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224
The DLV System for Knowledge Representation and Reasoning
 ACM Transactions on Computational Logic
, 2002
"... Disjunctive Logic Programming (DLP) is an advanced formalism for knowledge representation and reasoning, which is very expressive in a precise mathematical sense: it allows to express every property of finite structures that is decidable in the complexity class ΣP 2 (NPNP). Thus, under widely believ ..."
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Cited by 456 (102 self)
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Disjunctive Logic Programming (DLP) is an advanced formalism for knowledge representation and reasoning, which is very expressive in a precise mathematical sense: it allows to express every property of finite structures that is decidable in the complexity class ΣP 2 (NPNP). Thus, under widely believed assumptions, DLP is strictly more expressive than normal (disjunctionfree) logic programming, whose expressiveness is limited to properties decidable in NP. Importantly, apart from enlarging the class of applications which can be encoded in the language, disjunction often allows for representing problems of lower complexity in a simpler and more natural fashion. This paper presents the DLV system, which is widely considered the stateoftheart implementation of disjunctive logic programming, and addresses several aspects. As for problem solving, we provide a formal definition of its kernel language, functionfree disjunctive logic programs (also known as disjunctive datalog), extended by weak constraints, which are a powerful tool to express optimization problems. We then illustrate the usage of DLV as a tool for knowledge representation and reasoning, describing a new declarative programming methodology which allows one to encode complex problems (up to ∆P 3complete problems) in a declarative fashion. On the foundational side, we provide a detailed analysis of the computational complexity of the language of
Answer Set Programming and Plan Generation
 ARTIFICIAL INTELLIGENCE
, 2002
"... The idea of answer set programming is to represent a given computational problem by a logic program whose answer sets correspond to solutions, and then use an answer set solver, such as smodels or dlv, to find an answer set for this program. Applications of this method to planning are related to the ..."
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Cited by 174 (6 self)
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The idea of answer set programming is to represent a given computational problem by a logic program whose answer sets correspond to solutions, and then use an answer set solver, such as smodels or dlv, to find an answer set for this program. Applications of this method to planning are related to the line of research on the frame problem that started with the invention of formal nonmonotonic reasoning in 1980.
Potassco: The Potsdam Answer Set Solving Collection
, 2011
"... This paper gives an overview of the open source project Potassco, the Potsdam Answer Set Solving Collection, bundling tools for Answer Set Programming developed at the University of Potsdam. ..."
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Cited by 98 (15 self)
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This paper gives an overview of the open source project Potassco, the Potsdam Answer Set Solving Collection, bundling tools for Answer Set Programming developed at the University of Potsdam.
An AProlog decision support system for the Space Shuttle
 In PADL 2001
, 2000
"... The goal of this paper is to test if a programming methodology based on the declarative language AProlog, algorithms for computing answer sets of programs of AProlog, and programming systems implementing these algorithms can be successfully applied to the development of medium size knowledge ..."
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Cited by 81 (17 self)
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The goal of this paper is to test if a programming methodology based on the declarative language AProlog, algorithms for computing answer sets of programs of AProlog, and programming systems implementing these algorithms can be successfully applied to the development of medium size knowledgeintensive applications. We report on a successful design and development of such a system controlling some of the functions of the Space Shuttle. Introduction The research presented in this paper is rooted in recent developments in several areas of AI. Advances in the work on semantics of negation in logic programming (Gelfond & Lifschitz 1988; 1991) and on formalization of commonsense reasoning (Reiter 1980; Moore 1985) led to the development of the declarative language, AProlog, used in this paper to encode the domain knowledge, and to an AProlog based methodology for representing defaults. Insights on the nature of causality and its relationship with answer sets of logic programs (...
Representing Knowledge in AProlog
"... In this paper, we review some recent work on declarative logic programming languages based on stable models/answer sets semantics of logic programs. These languages, gathered together under the name of AProlog, can be used to represent various types of knowledge about the world. By way of example ..."
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Cited by 63 (2 self)
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In this paper, we review some recent work on declarative logic programming languages based on stable models/answer sets semantics of logic programs. These languages, gathered together under the name of AProlog, can be used to represent various types of knowledge about the world. By way of example we demonstrate how the corresponding representations together with inference mechanisms associated with AProlog can be used to solve various programming tasks.
Answer Sets
, 2007
"... This chapter is an introduction to Answer Set Prolog a language for knowledge representation and reasoning based on the answer set/stable model semantics of logic programs [44, 45]. The language has roots in declarative programing [52, 65], the syntax and semantics of standard Prolog [24, 23], disj ..."
