A New Penta-valued Logic Based Knowledge Representation
This work addresses knowledge representation challenges in AI, but it appears incremental as it builds on existing fuzzy and multi-valued logic approaches.
The paper tackles the problem of representing imprecise knowledge by proposing FP5, a new knowledge representation model that combines fuzzy sets and penta-valued logic to handle undefined, contradictory, or indeterminate properties. It introduces basic operations and discusses relations to existing models like fuzzy sets and intuitionistic fuzzy sets.
In this paper a knowledge representation model are proposed, FP5, which combine the ideas from fuzzy sets and penta-valued logic. FP5 represents imprecise properties whose accomplished degree is undefined, contradictory or indeterminate for some objects. Basic operations of conjunction, disjunction and negation are introduced. Relations to other representation models like fuzzy sets, intuitionistic, paraconsistent and bipolar fuzzy sets are discussed.