Search results

Pattern‐directed inference systems (P.D.I.S.) are among the most largely used tools in A.I. to‐day in order to represent and exploit knowledge. Generally, P.D.I.S.'s use production rules triggered by matching between rule patterns and elements of the data base. However, the lack of flexibility in the matching remains a drawback in this kind of system. In the framework of the communication in natural language with robots, approximate descriptions of real world situations and approximately specified rules are needed; furthermore, similarity in the matching process does not always need to be perfect. Thus, the pervading fuzziness of natural language can be taken into account. The following levels, belonging to the real interval [0,1], are evaluated: The possibility of similarity between referents designated in the data and in the pattern respectively; the necessity that a referent designated in the data is similar to a referent designated in the pattern. Designations are fuzzy when the pattern or the data are fuzzy, which is usual with words of a natural language.

Fuzzy numbers are often used to represent non-probabilistic uncertainty in engineering, decision-making and control system applications. In these applications, fuzzy arithmetic operations are frequently used for solving mathematical equations that contain fuzzy numbers. There are two approaches proposed in the literature for implementing fuzzy arithmetic operations: the α-cut approach and the extension principle approach using different t-norms. Computational methods for the implementation of fuzzy arithmetic operations in different applications are also proposed in the literature; these methods are usually developed for specific types of fuzzy numbers. This chapter discusses existing methods for implementing fuzzy arithmetic on triangular fuzzy numbers using both the α-cut approach and the extension principle approach using the min and drastic product t-norms. This chapter also presents novel computational methods for the implementation of fuzzy arithmetic on triangular fuzzy numbers using algebraic product and bounded difference t-norms. The applicability of the α-cut approach is limited because it tends to overestimate uncertainty, and the extension principle approach using the drastic product t-norm produces fuzzy numbers that are highly sensitive to changes in the input fuzzy numbers. The novel computational methods proposed in this chapter for implementing fuzzy arithmetic using algebraic product and bounded difference t-norms contribute to a more effective use of fuzzy arithmetic in construction applications. This chapter also presents an example of the application of fuzzy arithmetic operations to a construction problem. In addition, it discusses the effects of using different approaches for implementing fuzzy arithmetic operations in solving practical construction problems.

The purpose of this paper is to address the need for new approaches in locating items that closely match user preference criteria due to the rise in data volume of knowledge bases resulting from Open Data initiatives. Specifically, the paper focuses on evaluating SPARQL qualitative preference queries over user preferences in SPARQL.

The paper outlines a novel approach for handling SPARQL preference queries by representing preferences through symbolic weights using the possibilistic logic (PL) framework. This approach allows for the management of symbolic weights without relying on numerical values, using a partial ordering system instead. The paper compares this approach with numerous other approaches, including those based on skylines, fuzzy sets and conditional preference networks.

The paper highlights the advantages of the proposed approach, which enables the representation of preference criteria through symbolic weights and qualitative considerations. This approach offers a more intuitive way to convey preferences and manage rankings.

The paper demonstrates the usefulness and originality of the proposed SPARQL language in the PL framework. The approach extends SPARQL by incorporating symbolic weights and qualitative preferences.

Time modeling is a crucial feature in many application domains. However, temporal information often is not crisp, but is subjective and fuzzy. The purpose of this paper is to address the issue related to the modeling and handling of imperfection inherent to both temporal relations and intervals.

On the one hand, fuzzy extensions of Allen temporal relations are investigated and, on the other hand, extended temporal relations to define the positions of two fuzzy time intervals are introduced. Then, a database system, called Fuzzy Temporal Information Management and Exploitation (Fuzz-TIME), is developed for the purpose of processing fuzzy temporal queries.

To evaluate the proposal, the authors have implemented a Fuzz-TIME system and created a fuzzy historical database for the querying purpose. Some demonstrative scenarios from history domain are proposed and discussed.

The authors have conducted some experiments on archaeological data to show the effectiveness of the Fuzz-TIME system. However, thorough experiments on large-scale databases are highly desirable to show the behavior of the tool with respect to the performance and time execution criteria.

The tool developed (Fuzz-TIME) can have many practical applications where time information has to be dealt with. In particular, in several real-world applications like history, medicine, criminal and financial domains, where time is often perceived or expressed in an imprecise/fuzzy manner.

The social implications of this work can be expected, more particularly, in two domains: in the museum to manage, exploit and analysis the piece of information related to archives and historic data; and in the hospitals/medical organizations to deal with time information inherent to data about patients and diseases.

This paper presents the design and characterization of a novel and intelligent database system to process and manage the imperfection inherent to both temporal relations and intervals.

In previous work, the algebraical properties of this rule and its relationship with other generalized operator was studied. In this paper, the aim is to focus on one of the previous steps, which consists in certainty qualification, and it is investigated how this factor influences the behavior of the induced combination rule.

