Fuzzy constraint logic programming with answer set semantics

Authors:
Jie Wang;Chunnian Liu
Affiliations:
Key Laboratory of Multimedia and Intelligent Software, Beijing University of Technology, Beijing, P.R. China;Key Laboratory of Multimedia and Intelligent Software, Beijing University of Technology, Beijing, P.R. China
Venue:
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
Year:
2007

Citing 7
Cited 0

Foundations of logic programming

Foundations of logic programming
Logic programming

Handbook of theoretical computer science (vol. B)
Fuzzy sets and fuzzy logic: theory and applications

Fuzzy sets and fuzzy logic: theory and applications
A hybrid model for sharing information between fuzzy, uncertain and default reasoning models in multi-agent systems

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Prioritised fuzzy constraint satisfaction problems: axioms, instantiation and validation

Fuzzy Sets and Systems - Theme: Multicriteria decision
A fuzzy constraint based model for bilateral, multi-issue negotiations in semi-competitive environments

Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
Agent-Oriented probabilistic logic programming with fuzzy constraints

PRIMA'06 Proceedings of the 9th Pacific Rim international conference on Agent Computing and Multi-Agent Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present a new framework of fuzzy constraint logic programming language including negation as failure which is a combination of fuzzy logic and fuzzy relation. We give the answer set semantics which is based on the method of stable model. Although much work have been done for answer set programming, no work addressed the answer set semantics for fuzzy constraint logic programming with negation as failure. Also, we give an example to illustrate the idea. Compared to Zadeh's compositional rule of inference for approximate reasoning used in [9], we find an effective and efficient computational procedure for fuzzy constraint logic programming by using answer set semantics.