This paper proposes an extension of Answer Set programming (ASP) with quantifiers called Quantified ASP (QASP). This proposal is somehow inspired to the extension of SAT formulas in Quantified Boolean Formulas (QBF). ...
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Damages to cultural heritage due to human malicious actions or to natural disasters (e.g., earthquakes, tornadoes) are nowadays more and more frequent. Huge work is needed by professional restores to reproduce, as bes...
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We explore the range of propositional logics suitable for logic programs under the stable semantics, starting with the logic of here-and-there as a primary representative. It will be shown, however, that there are oth...
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In this paper we follow ideas from our Equational approach to argumentation, [3,4], and develop the Equational approach to logic programs. We regard a logic program P as a template for generating a system of equations...
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In this work, we present a causal extension of logic programming under the stable models semantics where, for a given stable model, we capture the alternative causes of each true atom. The syntax is extended by the si...
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This paper proposes a theoretical foundation of what could be an information flow in logic programming. Several information flow definitions (based on success/failure, substitution answers, bisimulation between goals)...
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A new genetic inductive logic programming (GILP for short) algorithm named PT-NFF-GILP (Phase Transition and New Fitness Function based Genetic Inductive logic programming) is proposed in this paper. Based on phase tr...
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ISBN:
(纸本)9781467315104
A new genetic inductive logic programming (GILP for short) algorithm named PT-NFF-GILP (Phase Transition and New Fitness Function based Genetic Inductive logic programming) is proposed in this paper. Based on phase transition of the covering test, PT-NFF-GILP randomly generates initial population in phase transition region instead of the whole space of candidate clauses. Moreover, a new fitness function, which not only considers the number of examples covered by rules, but also considers the ratio of the examples covered by rules to the training examples, is defined in PT-NFF-GILP. The new fitness function measures the quality of firstorder rules more precisely, and enhances the search performance of algorithm. Experiments on ten learning problems show that: 1) the new method of generating initial population can effectively reduce iteration number and enhance predictive accuracy of GILP algorithm; 2) the new fitness function measures the quality of first-order rules more precisely and avoids generating over-specific hypothesis; 3) The performance of PT-NFF-GILP is better than other algorithms compared with it, such as G-NET, KFOIL and NFOIL.
Information recommender system attempts to present information that is likely to be useful for the user. Some information recommender systems recommend persons which users are likely to follow in Twitter. Showing reco...
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Information recommender system attempts to present information that is likely to be useful for the user. Some information recommender systems recommend persons which users are likely to follow in Twitter. Showing recommendation reason is an important role of the systems. However, current recommender systems give only simple or quantitative reasons for the recommendation. In this paper, we aim at giving precise and non-quantitative reasons which are also easy to understand. We make use of formulas in first-order predicate logic for explaining the reason. In order to build such formulas, we use Inductive logic programming. We succeeded to extract several useful formulas from micro-blog.
Constraint Satisfaction Problems typically exhibit very strong combinatorial explosion of exponential nature. This is due to their intrinsic nature: a number of variables have to be assigned values from their domains....
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Constraint Satisfaction Problems typically exhibit very strong combinatorial explosion of exponential nature. This is due to their intrinsic nature: a number of variables have to be assigned values from their domains. This induces a very large number of potential solutions to be explored. Most typical approaches are oriented towards reduction of the inevitable search through advanced constraint propagation methods. In this paper we analyze a possibility of improving efficiency in Constraint logic programming. A hypergraph model of constraints is proposed as a base tool for planning approach. Building the partial solution plan in the form of definite sequence of variables is performed a priori. The plan is executed with a classical backtrack search. The whole process is focused on efficient use of variable values propagation rules. Two example cryptoarithmetic problems are explored in order to explain the proposed approach. The reported results are amazing in comparison to contemporary tools.
In fuzzy linguistic logic programming, there are two approaches to compute answers to queries w.r.t. a logic program: (i) by bottom-up iterating the immediate consequence operator T P ; or (ii) by using the procedural...
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In fuzzy linguistic logic programming, there are two approaches to compute answers to queries w.r.t. a logic program: (i) by bottom-up iterating the immediate consequence operator T P ; or (ii) by using the procedural semantics. The former is not goal-oriented and exhaustive. The latter is goal-oriented, but may lead to an infinite loop and recomputes subgoals in rule bodies. This paper presents an efficient tabulation proof procedure, which can overcome these problems, for fuzzy linguistic logic programming, shows its termination, and proves its soundness.
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