Unification is a central concept in deductive systems based on the resolution principle. Recently, we introduced a new weak unification algorithm based on proximity relations (i.e., reflexive, symmetric, fuzzy binary ...
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Unification is a central concept in deductive systems based on the resolution principle. Recently, we introduced a new weak unification algorithm based on proximity relations (i.e., reflexive, symmetric, fuzzy binary relations). Proximity relations are able to manage vague or imprecise information and, in combination with the unification algorithm, allow certain forms of approximate reasoning in a logic programming framework. In this paper, we present a reformulation of the weak unification algorithm and an elaborated method to implement it efficiently.
Recent research in extensions of Answer Set programming has included a renewed interest in the language of Epistemic Specifications, which adds modal operators K ("known") and M ("may be true") to ...
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As we know, table data is a popular data form in industry and scientific research fields. However, sometimes the original table data could not meet updating requirements in real applications, so we need to convert the...
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As we know, table data is a popular data form in industry and scientific research fields. However, sometimes the original table data could not meet updating requirements in real applications, so we need to convert them into required form. In this paper we propose an approach to learn the transformation rules that convert original table data to target form. Based on Inductive logic programming(ILP), we design a learning system called Table Transformation Rule Learner (TTRL). It uses specific predicates and background knowledge for this task to generate table transformation rules. We implement a unique heuristic function (HF) in TTRL to accelerate searching process for rule generation, and we use semi-supervised learning (SSL) in order to obtain more information especially from small set of sample data. We also address the problem like over-generalization which may occur when having only positive training examples in ILP learning process. We test our program in several kinds of table data, and the result shows that the transformation rules can be learned correctly. Moreover, our designed searching strategy can greatly reduce the time cost of searching rules.
We present I-DLV+MS, a new Answer Set programming (ASP) system that integrates an efficient grounder, namely I-DLV, with an automatic selector that inductively chooses a solver: depending on some inherent features of ...
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The purpose of this paper is to present a fresh idea on how symbolic learning might be realized via analogical reasoning. For this, we introduce directed analogical proportions between logic programs of the form "...
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ASP programs are a convenient tool for problem solving, whereas with large problem instances the size of the state space can be prohibitive. We consider abstraction as a means of over-approximation and introduce a met...
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We define a novel, extensional, three-valued semantics for higher-order logic programs with negation. The new semantics is based on interpreting the types of the source language as three-valued Fitting-monotonic funct...
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Confluence of a nondeterministic program ensures a functional input-output relation, freeing the programmer from considering the actual scheduling strategy, and allowing optimized and perhaps parallel implementations....
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We propose an interpretation of the first-order answer set programming (FOASP) in terms of intuitionistic proof theory. It is obtained by two polynomial translations between FOASP and the bounded-arity fragment of the...
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Answer Set programming (ASP) is one of the major declarative programming paradigms in the area of logic programming and non-monotonic reasoning. Despite that ASP features a simple syntax and an intuitive semantics, er...
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