3,956 research outputs found
Logic-based Technologies for Intelligent Systems: State of the Art and Perspectives
Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future
The CIFF Proof Procedure for Abductive Logic Programming with Constraints: Theory, Implementation and Experiments
We present the CIFF proof procedure for abductive logic programming with
constraints, and we prove its correctness. CIFF is an extension of the IFF
proof procedure for abductive logic programming, relaxing the original
restrictions over variable quantification (allowedness conditions) and
incorporating a constraint solver to deal with numerical constraints as in
constraint logic programming. Finally, we describe the CIFF system, comparing
it with state of the art abductive systems and answer set solvers and showing
how to use it to program some applications. (To appear in Theory and Practice
of Logic Programming - TPLP)
Terminological reasoning with constraint handling rules
Constraint handling rules (CHRs) are a flexible means to implement \u27user-defined\u27 constraints on top of existing host languages (like Prolog and Lisp). Recently, M. Schmidt-Schauß and G. Smolka proposed a new methodology for constructing sound and complete inference algorithms for terminological knowledge representation formalisms in the tradition of KLONE. We propose CHRs as a flexible implementation language for the consistency test of assertions, which is the basis for all terminological reasoning services.
The implementation results in a natural combination of three layers: (i) a constraint layer that reasons in well- understood domains such as rationals or finite domains, (ii) a terminological layer providing a tailored, validated vocabulary on which (iii) the application layer can rely. The flexibility of the approach will be illustrated by extending the formalism, its implementation and an application example (solving configuration problems) with attributes, a new quantifier and concrete domains
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