J. F. Baldwin. Evidential support of logic programming. Fuzzy Sets and Systems, 24(1):1-26, 1987.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....or a k greater value so we can conclude from the result that there are divergent opinions about the readability of the paper. figure 3. T(I,I) and finite subsets Another remark on T(I,I) It has been proposed to use a pair (X,Y) with X being the necessity and Y being the possibility of a fact [Baldwin, 87, Zhang, 92] This interpretation can be transformed to the above given if we take into consideration that A is possible as far as not A is necessary. 4 R R Resolution: A proof procedure for many valued Horn clauses R R resolution is essentially based on signed resolution. A signed clause has ....

Baldwin J.W.: "Evidential Support Logic Programming". Fuzzy Sets and Systems 24, 1-26. North Holland,1987.

....order to answer the query FRIL combines relations and the fuzzy degrees of membership of the members of the relations to compute which facts t the query and to what degree. Thus FRIL is inherently fuzzy, but can also deal with point, interval and fuzzy probabilities [9] A subsequent development [8], which has now been combined with the fuzzy relational inference mechanism described above, is support logic programming. In this system each clause is quanti ed with a support pair, that is a pair of numbers which represent the possible and necessary degree to which the statement is supported. ....

Baldwin, J. F. (1987) Evidential support logic programming, Fuzzy Sets and Systems, 24 1-26.

....equivalence, negative correlation, mutual exclusion, exhaustion, and antivalence are given as in Table 1. We remark that the static probabilistic conjunction and disjunction strategies for ignorance, independence, positive correlation, and mutual exclusion are already known from Baldwin [2]. Recently, the probabilistic conjunction and disjunction strategies for ignorance, independence, positive correlation, negative correlation, and mutual exclusion have especially been discussed in [21] and [20] To our knowledge, the strategies for the remaining dependence informations in Table 1 ....

J. F. Baldwin. Evidential support logic programming. Fuzzy Sets and Systems, 24:1--26, 1987.

....S i is the conorm de ned as: S i (a; b) 1 T i (1 a; 1 b) 7.12) The di erent norms range from conservative combination techniques to liberal combination techniques as i increases from 1 to 3. In addition to Bonissone s norms and conorms, Baldwin has identi ed a few other combination techniques [25]. The liberal T 3 norm is equivalent to Zadeh s intersection rule [111] and is also used by Baldwin for combining groups of evidence from the same source prior to reasoning. The basic concepts involved with probability bound inference are shown in Figure 7.2. 7.1.4 Dempster Shafer Inference ....

J. F. Baldwin. Evidential support logic programming. Fuzzy Sets and Systems, 24(1):1-26, 1987.

....a model theory and fixpoint theory for such programs. Last, but not least, we develop three alternative procedures to answer queries, each of which is guaranteed to be sound and complete. 1 Introduction Though there has now been considerable work in the area of quantitative logic programming [1, 12, 27, 31, 17], it is only recently that probabilistic logic programming [20, 19, 22, 23, 24] has been studied. The reason for this is that while connectives in multivalued logics can be interpreted in terms of the lattice s LUB (for disjunction) and GLB (for conjunction) operators, the same is not true in the ....

J.F. Baldwin. (1987) Evidential Support Logic Programming, J. of Fuzzy Sets and Systems, 24, pps 1-26.

....related work. 8. 1 Uncertainty in Logic Programming Logic knowledge bases have been extended to handle fuzzy modes of uncertainty since the early 70 s with the advent of the MYCIN and Prospector systems [18] Shapiro was one of the first to develop results in fuzzy logic programming [47] Baldwin [2] was one of the first to introduce evidential logic programming and a language called FRIL. Van Emden [54] was the first to provide formal semantical foundations for logic programs that was later extended by Subrahmanian [50] and then completely generalized in a succession of papers by Blair and ....

J.F. Baldwin. (1987) Evidential Support Logic Programming, J. of Fuzzy Sets and Systems, 24, pps 1-26.

....pertaining to any given belief about a domain, some of them possibly contradictory. Also, as pointed out in [14] evidence about some of the beliefs is likely to be imprecise or incomplete, requiring a framework with fuzzy logic [29] capabilities. A framework based on Baldwin s support logic [6] can be defined, which is specifically designed to handle reasoning about multiple sources of evidence with both boolean and fuzzy logic and includes an explicit accounting of ignorance regarding a belief. The framework is built around defining support pairs for every piece of evidence. 1 ....

