Explains for the first time how "computing with words" can aid in making subjective judgments Lotfi Zadeh, the father of fuzzy logic, coined the phrase "computing with words" (CWW) to describe a methodology in which the objects of computation are words and propositions drawn from a natural language. Perceptual Computing explains how to implement CWW to aid in the important area of making subjective judgments, using a methodology that leads to an interactive devicea "Perceptual Computer"that propagates random and linguistic uncertainties into the subjective judgment in a way that can be modeled and observed by the judgment maker. This book focuses on the three components of a Perceptual Computerencoder, CWW engines, and decoderand then provides detailed applications for each. It uses interval type-2 fuzzy sets (IT2 FSs) and fuzzy logic as the mathematical vehicle for perceptual computing, because such fuzzy sets can model first-order linguistic uncertainties whereas the usual kind of fuzzy sets cannot. Drawing upon the work on subjective judgments that Jerry Mendel and his students completed over the past decade, Perceptual Computing shows readers how to: Map word-data with its inherent uncertainties into an IT2 FS that captures these uncertainties Use uncertainty measures to quantify linguistic uncertainties Compare IT2 FSs by using similarity and rank Compute the subsethood of one IT2 FS in another such set Aggregate disparate data, ranging from numbers to uniformly weighted intervals to nonuniformly weighted intervals to words Aggregate multiple-fired IF-THEN rules so that the integrity of word IT2 FS models is preserved Free MATLAB-based software is also available online so readers can apply the methodology of perceptual computing immediately, and even try to improve upon it. Perceptual Computing is an important go-to for researchers and students in the fields of artificial intelligence and fuzzy logic, as well as for operations researchers, decision makers, psychologists, computer scientists, and computational intelligence experts.

Perceptual Computing: Aiding People in Making Subjective Judgments

Computing with words and its relationships with fuzzistics

Modeling uncertainty in multi-criteria decision analysis

Measurement of membership functions and their acquisition

An approximate analogical reasoning schema (AARS) which exhibits the advantages of fuzzy set theory and analogical reasoning in expert systems development is described. The AARS avoids going through the conceptually complicated compositional rule of inference. It uses a similarity measure of fuzzy sets as well as a threshold to determine whether a rule should be fired and a modification function inferred from a similarity measure to deduce a consequent. Some numerical examples to illustrate the operation of the schema are presented. Finally, the proposed schema is compared with conventional expert systems and existing fuzzy expert systems. >

An approximate analogical reasoning approach based on similarity measures

Membership functions I: Comparing methods of measurement

Membership functions II: Trends in fuzziness and implications

The practice of soil improvement has received renewed attention in the last two decades. As a result, existing methods were improved and new ones developed. This paper presents a knowledge‐based decision support system for the selection of soil improvement methods, Improve. The system uses a knowledge representation structure based on “windows” together with a best‐first search algorithm. In this context, windows are mathematical representations of the restrictions to the values variables can take, combining the knowledge and its uncertainty in a unique entity. This form of knowledge representation has many advantages and allows for procedures not available in other systems, such as the development of composite solutions, the use of different evaluation functions, the search for lacunae, and the case‐based representation of knowledge.

Knowledge‐Based System for Soil Improvement

The overall purpose of this study was to examine the influence of changes in the retained fill and the foundation soil on wall behavior. This technical note presents some of the results obtained in analyzing departures from similarity and assessing model repeatability, While the study examined the behavior of reinforced soil prototypes, results of these tests are relevant to the modeling of other systems as well.

Centrifuge Modeling: A Study of Similarity

Failures, understood in a broad sense as the poor performance of a system, can be analyzed from different perspectives. A top level view finds human cognitive characteristics determining performance. While cognitive psychology is frequently mentioned in the literature of varied fields (from artificial intelligence to civil engineering), its findings are seldom noted. In this paper, a summary of the most relevant human biases and limitations is presented and exemplified with civil engineering cases. Alternative means of reducing their effects are then discussed.

Juan Carlos Santamarina

Papers

Membership functions I: Comparing methods of measurement

Membership functions II: Trends in fuzziness and implications

Knowledge‐Based System for Soil Improvement

Centrifuge Modeling: A Study of Similarity

Limitations in Decision Making and System Performance