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253
Extending and Implementing the Stable Model Semantics
, 2002
"... A novel logic program like language, weight constraint rules, is developed for answer set programming purposes. It generalizes normal logic programs by allowing weight constraints in place of literals to represent, e.g., cardinality and resource constraints and by providing optimization capabilities ..."
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Cited by 310 (5 self)
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A novel logic program like language, weight constraint rules, is developed for answer set programming purposes. It generalizes normal logic programs by allowing weight constraints in place of literals to represent, e.g., cardinality and resource constraints and by providing optimization capabilities. A declarative semantics is developed which extends the stable model semantics of normal programs. The computational complexity of the language is shown to be similar to that of normal programs under the stable model semantics. A simple embedding of general weight constraint rules to a small subclass of the language called basic constraint rules is devised. An implementation of the language, the smodels system, is developed based on this embedding. It uses a two level architecture consisting of a frontend and a kernel language implementation. The frontend allows restricted use of variables and functions and compiles general weight constraint rules to basic constraint rules. A major part of the work is the development of an ecient search procedure for computing stable models for this kernel language. The procedure is compared with and empirically tested against satis ability checkers and an implementation of the stable model semantics. It offers a competitive implementation of the stable model semantics for normal programs and attractive performance for problems where the new types of rules provide a compact representation.
Design of a rolebased trust management framework
 In Proceedings of the 2002 IEEE Symposium on Security and Privacy
, 2002
"... We introduce the RT framework, a family of Rolebased Trustmanagement languages for representing policies and credentials in distributed authorization. RT combines the strengths of rolebased access control and trustmanagement systems and is especially suitable for attributebased access control. Usi ..."
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Cited by 282 (36 self)
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We introduce the RT framework, a family of Rolebased Trustmanagement languages for representing policies and credentials in distributed authorization. RT combines the strengths of rolebased access control and trustmanagement systems and is especially suitable for attributebased access control. Using a few simple credential forms, RT provides localized authority over roles, delegation in role definition, linked roles, and parameterized roles. RT also introduces manifold roles, which can be used to express threshold and separationofduty policies, and delegation of role activations. We formally define the semantics of credentials in the RT framework by presenting a translation from credentials to Datalog rules. This translation also shows that this semantics is algorithmically tractable. 1
Complexity and Expressive Power of Logic Programming
, 1997
"... This paper surveys various complexity results on different forms of logic programming. The main focus is on decidable forms of logic programming, in particular, propositional logic programming and datalog, but we also mention general logic programming with function symbols. Next to classical results ..."
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Cited by 278 (56 self)
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This paper surveys various complexity results on different forms of logic programming. The main focus is on decidable forms of logic programming, in particular, propositional logic programming and datalog, but we also mention general logic programming with function symbols. Next to classical results on plain logic programming (pure Horn clause programs), more recent results on various important extensions of logic programming are surveyed. These include logic programming with different forms of negation, disjunctive logic programming, logic programming with equality, and constraint logic programming. The complexity of the unification problem is also addressed.
Experimental Results on the Crossover Point in Satisfiability Problems
 In Proceedings of the Eleventh National Conference on Artificial Intelligence
, 1993
"... Determining whether a propositional theory is satisfiable is a prototypical example of an NPcomplete problem. Further, a large number of problems that occur in knowledge representation, learning, planning, and other areas of AI are essentially satisfiability problems. This paper reports on a series ..."
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Cited by 201 (3 self)
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Determining whether a propositional theory is satisfiable is a prototypical example of an NPcomplete problem. Further, a large number of problems that occur in knowledge representation, learning, planning, and other areas of AI are essentially satisfiability problems. This paper reports on a series of experiments to determine the location of the crossover point  the point at which half the randomly generated propositional theories with a given number of variables and given number of clauses are satisfiable  and to assess the relationship of the crossover point to the difficulty of determining satisfiability. We have found empirically that, for 3sat, the number of clauses at the crossover point is a linear function of the number of variables. This result is of theoretical interest since it is not clear why such a linear relationship should exist, but it is also of practical interest since recent experiments [ Mitchell et al. 92; Cheeseman et al. 91 ] indicate that the most comput...
The Fast Downward planning system
 Journal of Artifical Intelligence Research
, 2006
"... Fast Downward is a classical planning system based on heuristic search. It can deal with general deterministic planning problems encoded in the propositional fragment of PDDL2.2, including advanced features like ADL conditions and effects and derived predicates (axioms). Like other wellknown planne ..."
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Cited by 201 (26 self)
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Fast Downward is a classical planning system based on heuristic search. It can deal with general deterministic planning problems encoded in the propositional fragment of PDDL2.2, including advanced features like ADL conditions and effects and derived predicates (axioms). Like other wellknown planners such as HSP and FF, Fast Downward is a progression planner, searching the space of world states of a planning task in the forward direction. However, unlike other PDDL planning systems, Fast Downward does not use the propositional PDDL representation of a planning task directly. Instead, the input is first translated into an alternative representation called multivalued planning tasks, which makes many of the implicit constraints of a propositional planning task explicit. Exploiting this alternative representation, Fast Downward uses hierarchical decompositions of planning tasks for computing its heuristic function, called the causal graph heuristic, which is very different from traditional HSPlike heuristics based on ignoring negative interactions of operators. In this article, we give a full account of Fast Downward’s approach to solving multivalued planning tasks. We extend our earlier discussion of the causal graph heuristic to tasks involving
On the Complexity of Propositional Knowledge Base Revision, Updates, and Counterfactuals
 ARTIFICIAL INTELLIGENCE
, 1992
"... We study the complexity of several recently proposed methods for updating or revising propositional knowledge bases. In particular, we derive complexity results for the following problem: given a knowledge base T , an update p, and a formula q, decide whether q is derivable from T p, the updated (or ..."