....generated to effectively filter out uninteresting results from the Web Usage Mining process. The specific contributions of this paper are: Development of a general quantitative model of what determines the interestingness of discovered knowledge, based on Baldwin s support logic framework [6]. Development of an approach for the automatic creation of an initial set of evidence about a belief set. Development of specific algorithms for automated discovery of interesting rules in the Web Usage Mining domain. Presentation of results from a Web Usage Mining system called the ....

[Article contains additional citation context not shown here]

J. F. Baldwin. Evidential support logic programming. Fuzzy Sets and Systems, 24(1):1--26, 1987.

....equivalence, negative correlation, mutual exclusion, exhaustion, and antivalence are given as in Table 1. We remark that the static probabilistic conjunction and disjunction strategies for ignorance, independence, positive correlation, and mutual exclusion are already known from Baldwin [2]. Recently, the probabilistic conjunction and disjunction strategies for ignorance, independence, positive correlation, negative correlation, and mutual exclusion have especially been discussed in [18] and [17] To our knowledge, the strategies for the remaining dependence informations in Table 1 ....

J. F. Baldwin. Evidential support logic programming. Fuzzy Sets and Systems, 24:1--26, 1987.

....Many applications in practice make it indispensable for a knowledge representation and reasoning system to deal with uncertain knowledge. The need for representing uncertainty in the logic programming framework is already reported by a great number of publications on many valued logic programming [67, 5, 19, 7], logic programming with signed formulas [40, 27] generalized annotated logic programming [35, 38] and probabilistic logic programming (see references below) In this paper, we elaborate an approach to probabilistic logic programming with conditional constraints, which can be understood both as ....

J. F. Baldwin. Evidential support logic programming. Fuzzy Sets Syst., 24:1--26, 1987.

....7.1. This is because the notion of a class being more specific than another is a concept that is based on the classes themselves, but not on individual elements in the classes. 8 Related Work There have been many proposals on multivalued logic programming. These include the works by Baldwin [2], Blair and Subrahmanian [4] Dubois, Prade and Lang [5] Fitting [7, 8] Kifer 32 et al. [11, 12, 13] Shapiro [25] and van Emden [26] However, all of these proposals are nonprobabilistic, as they are based either on fuzzy set theory, possibilistic logic or DempsterShafer theory. As we believe ....

J.F. Baldwin. (1987) Evidential Support Logic Programming, Journal of Fuzzy Sets and Systems, 24, pps 1-26.

.... employ one of the following formalisms: 1) a form of fuzzy logic (programming) e.g. van Emden [20] Steger et al. 19] and Fitting [8] 2) annotated logic programming (e.g. see Kifer and Li [12] and Kifer and Subrahmanian [14] 3) evidence theoretic logic programming (e.g. see Baldwin [2]) and (4) probabilistic logic programming (see below) Ng and Subrahmanian [17, 18] have recently proposed an interesting scheme for logic programming with uncertainty modeled using probabilities. Syntactically, the framework shares the notation with annotated logic program This research was ....

.... fl) is not necessarily 1. This shows we cannot regard the expert s doubt as the complement (w.r.t. 1) of his belief. Thus, if we have to model what necessarily follows according to the expert s knowledge, then we must carry both the belief and the doubt explicitly. Kifer and Li [12] and Baldwin [2] have argued that incorporating both belief and doubt (called disbelief there) is useful in dealing with incomplete knowledge, where different evidences may contradict each other. However, in their frameworks, doubt need not be maintained explicitly. For suppose we have a belief b and a disbelief ....

J.F. Baldwin. Evidential support logic programming. Journal of Fuzzy Sets and Systems, 24:1--26, 1987.

.... [17] while van Emden s quantitative deduction can be understood as an approximation of probabilistic logic programming under the conditional probability implication (as de ned in [14] The literature contains many other approaches to many valued logic programming (see, for example, 11] 30] [3], 8] and [19] and probabilistic logic programming (see, for example, 22] 26] 23] 24] 4] 14] and [21] To our knowledge, this paper is the rst to integrate numerical uncertainty in the form of probabilities over possible worlds, disjunction, and nonmonotonic negation in a uniform ....

J. F. Baldwin. Evidential support logic programming. Fuzzy Sets Syst., 24:1-26, 1987.

.... [18] while van Emden s quantitative deduction can be understood as an approximation of probabilistic logic programming under the conditional probability implication (as de ned in [14] The literature contains many other approaches to many valued logic programming (see, for example, 11] 31] [3], 8] and [20] and probabilistic logic programming (see, for example, 23] 27] 24] 25] 4] 14] and [22] To our knowledge, this paper is the rst to integrate numerical uncertainty in the form of probabilities over possible worlds, disjunction, and nonmonotonic negation in a uniform ....

