T. Eiter, W. Faber, N. Leone, and G. Pfeifer. The diagnosis frontend of the DLV system. AI Communications, 12(1-2):99--111, 1999.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....by: goal : explains, not inconsistent, not nonsingle. not goal. This clearly resembles the formulation of an abduction problem: our goal is that all observed output values are explained, there are no inconsistencies and no An alternative is to use the DLV system with a diagnosis front end [5]. diagnoses with more than one abnormal gate. And we are only interested in stable models in which our goal is satis ed (i.e. it is not false) Our XSB Prolog program starts by running the query goal in a program with the representation of the problem having the above clause for goal 0, without ....

T. Eiter, W. Faber, N. Leone, and G. Pfeifer. The diagnosis frontend of the dlv system. AI Communications, 12(1-2):99-111, 1999.

....as abduction. For explaining this point, rst note that the representation is equivalent to another one, where the 3 clauses above with no head are replaced by: goal : explains, not inconsistent, not nonsingle. not goal. 2 An alternative is to use the DLV system with a diagnosis front end [4]. This clearly resembles the formulation of an abduction problem: our goal is that all observed output values are explained, there are no inconsistent and no diagnoses with more than one abnormal gate. And we are only interested in stable models in which our goal is satis ed (i.e. it is not ....

T. Eiter, W. Faber, N. Leone, and G. Pfeifer. The diagnosis frontend of the dlv system. AI Communications, 12(1-2):99-111, 1999.

....several researchers in the context of extended logic programs [34,43,28] These approaches use program transformations to resolve contradiction in a program. By contrast, PLP expresses priorities over defaults outside a program, which enables us to specify priorities independent of a program. 13 [16] presents an algorithm of computing minimal explanations in (function free) definite logic programs via answer sets of disjunctive logic programs. 40 5.2.3 (Prioritized) Circumscription Several researchers propose methods for compiling (prioritized) circumscription into logic programs. Gelfond ....

T. Eiter, W. Faber, N. Leone, and G. Pfeifer, The diagnosis front-end of the dlv system, AI Communications 12(1999) 99--111, IOS Press.

....work, which may also be exploited for obtaining rapid prototype implementations. e.g. by mapping binary causal explanations to abductive explanations, extended) variants of the Truth Maintenance System (cf. 43] could be utilized for this purpose, or the diagnostic frontend of the DLV system [10]. Another possibility would be an encoding of causal explanations in Answer Set Programming, and using the DLV engine to compute solutions. For the case of general causal explanations, reductions to QBF solvers such as [5, 41, 19] could be used. 7.2 Bayesian Networks After Cooper s well known ....

T. Eiter, W. Faber, N. Leone, and G. Pfeifer. The diagnosis frontend of the dlv system. The European Journal on Artificial Intelligence (AI Communications), 12(1--2):99--111, 1999.

....domains. The experiments con rm the usefulness of both techniques, sensibly improving the performance of DLV. 1 Introduction DLV is a knowledge representation system based on disjunctive logic programming (DLP) Min82,GL91] o ering front ends to several Knowledge Representation (KR) formalisms [ELM 98b,ELM 98a,EFLP99]. A strong point of DLV is its highly expressive language, which allows elegant and natural representations of very hard problems (up to 2 hard problems) DLV supports a declarative programming style which has recently been termed Answer Set Programming (ASP) hence it is referred to as an ....

T. Eiter, W. Faber, N. Leone, and G. Pfeifer. The Diagnosis Frontend of the dlv System. AI Communications, 12(1-2):99-111, 1999.

....ideas, we are currently developing a front end for deductive planning in dlv, which will be included in future releases of dlv. 1 Introduction Planning and diagnostic reasonings are very important AI tasks. Both elds have been studied quite extensively before, for an overview we refer to [4] (for diagnosis) and to [17, 22] for planning) In this paper, we compare the two domains and review the use of the dlv system for solving these problems. We show that both problems exhibit a goal oriented guess and check structure, which allows for a declarative representation in disjunctive ....

....In diagnosis, given a theory and some observations, the goal is to nd a set of hypotheses which can explain the observations by means of the theory. The notion of explanation is not a priori clear, therefore we de ne two notions of diagnosis, abductive and consistency based diagnosis, following [4]. In the abductive model of diagnosis [1, 5, 20] the theory is a function free disjunctive logic program, whose semantics is given by the set of its stable models [14] This form of diagnostic reasoning has been recently proved to be highly expressive, as it is able to represent even problems ....

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T. Eiter, W. Faber, N. Leone, and G. Pfeifer. The Diagnosis Frontend of the dlv System. AI Communications { The European Journal on Articial Intelligence, 12(1-2):99-111, 1999.

....show dramatic e ciency gains due to the new techniques. Keywords: Disjunctive Logic Programming, Algorithms, Heuristics. 1 Introduction dlv is a knowledge representation system, based on disjunctive logic programming (DLP) 13, 8] which o ers front ends to several advanced KR formalisms [5, 4, 2]. A strong point of dlv is its highly expressive language, which allows to represent very hard problems (even P 2 hard problems) in an elegant and natural fashion. An e cient support for such an expressive language requires the use of smart algorithms and optimization techniques, that are ....

....the DLP encoding of planning problems [6, 11] for instance, we usually look for a particular answer set where a query goal, representing the desired evolution of the system, is true. Similarly, in abductive diagnosis we look for answer sets where the observation (encoded as a query goal) is true [2]. Sometimes the query goals are implicitly expressed by integrity constraints [14, 12] Consider, for instance, the program P hp in Figure 1, that we will use as This work was supported by FWF (Austrian Science Funds) under the projects P11580 MAT and Z29 INF. i) reached(X) start(X) ....

T. Eiter, W. Faber, N. Leone, and G. Pfeifer. The Diagnosis Frontend of the dlv System. AI Communications { The European Journal on Articial Intelligence, 12(1-2):99-111, 1999.

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T. Eiter, W. Faber, N. Leone, and G. Pfeifer. The diagnosis frontend of the DLV system. AI Communications, 12(1-2):99--111, 1999.

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Thomas Eiter, Wolfgang Faber, Nicola Leone, and Gerald Pfeifer. The diagnosis frontend of the dlv system. AI Communications, 12(1-2):99--111, 1999.

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T. Eiter, W. Faber, N. Leone, and G. Pfeifer. The diagnosis frontend of the DLV system. AI Communications, 12(1-2):99--111, 1999.

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Thomas Eiter, Wolfgang Faber, Nicola Leone, and Gerald Pfeifer. The diagnosis frontend of the dlv system. AI Communications, 12(1-2):99--111, 1999.

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T. Eiter, W. Faber, N. Leone, and G. Pfeifer. The diagnosis frontend of the dlv system. AI Communications, 12(1-2):99--111, 1999.

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Thomas Eiter, Wolfgang Faber, Nicola Leone, and Gerald Pfeifer. The diagnosis frontend of the dlv system. AI Communications, 12(1-2):99--111, 1999.

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T. Eiter, W. Faber, N. Leone, G. Pfeifer, The Diagnosis Frontend of the dlv System, AI Communications 12(1-2): 99-111, 1999.

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