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Cited by 186 (12 self)
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We study the complexity of several recently proposed methods for updating or revising propositional knowledge bases. In particular, we derive complexity results for the following problem: given a knowledge base T , an update p, and a formula q, decide whether q is derivable from T p, the updated (or revised) knowledge base. This problem amounts to evaluating the counterfactual p > q over T . Besides the general case, also subcases are considered, in particular where T is a conjunction of Horn clauses, or where the size of p is bounded by a constant.
The Complexity of LogicBased Abduction
, 1993
"... Abduction is an important form of nonmonotonic reasoning allowing one to find explanations for certain symptoms or manifestations. When the application domain is described by a logical theory, we speak about logicbased abduction. Candidates for abductive explanations are usually subjected to minima ..."
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Cited by 161 (26 self)
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Abduction is an important form of nonmonotonic reasoning allowing one to find explanations for certain symptoms or manifestations. When the application domain is described by a logical theory, we speak about logicbased abduction. Candidates for abductive explanations are usually subjected to minimality criteria such as subsetminimality, minimal cardinality, minimal weight, or minimality under prioritization of individual hypotheses. This paper presents a comprehensive complexity analysis of relevant decision and search problems related to abduction on propositional theories. Our results indicate that abduction is harder than deduction. In particular, we show that with the most basic forms of abduction the relevant decision problems are complete for complexity classes at the second level of the polynomial hierarchy, while the use of prioritization raises the complexity to the third level in certain cases.
Improvements To Propositional Satisfiability Search Algorithms
, 1995
"... ... quickly across a wide range of hard SAT problems than any other SAT tester in the literature on comparable platforms. On a Sun SPARCStation 10 running SunOS 4.1.3 U1, POSIT can solve hard random 400variable 3SAT problems in about 2 hours on the average. In general, it can solve hard nvariable ..."
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Cited by 160 (0 self)
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... quickly across a wide range of hard SAT problems than any other SAT tester in the literature on comparable platforms. On a Sun SPARCStation 10 running SunOS 4.1.3 U1, POSIT can solve hard random 400variable 3SAT problems in about 2 hours on the average. In general, it can solve hard nvariable random 3SAT problems with search trees of size O(2 n=18:7 ). In addition to justifying these claims, this dissertation describes the most significant achievements of other researchers in this area, and discusses all of the widely known general techniques for speeding up SAT search algorithms. It should be useful to anyone interested in NPcomplete problems or combinatorial optimization in general, and it should be particularly useful to researchers in either Artificial Intelligence or Operations Research.
Efficient Implementation of the Wellfounded and Stable Model Semantics
 Proceedings of the Joint International Conference and Symposium on Logic Programming
, 1996
"... An implementation of the wellfounded and stable model semantics for rangerestricted functionfree normal programs is presented. It includes two modules: an algorithm for implementing the two semantics for ground programs and an algorithm for computing a grounded version of a rangerestricted funct ..."
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Cited by 137 (16 self)
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An implementation of the wellfounded and stable model semantics for rangerestricted functionfree normal programs is presented. It includes two modules: an algorithm for implementing the two semantics for ground programs and an algorithm for computing a grounded version of a rangerestricted functionfree normal program. The latter algorithm does not produce the whole set of ground instances of the program but a subset which is sufficient in the sense that no stable models are lost. The implementation of the stable model semantics for ground programs is based on bottomup backtracking search. It works in linear space and employs a powerful pruning method based on an approximation technique for stable models which is closely related to the wellfounded semantics. The implementation includes an efficient algorithm for computing the wellfounded model of a ground program. The implementation has been tested extensively and compared with a state of the art implementation of the stable mode...
Experimental Results on the Crossover Point in Random 3sat
 Artificial Intelligence
, 1996
"... Determining whether a propositional theory is satisfiable is a prototypical example of an NPcomplete problem. Further, a large number of problems that occur in knowledgerepresentation, learning, planning, and other ares of AI are essentially satisfiability problems. This paper reports on the most ..."
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Cited by 136 (5 self)
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Determining whether a propositional theory is satisfiable is a prototypical example of an NPcomplete problem. Further, a large number of problems that occur in knowledgerepresentation, learning, planning, and other ares of AI are essentially satisfiability problems. This paper reports on the most extensive set of experiments to date on the location and nature of the crossover point in satisfiability problems. These experiments generally confirm previous results with two notable exceptions. First, we have found that neither of the functions previously proposed accurately models the location of the crossover point. Second, we have found no evidence of any hard problems in the underconstrained region. In fact the hardest problems found in the underconstrained region were many times easier than the easiest unsatisfiable problems found in the neighborhood of the crossover point. We offer explanations for these apparent contradictions of previous results. This work has been supported ...