Evidential support logic programming
References (9)
Fuzzy sets as a basis for a theory of possibility
Fuzzy Sets and Systems
(1978)Support logic programming
- J.F. Baldwin, An evidential support theory for knowledge engineering, to...
Cited by (120)
Proximity-based unification theory
2015, Fuzzy Sets and SystemsCitation Excerpt :This last approach is usually named Similarity-based Logic Programming, because the unification algorithm is supported by similarity relations. Examples of hybrid approaches, where both the resolution principle and the classical unification algorithm are modified, are [12] and [13,14]. In the case of [12] the fuzzy unification algorithm has a semantic nature, while in Baldwin's approach probabilistic and fuzzy uncertainty are combined into a single framework.
The operations on intuitionistic fuzzy values in the framework of Dempster-Shafer theory
2012, Knowledge-Based SystemsCitation Excerpt :For example, in [29] the author deals with a label space using mass assigned to the power set on the frame of discernment. The semantic of DST can be used in the frameworks of different approaches to the modelling uncertainty [6,7,26]. The above information of DST is quite enough for the presentation of A-IFS in terms of DST, but it is worth noting that besides the Definition 1 there are other possible representations of A-IFS proposed in the literature.
Identifying and eliminating dominated alternatives in multi-attribute decision making with intuitionistic fuzzy information
2012, Applied Soft Computing JournalCitation Excerpt :Recently, more and more attention has been paid to multi-attribute decision making problems with intuitionistic fuzzy information. A lot of classical methods, such as the technique for order preference by similarity to ideal solution (TOPSIS) method [2], the maximizing deviation method [7], the elimination et choice translating reality (ELECTRE) method [8,9], the višekriterijumsko kompromisno rangiranje (VIKOR) method [10], and the Dempster–Shafer theory of evidence [11], have been extended to intuitionsitic fuzzy environments [12–20]. According to the information about the weight vector of attributes, the research on multi-attribute decision making can be mainly be summarized as: (1) intuitionistic fuzzy multi-attribute decision making with the completely known attribute weights [12,16,17,20–27]; (2) intuitionistic fuzzy multi-attribute decision making with the weight information on attributes completely unknown [13–15,28]; (3) intuitionistic fuzzy multi-attribute decision making with the partially known weight information on attributes [18,19,29–31]; and (4) dynamic intuitionistic fuzzy multi-attribute decision making [32].
Maximal confidence intervals of the interval-valued belief structure and applications
2011, Information SciencesCitation Excerpt :The theory of belief functions, also called the Dempster–Shafer (DS) theory of evidence, was first introduced by Dempster [22] and was further mathematically formalized by Shafer [57] in the 1970s. In the 1980s, parallel formalizations continued with the work of Higashi and Klir [34], Hohle [35], Yager [87] and Baldwin [3]. In the following decade, Klir [39] further developed the DS theory and called it the “General Theory of Information”.
An interpretation of intuitionistic fuzzy sets in terms of evidence theory: Decision making aspect
2010, Knowledge-Based SystemsReasoning with Uncertain and Conflicting Opinions in Open Reputation Systems
2009, Electronic Notes in Theoretical Computer Science