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Cited by 62 (5 self)
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This chapter is an introduction to Answer Set Prolog a language for knowledge representation and reasoning based on the answer set/stable model semantics of logic programs [44, 45]. The language has roots in declarative programing [52, 65], the syntax and semantics of standard Prolog [24, 23], disjunctive databases [66, 67] and nonmonotonic logic
A Logic of Universal Causation
 Artificial Intelligence
, 1999
"... For many commonsense reasoning tasks associated with action domains, only a relatively simple kind of causal knowledge is required  knowledge of the conditions under which facts are caused. This note introduces a modal nonmonotonic logic for representing causal knowledge of this kind, relates it to ..."
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Cited by 56 (6 self)
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For many commonsense reasoning tasks associated with action domains, only a relatively simple kind of causal knowledge is required  knowledge of the conditions under which facts are caused. This note introduces a modal nonmonotonic logic for representing causal knowledge of this kind, relates it to other nonmonotonic formalisms, and shows that a variety of causal theories of action can be expressed in it, including the recently proposed causal action theories of Lin. The new logic extends the causal theories formalism of McCain and Turner, and provides a more adequate semantic account of it. A useful subset of the logic has a concise translation into classical propositional logic, and so can be used for automated planning and reasoning about action. A larger subset is closely related to logic programming under the answer set semantics, yielding another approach to automated reasoning.
Learning partially observable deterministic action models
 In Proc. Nineteenth International Joint Conference on Artificial Intelligence (IJCAI ’05
, 2005
"... We present exact algorithms for identifying deterministicactions ’ effects and preconditions in dynamic partially observable domains. They apply when one does not know the action model (the way actions affect the world) of a domain and must learn it from partial observations over time. Such scenari ..."
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Cited by 55 (2 self)
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We present exact algorithms for identifying deterministicactions ’ effects and preconditions in dynamic partially observable domains. They apply when one does not know the action model (the way actions affect the world) of a domain and must learn it from partial observations over time. Such scenarios are common in real world applications. They are challenging for AI tasks because traditional domain structures that underly tractability (e.g., conditional independence) fail there (e.g., world features become correlated). Our work departs from traditional assumptions about partial observations and action models. In particular, it focuses on problems in which actions are deterministic of simple logical structure and observation models have all features observed with some frequency. We yield tractable algorithms for the modified problem for such domains. Our algorithms take sequences of partial observations over time as input, and output deterministic action models that could have lead to those observations. The algorithms output all or one of those models (depending on our choice), and are exact in that no model is misclassified given the observations. Our algorithms take polynomial time in the number of time steps and state features for some traditional action classes examined in the AIplanning literature, e.g., STRIPS actions. In contrast, traditional approaches for HMMs and Reinforcement Learning are inexact and exponentially intractable for such domains. Our experiments verify the theoretical tractability guarantees, and show that we identify action models exactly. Several applications in planning, autonomous exploration, and adventuregame playing already use these results. They are also promising for probabilistic settings, partially observable reinforcement learning, and diagnosis. 1.
Automatatheoretic approach to planning for temporally extended goals
 IN ECP
, 2000
"... We study an automatatheoretic approach to planning for temporally extended goals. Specifically, we devise techniques based on nonemptiness of Büchi automata on infinite words, to synthesize sequential and conditional plans in a generalized setting in which we have that: goals are general temporal ..."
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Cited by 53 (10 self)
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We study an automatatheoretic approach to planning for temporally extended goals. Specifically, we devise techniques based on nonemptiness of Büchi automata on infinite words, to synthesize sequential and conditional plans in a generalized setting in which we have that: goals are general temporal properties of desired execution; dynamic systems are represented by finite transition systems; incomplete information on the initial situation is allowed; and states are only partially observable. We prove that the techniques proposed are optimal wrt the worst case complexity of the problem. Thanks to the scalability of the nonemptiness algorithms, the techniques presented here promise to be applicable to fairly large systems, notwithstanding the intrinsic complexity of the problem.
Representing Transition Systems by Logic Programs
, 1999
"... This paper continues the line of research on representing actions, on the automation of commonsense reasoning and on planning that deals with causal theories and with action language C. We show here that many of the ideas developed in that work can be formulated in terms of logic programs under th ..."
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Cited by 52 (9 self)
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This paper continues the line of research on representing actions, on the automation of commonsense reasoning and on planning that deals with causal theories and with action language C. We show here that many of the ideas developed in that work can be formulated in terms of logic programs under the answer set semantics, without mentioning causal theories. The translations from C into logic programming that we investigate serve as a basis for the use of systems for computing answer sets to reason about action domains described in C and to generate plans in such domains.