One of the major challenges in knowledge discovery is how to extract meaningful and useful knowledge from the complex structured data that one finds in scientific and technological applications. One approach is to exp...
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One of the major challenges in knowledge discovery is how to extract meaningful and useful knowledge from the complex structured data that one finds in scientific and technological applications. One approach is to explore the logic relations in the database and using, say, an inductive logicprogramming (ILP) algorithm find descriptive and expressive patterns. these patterns can then be used as features to characterize the target concept. the effectiveness of these algorithms depends both upon the algorithm we use to generate the patterns and upon the classifier. Rule mining provides an excellent framework for efficiently mining the interesting patterns that are relevant. We propose a novel method to select discriminative patterns and evaluate the effectiveness of this method on a complex discovery application of practical interest.
We elaborate upon the usage of action language C for representing and reasoning about biological models. First, we provide a simple extension of C allowing for variables and show its usefulness in modeling biochemical...
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We elaborate upon the usage of action language C for representing and reasoning about biological models. First, we provide a simple extension of C allowing for variables and show its usefulness in modeling biochemical reactions according to the well-known model of BIOCHAM. Second, we show how the biological action description language C TAID can be mapped onto C. Finally, we describe a toolbox for using action languages, including among them, a compiler mapping C and C TAID to logic programs under answer sets semantics along with a Web-service integrating different front- and back-ends for addressing dynamical systems by means of action description languages via answer set programming. this is accompanied by an empirical evaluation with existing systems for processing action description languages.
We formulate semantic parsing as a parsing problem on a synchronous context free grammar (SCFG) which is automatically built on the corpus of natural language sentences and the representation of semantic outputs. We t...
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We formulate semantic parsing as a parsing problem on a synchronous context free grammar (SCFG) which is automatically built on the corpus of natural language sentences and the representation of semantic outputs. We then present an online learning framework for estimating the synchronous SCFG grammar. In addition, our online learning methods for semantic parsing problems are also extended to deal withthe case, in which the semantic representation could be represented under lambda-calculus. Experimental results in the domain of semantic parsing show advantages in comparison with previous works.
Semantic Web Service (SWS) composition is a challenging AI problem. We describe a theoretical and experimental framework based upon finite model search for constrained object models to address this problem. In many AI...
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Semantic Web Service (SWS) composition is a challenging AI problem. We describe a theoretical and experimental framework based upon finite model search for constrained object models to address this problem. In many AI situations the input is rather simple, and the results complex to obtain. SWS composition requests themselves can turn very complex, and the problem of building these requests can be viewed as an AI problem of its own. this paper presents an operational end to end approach to composing/publishing Semantic Web Services involving two main reasoning stages. Composing is first performed at the abstract level of goals (each roughly representing a discovery request), which yields a composition request at the workflow level. the resulting workflow is finally processed to generate a valid publishable semantic web service. We present experimental results obtained on industrial use cases during the DIP project.
An inherent difficulty in enumerative search algorithms for optimisation is the combinatorial explosion that occurs when increasing the size of the input. Among incomplete algorithms that address this issue, ant colon...
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An inherent difficulty in enumerative search algorithms for optimisation is the combinatorial explosion that occurs when increasing the size of the input. Among incomplete algorithms that address this issue, ant colony optimization(ACO) uses a combination of random and heuristic methods plus reinforcement learning, which proved efficient on a wide range of CSPs problems. this paper presents results in applying an ACO-based algorithm to configuration, which to the best of our knowledge was never investigated before. We describe how the nature of unbounded configuration problems impacts the ACO approach due to the presence of set-variables with open domains. We propose an ACO framework able to deal withthose issues through an original pheromone model and algorithm. We also present the use of particle swarm optimization (PSO) to converge towards good parameter sets. Finally, we provide early experimental results, both for random problem instances andthe "racks" optimisation problem.
In this paper we extend the logicprogramming based conformant planner described in [Son et al., 2005a] to allow it to work on planning problems with more complex descriptions of the initial states. We also compare th...
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In this paper we extend the logicprogramming based conformant planner described in [Son et al., 2005a] to allow it to work on planning problems with more complex descriptions of the initial states. We also compare the extended planner with other concurrent conformant planners.
P-log is a probabilistic logicprogramming language, which combines bothlogicprogramming style knowledge representation and probabilistic reasoning. In earlier papers various advantages of P-log have been discussed....
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P-log is a probabilistic logicprogramming language, which combines bothlogicprogramming style knowledge representation and probabilistic reasoning. In earlier papers various advantages of P-log have been discussed. In this paper we further elaborate on the KR prowess of P-log by showing that: (i) it can be used for causal and counterfactual reasoning and (ii) it provides an elaboration tolerant way for non-naive conditioning.
Integrating description logics (DL) and logicprogramming (LP) would produce a very powerful and useful formalism. However, DLs and LP are based on quite different principles, so achieving a seamless integration is no...
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Integrating description logics (DL) and logicprogramming (LP) would produce a very powerful and useful formalism. However, DLs and LP are based on quite different principles, so achieving a seamless integration is not trivial. In this paper, we introduce hybrid MKNF knowledge bases that faithfully integrate DLs with LP using the logic of Minimal Knowledge and Negation as Failure (MKNF) [Lifschitz, 1991]. We also give reasoning algorithms and tight data complexity bounds for several interesting fragments of our logic.
Nonmonotonic causal logic, invented by McCain and Turner, is a formalism well suited for representing knowledge about actions, and the definite fragment of that formalism has been implemented in the reasoning and plan...
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Nonmonotonic causal logic, invented by McCain and Turner, is a formalism well suited for representing knowledge about actions, and the definite fragment of that formalism has been implemented in the reasoning and planning system called CCalc. A 1997 theorem due to McCain shows how to translate definite causal theories into logicprogramming under the answer set semantics, and thus opens the possibility of using answer set programming for the implementation of such theories. In this paper we propose a generalization of McCain's theorem that extends it in two directions. First, it is applicable to arbitrary causal theories, not only definite. Second, it covers causal theories of a more general kind, which can describe non-Boolean fluents.
In this paper we introduce a multi-context variant of Reiter's default logic. the logic provides a syntactical counterpart of Roelofsen and Serafini's information chain approach (IJCAI-05), yet has several adv...
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In this paper we introduce a multi-context variant of Reiter's default logic. the logic provides a syntactical counterpart of Roelofsen and Serafini's information chain approach (IJCAI-05), yet has several advantages: it is closer to standard ways of representing nonmonotonic inference and a number of results from that area come "for free";it is closer to implementation, in particular the restriction to logicprogramming gives us a computationally attractive framework;and it allows us to handle a problem withthe information chain approach related to skeptical reasoning.
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