J. F. Baldwin. Evidential support logic programming. Fuzzy Sets Syst., 24:1-26, 1987.

....primarily as expert systems, rather than using a database system. Some examples of expert systems using a high level language include FLISP [Sos90] in which the language LISP is extended to allow representation and manipulation of fuzzy data; FuzzyCLIPS [FCL94] which is written in C; and FRIL [Bal87], which is based on Prolog and extensions to logic programming. In [BuT89] a fuzzy expert system FLOPS is described, in which the notion of Upper and Lower confidence values for fuzzy rules is utilized. We have adapted this technique into our R2R system. This particular representation with two ....

J. Baldwin, "Evidential Support Logic Programming," Fuzzy Sets and Systems 24 (1987), 1-26.

....primarily as expert systems, rather than using a database system. Some examples of expert systems using a high level language include FLISP [Sos90] in which the language LISP is extended to allow representation and manipulation of fuzzy data; FuzzyCLIPS [FCL94] which is written in C; and FRIL [Bal87], which is based on Prolog and extensions to logic programming. In [BuT89] a fuzzy expert system FLOPS is described, in which the notion of Upper and Lower confidence values for fuzzy rules is utilized. We have adapted this technique into our R2R system. This particular representation with two ....

J. Baldwin, "Evidential Support Logic Programming," Fuzzy Sets and Systems 24 (1987), 1-26.

....and dynamic process behavior in an efficient and easy way. There exist several models which deal with these problems and we think a promising concept is ConFuP [10, 9] ConFuP combines the parallel semantic of Concurrent Prolog (CP) 20] with the fuzzy semantic of Support Logic Programming (SLOP) [2]. Thus, ConFuP is a concurrent logic programming language which offers a parallel process model and a fuzzy semantic. The application of concurrent logic programming languages like CP and FCP, a subset of CP, in the field of system modeling is well known [4, 6, 5] just as the application of SLOP ....

.... programming languages like CP and FCP, a subset of CP, in the field of system modeling is well known [4, 6, 5] just as the application of SLOP as AI language particular suited for knowledge base applications involving uncertainty in various forms, including fuzzy logic and nonmonotonic reasoning[1, 2, 17]. In section 2 and 3 we give a short introduction in Fuzzy Logic and ConFuP. Section 4 shows the application of modeling flexible manufacturing systems. 2 Fuzzy Logic and its Applications The modeling of human knowledge and reasoning requires the formulation of uncertainty in its various forms. ....

[Article contains additional citation context not shown here]

J.F. Baldwin. Evidential support logic programming. Fuzzy Sets and Systems, 24:1--26, 1987.

....disjuncts) van Emden [51] develops a quantitative logic programming language in which multiplication is used to assign truth values to conjunctions. Of course, probabilities can be multiplied only if the events are independent, and hence van Emden s framework is also non probabilistic. Baldwin [3] develops an operational model for evidential logic programming which is based on fuzzy set theory, and there is no immediately forthcoming model theoretic basis for his work. Our framework has its limitations. In particular, there is no provision made for expressing conditional probabilities. ....

J.F. Baldwin. (1987) Evidential Support Logic Programming, J. of Fuzzy Sets and Systems, 24, pps 1-26.

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J. F. Baldwin. Evidential support of logic programming. Fuzzy Sets and Systems, 24(1):1-26, 1987.

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J.F. Baldwin. Evidential support logic programming. Fuzzy sets and systems, 24(1): 1-26, 1987.

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J. F. Baldwin, \Evidential support logic programming", Fuzzy Sets and Systems 14 (1987) 1-26.

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J. F. Baldwin, "Evidential support logic programming", Fuzzy Sets and Systems 14 (1987) 1-26.

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J. F. Baldwin. Evidential support logic programming. Fuzzy Sets Syst., 24:1--26, 1987.

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J. F. Baldwin. Evidential support logic programming. Fuzzy Sets Syst., 24:1--26, 1987.

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J.F. Baldwin. (1987) Evidential Support Logic Programming, J. of Fuzzy Sets and Systems, 24, pps 1-26.

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J.F. Baldwin. (1987) Evidential Support Logic Programming, J. of Fuzzy Sets and Systems, 24, pps 1-26.

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