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19
Reasoning Agents In Dynamic Domains
 In Workshop on LogicBased Artificial Intelligence
, 2000
"... The paper discusses an architecture for intelligent agents based on the use of AProlog  a language of logic programs under the answer set semantics. AProlog is used to represent the agent's knowledge about the domain and to formulate the agent's reasoning tasks. We outline how these ta ..."
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Cited by 95 (30 self)
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The paper discusses an architecture for intelligent agents based on the use of AProlog  a language of logic programs under the answer set semantics. AProlog is used to represent the agent's knowledge about the domain and to formulate the agent's reasoning tasks. We outline how these tasks can be reduced to answering questions about properties of simple logic programs and demonstrate the methodology of constructing these programs. Keywords: Intelligent agents, logic programming and nonmonotonic reasoning. 1 INTRODUCTION This paper is a report on the attempt by the authors to better understand the design of software components of intelligent agents capable of reasoning, planning and acting in a changing environment. The class of such agents includes, but is not limited to, intelligent mobile robots, softbots, immobots, intelligent information systems, expert systems, and decisionmaking systems. The ability to design intelligent agents (IA) is crucial for such diverse tasks as ...
On Strongest Necessary and Weakest Sufficient
 Artificial Intelligence
, 2000
"... Given a propositional theory T and a proposition q, a sufficient condition of q is one that will make q true under T , and a necessary condition of q is one that has to be true for q to be true under T . In this paper, we propose a notion of strongest necessary and weakest sufficient conditions. ..."
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Cited by 38 (2 self)
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Given a propositional theory T and a proposition q, a sufficient condition of q is one that will make q true under T , and a necessary condition of q is one that has to be true for q to be true under T . In this paper, we propose a notion of strongest necessary and weakest sufficient conditions. Intuitively, the strongest necessary condition of a proposition is the most general consequence that we can deduce from the proposition under the given theory, and the weakest sufficient condition is the most general abduction that we can make from the proposition under the given theory. We show that these two conditions are dual ones, and can be naturally extended to arbitrary formulas. We investigate some computational properties of these two conditions and discuss some of their potential applications.
Course of action generation for cyber security using classical planning
 In Proc. of ICAPS’05
, 2005
"... We report on the results of applying classical planning techniques to the problem of analyzing computer network vulnerabilities. Specifically, we are concerned with the generation of Adversary Courses of Action, which are extended sequences of exploits leading from some initial state to an attacker’ ..."
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Cited by 28 (0 self)
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We report on the results of applying classical planning techniques to the problem of analyzing computer network vulnerabilities. Specifically, we are concerned with the generation of Adversary Courses of Action, which are extended sequences of exploits leading from some initial state to an attacker’s goal. In this application, we have demonstrated the generation of attack plans for a simple but realistic webbased document control system, with excellent performance compared to the prevailing state of the art in this area. In addition to the new capabilities gained in the area of vulnerability analysis, this implementation provided some insights into performance and modeling issues for classical planning systems, both specifically with regard to METRICFF and other forward heuristic planners, and more generally for classical planning. To facilitate additional work in this area, the domain model on which this work was done will be made freely available. See the paper’s Conclusion for details.
Loop formulas for circumscription
 Artificial Intelligence
, 2004
"... Clark’s completion is a simple nonmonotonic formalism and a special case of many nonmonotonic logics. Recently there has been work on extending completion with “loop formulas ” so that general cases of nonmonotonic logics such as logic programs (under the answer set semantics) and McCain–Turner caus ..."
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Cited by 26 (16 self)
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Clark’s completion is a simple nonmonotonic formalism and a special case of many nonmonotonic logics. Recently there has been work on extending completion with “loop formulas ” so that general cases of nonmonotonic logics such as logic programs (under the answer set semantics) and McCain–Turner causal logic can be characterized by propositional logic in the form of “completion + loop formulas”. In this paper, we show that the idea is applicable to McCarthy’s circumscription in the propositional case. We also show how to embed propositional circumscription in logic programs and in causal logic, inspired by the uniform characterization of “completion + loop formulas”.
Abduction in Logic Programming: A New Definition and an Abductive Procedure Based on Rewriting
 Artificial Intelligence
, 2002
"... We propose a new definition of abduction in logic programming, and contrast it with that of Kakas and Mancarella's. We then introduce a rewriting system for answering queries and generating explanations, and show that it is both sound and complete under the partial stable model semantics a ..."
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Cited by 23 (5 self)
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We propose a new definition of abduction in logic programming, and contrast it with that of Kakas and Mancarella's. We then introduce a rewriting system for answering queries and generating explanations, and show that it is both sound and complete under the partial stable model semantics and sound and complete under the answer set semantics when the underlying program is socalled oddloop free. We discuss an application of the work to a problem in reasoning about actions and provide some experimental results. 1 Abduction in logic programming In general, given a background theory T , and an observation q to explain, an abduction of q w.r.t. T is a theory \Pi such that \Pi [ T j= q. Normally, we want to put some additional conditions on \Pi, such as that it is consistent with T and contains only those propositions called abducibles. For instance, in propositional logic, given a background theory T , a set A of assumptions or abducibles, and a proposition q, an explanation S...
