bridge is engineering a concise, safe, and flexible representation that is compatible with the semanti ..."
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Cited by 14 (5 self)
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Abstract — LUDOCORE is a logical “game engine”, linking game rules as reasoned about by game designers to the formal logic used by automated reasoning tools in AI. A key challenge in designing this bridge is engineering a concise, safe, and flexible representation that is compatible with the semantics of the games that logical models created with our engine intend to represent. Building on the event calculus, a formalism for reasoning about state and events over time, and a set of common structures and idioms used in modeling games, we present a tool that is capable of generating gameplay traces that illustrate the game’s dynamic behavior. It supports incremental modeling of player and nonplayer entities in the game world, modification of game rules without extensive nonlocal changes, and exploratory temporal and structural queries. In addition, its logical models can support play as realtime, graphical games with minimal userinterface description. I.