We present new complexity results on answer set checking for nonground programs under a variety of syntactic restrictions. For several of these problems, the kind of representation of the answer set to be checked is i...
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We present new complexity results on answer set checking for nonground programs under a variety of syntactic restrictions. For several of these problems, the kind of representation of the answer set to be checked is important. In particular, we consider set-based and bitmap-based representations, which are popular in implementations of Answer Set programming systems. Furthermore, we present new complexity results for various reasoning tasks under the assumption that predicate arities are bounded by some constant. These results imply that in such a setting - which appears to be a reasonable assumption in practice - more efficient implementations than those currently available may be feasible.
It is well known by Inductive logic programming (ILP) practioners that ILP systems usually take a long time to find valuable models (theories). The problem is specially critical for large datasets, preventing ILP syst...
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Nonmonotonic description logic programs are a major formalism for a loose coupling of rules and ontologies, formalized in logic programming and description logics, respectively. While this approach is attractive for c...
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ILASP (Inductive Learning of Answer Set Programs) is a logic-based machine learning system. It makes use of existing knowledge base, containing anything known before the learning starts or even previously learned rule...
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From a software engineering point of view, logic programming lacks many properties allowing secure development of large programs by many programmers. In this paper we try to improve these properties by proposing a pol...
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Functional logic languages combine nondeterministic search facilities of logic languages with features of functional languages, e.g., monadic I/O to provide a declarative method to deal with I/O actions. Unfortunately...
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ISBN:
(纸本)1581132654
Functional logic languages combine nondeterministic search facilities of logic languages with features of functional languages, e.g., monadic I/O to provide a declarative method to deal with I/O actions. Unfortunately, monadic I/O cannot be used in programs which split the computation due to nondeterministic reductions. This problem can be avoided if nondeterministic computations are encapsulated by search operators which are available, for instance, in the multi-paradigm language Curry. To support the programmer in identifying nondeterministic parts of a program, we develop a method based on a type and effect system that will find every possible source of nondeterminism. Additionally, such information can be exploited in compilers to optimize deterministically reducible parts of a program.
The deployment of knowledge representation formalisms to the Web has created the need for hybrid formalisms that combine heterogeneous knowledge bases. The aim of this research is to improve the reasoning efficiency o...
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We discuss the design of an abstract machine for the Reduce-OR process model (ROPM) for the AND and OR parallel execution of Prolog programs. It differs from the WAM in many respects, and some of these differences are...
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Electronic Blocks is a new programming interface, designed for children aged between three and eight years. The Electronic Blocks programming environment includes sensor blocks, action blocks and logic blocks. By conn...
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Answer Set Planning was one of the first challenging applications of Answer Set programming (ASP). However, when putting plans into practice, their execution and monitoring lead to an increased complexity. Foremost, t...
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Answer Set Planning was one of the first challenging applications of Answer Set programming (ASP). However, when putting plans into practice, their execution and monitoring lead to an increased complexity. Foremost, this is due to the inherent incompleteness of information faced in real-world scenarios. This fundamental problem has already been addressed in various ways, as in conformant, assertional, and assumption-based planning, and often combined with additional sensing actions. Our objective is to conceive a framework for planning and execution under incomplete information by combining the key concepts of the aforementioned approaches in a uniform ASP-based framework. This paper reports on our first step in this direction. More precisely, we define a series of increasingly complex ASP problems reflecting the successive integration of the key concepts. Our prime concern lies in obtaining a uniform characterization that matches the expressive realm of ASP. This is reflected by developing appropriate approximations that are implemented by means of metaencoding techniques.
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