DomainSpecific Preferences for Causal Reasoning and Planning
 9TH INTERNATIONAL CONFERENCE ON KNOWLEDGE REPRESENTATION AND REASONING (KR2004), DELTA WHISTLER RESORT
, 2004
"... We address the issue of incorporating domainspecific preferences in planning systems, where a preference may be seen as a "soft" constraint that it is desirable, but not necessary, to satisfy. To this end ..."
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Cited by 13 (2 self)
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We address the issue of incorporating domainspecific preferences in planning systems, where a preference may be seen as a "soft" constraint that it is desirable, but not necessary, to satisfy. To this end
On control knowledge acquisition by exploiting humancomputer interaction," presented at
 Sixth International Conference on Artificial Intelligence Planning Systems
, 2002
"... In the last decade, there has been a strong and increasing interest on building fast planning systems. From it, we have seen an enormous improvement in relation to the solvability horizon of current planning techniques. Most of these techniques rely on the translation of the predicate logic descript ..."
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Cited by 12 (4 self)
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In the last decade, there has been a strong and increasing interest on building fast planning systems. From it, we have seen an enormous improvement in relation to the solvability horizon of current planning techniques. Most of these techniques rely on the translation of the predicate logic description of domains into propositional representations of them. Then, a fast search procedure is applied for obtaining solutions to planning problems. While this is an important step towards solving the planning problem, we believe older planners that are based on predicate logic computation can still be competitive when they are combined with learning capabilities. representations of control knowledge (CK) which are easier to describe and maintain by humans and/or automatic systems than the ones based on propositional logic. In this paper, we present the results we have obtained with a relatively “old ” planner, Prodigy4.0, powered with CK that has been acquired using a mixed humancomputer collaboration. We advocate for the initial generation of CK by a learning system, and its later refinement by a human. In fact, we can iterate this process until the desired result has been achieved. We show results in the logistics domain used at AIPS’00 that are at the same level when compared with other techniques in the Track on handtailored planning systems (Track2).
Situation Calculus
, 2008
"... The situation calculus is a logical language for representing changes. It was first introduced by McCarthy in 1963, 1 and described in further details by McCarthy and Hayes [29] in 1969. The basic concepts in the situation calculus are situations, actions and fluents. Briefly, actions are what make ..."
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Cited by 10 (2 self)
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The situation calculus is a logical language for representing changes. It was first introduced by McCarthy in 1963, 1 and described in further details by McCarthy and Hayes [29] in 1969. The basic concepts in the situation calculus are situations, actions and fluents. Briefly, actions are what make the dynamic world change from one situation to another when performed by agents. Fluents are situationdependent functions used to describe the effects of actions. There are two kinds of them, relational fluents and functional fluents. The former have only two values: true or false, while the latter can take a range of values. For instance, one may have a relational fluent called handempty which is true in a situation if the robot’s hand is not holding anything. We may need a relation like this in a robot domain. One may also have a functional fluent called batterylevel whose value in a situation is an integer between 0 and 100 denoting the total battery power remaining on one’s laptop computer. According to McCarthy and Hayes [29], a situation is “the complete state of the universe at an instance of time”. But for Reiter [34], a situation is the same as its
Reformulating the Situation Calculus and the Event Calculus in the General Theory of Stable Models and in Answer Set Programming
"... Circumscription and logic programs under the stable model semantics are two wellknown nonmonotonic formalisms. The former has served as a basis of classical logic based action formalisms, such as the situation calculus, the event calculus and temporal action logics; the latter has served as a basis ..."
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Cited by 9 (4 self)
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Circumscription and logic programs under the stable model semantics are two wellknown nonmonotonic formalisms. The former has served as a basis of classical logic based action formalisms, such as the situation calculus, the event calculus and temporal action logics; the latter has served as a basis of a family of action languages, such as language A and several of its descendants. Based on the discovery that circumscription and the stable model semantics coincide on a class of canonical formulas, we reformulate the situation calculus and the event calculus in the general theory of stable models. We also present a translation that turns the reformulations further into answer set programs, so that efficient answer set solvers can be applied to compute the situation calculus and the event calculus. 1.
Situation calculus as answer set programming
 In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI
, 2010
"... We show how the situation calculus can be reformulated in terms of the firstorder stable model semantics. A further transformation into answer set programs allows us to use an answer set solver to perform propositional reasoning about the situation calculus. We also provide an answer set programmin ..."
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Cited by 4 (3 self)
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We show how the situation calculus can be reformulated in terms of the firstorder stable model semantics. A further transformation into answer set programs allows us to use an answer set solver to perform propositional reasoning about the situation calculus. We also provide an answer set programming style encoding method for Reiter’s basic action theories, which tells us how the solution to the frame problem in answer set programming is related to the solution in the situation calculus.