Dubois and Prade have proposed an adaptive combination rule that moves gradually from a conjunctive mode to a disjunctive mode as soon as the conflict between the sources increases. The proposal can be viewed as a result of some rational steps. This includes: conjunctive combination; re‐normalization of a subnormal result that may results from conjunctive operation where the lack of normalization is interpreted as a conflict; certainty qualification; restriction of the conflict influence; generalization to more than two sources.

Algebraical properties of the proposals have been investigated and illustrations of some special cases are highlighted and evaluated. Further studies continue in Part II.

New functional adaptive rules are put forward based on Residual implicators and t‐conorm operators.

The first part of this issue investigated the properties of the adaptive rule initially proposed by Dubois and Prade given in the framework of possibility theory, when the certainty qualification is rather expressed in more general t‐norms and t‐conorms connectives. This led to two new family of adaptive rules expressed using residual implication and t‐conorm connective, respectively. The problem of addressing uncertain inputs has also been examined and a waved decomposition has been proposed in PII we study adaptative combinations with incomplete certainty qualification. However, another problem that arises when combining uncertain inputs consists of the relationship between the certainty attached to the inputs and the certainty attached to the output, conceptualized by the resulting distribution when using adaptive combination rule. In other words, how does the combination rule improves or deteriorates the certainty of the overall system? This paper seeks to address this issue.

This paper fully addresses this issue and attempts to evaluate the combination rule from the certainty viewpoint attached to the result in comparison to initial certainty values attached to the inputs.

Especially, it has been proven that under certain hypotheses, the rule allows the user to hide the local certainties attached to the initial inputs, while highlighting only the certainty due to the lack of consistency among the sources.

New functional adaptative rules are put forward based on residual implicators and t‐conorm operators.

Construction is a highly dynamic environment with numerous interacting factors that affect construction processes and decisions. Uncertainty is inherent in most aspects of construction engineering and management, and traditionally, it has been treated as a random phenomenon. However, there are many types of uncertainty that are not naturally modelled by probability theory, such as subjectivity, ambiguity and vagueness. Fuzzy logic provides an approach for handling such uncertainties. However, fuzzy logic alone has some limitations, including its inability to learn from data and its extensive reliance on expert knowledge. To address these limitations, fuzzy logic has been combined with other techniques to create fuzzy hybrid techniques, which have helped solve complex problems in construction. In this chapter, a background on fuzzy logic in the context of construction engineering and management applications is presented. The chapter provides an introduction to uncertainty in construction and illustrates how fuzzy logic can improve construction modelling and decision-making. The role of fuzzy logic in representing uncertainty is contrasted with that of probability theory. Introductory material is presented on key definitions, properties and methods of fuzzy logic, including the definition and representation of fuzzy sets and membership functions, basic operations on fuzzy sets, fuzzy relations and compositions, defuzzification methods, entropy for fuzzy sets, fuzzy numbers, methods for the specification of membership functions and fuzzy rule-based systems. Finally, a discussion on the need for fuzzy hybrid modelling in construction applications is presented, and future research directions are proposed.

Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems.

The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems.

The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field.

Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.

This paper deals with a problem encountered in natural language generation which seems to have been largely ignored in the literature, that of generating non‐ambiguous (i.e. discriminating) designations of objects in a given context, from a knowledge basis, which associates the properties and relations, concerning the objects present in the environment, with their respective formal labels. A search algorithm of type A is proposed, which always generates a discriminating designation when such a designation exists in terms of the available knowledge; for the evaluation the algorithm uses a subjective length function which takes into account the “intelligibility” of the designation. This work takes place in the SYROCO system, a dialogue interface for limited domains of discourse; the sentence interpretation as well as the sentence generation in SYROCO are briefly presented in the first part of this paper.

It has long been recognised that humans draw from a large pool of processing aids to help manage the everyday challenges of life. It is not uncommon to observe individuals adopting simplifying strategies when faced with ever increasing amounts of information to process, and especially for decisions where the chosen outcome will have a very marginal impact on their well-being. The transactions costs associated with processing all new information often exceed the benefits from such a comprehensive review. The accumulating life experiences of individuals are also often brought to bear as reference points to assist in selectively evaluating information placed in front of them. These features of human processing and cognition are not new to the broad literature on judgment and decision-making, where heuristics are offered up as deliberative analytic procedures intentionally designed to simplify choice. What is surprising is the limited recognition of heuristics that individuals use to process the attributes in stated choice experiments. In this paper we present a case for a utility-based framework within which some appealing processing strategies are embedded (without the aid of supplementary self-stated intentions), as well as models conditioned on self-stated intentions represented as single items of process advice, and illustrate the implications on willingness to pay for travel time savings of embedding each heuristic in the choice process. Given the controversy surrounding the reliability of self-stated intentions, we introduce a framework in which mixtures of process advice embedded within a belief function might be used in future empirical studies to condition choice, as a way of increasingly judging the strength of the evidence.