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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.

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.

A problem of handling fuzzy quantities in a process of knowledge acquisition and deriving an inference mechanism by means of fuzzy relation equations is studied in extensive way. It is clearly pointed out that both of them are closely related and correspond to various types of fuzzy relation equations that are considered. Their relevance to the form of knowledge collected is also indicated. A problem of dimension reduction of a knowledge base is considered as well. Two modes of the use of the knowledge base (goal‐, and data‐driven) are also studied.

In this paper a new approach is proposed for dealing with uncertainty in reasoning. A numerical quantification based on possibility theory is used in the representation of uncertain facts or rules. The chaining of uncertain rules and the combination of results obtained from different chains of inference, are discussed at length. Only min or max operations are used in the chaining and in the combining processes for computing the possibility degrees corresponding to the different alternatives. Partial similarities with other approaches are pointed out.

The notion of Roi de rats corresponds to a functional association between values of variables. The causality relations between the more diversified properties of objects (concrete on abstract objects) can be easily represented and automatically dealt with. It was first introduced in the framework of the production rules system, ARGOS‐II; this notion is also implemented in TLISP. After preliminary remarks about variables, evaluation and assignment, we define direct variables and partitioned/tied variables and we give some examples of Roi de rats. Then the implementation of the Roi de rats by the TLISP interpreter is briefly described. We present three types of applications: Updating process of a data‐basis, checking of a variable and definition of a generator variable. In concluding remarks we observe that the RDR mechanism can be naturally expressed in PLASMA.

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.

The purpose of this paper is to describe the components, structure and theoretical underpinnings of a cognitive remediation intervention that was delivered within a supported education program for mental health survivors.

In total, 21 participants enrolled in the course Strengthening Memory, Concentration and Learning (PREP 1033 at George Brown College (GBC)) with the diagnosis of depression, anxiety, PTSD, ED and substance use disorder were included in the research. After a baseline assessment, participants completed 14 week cognitive remediation training (CRT) protocol that included six essential components that were integrated and implemented within the course structure of the supported education program at GBC. This was followed by a post-training assessment.

Analysis of the participants’ performance on CRT protocol using computerized games showed little significant progress. However, the research found a positive change in the self-esteem of the participants that was statistically significant and the findings also aligned with the social and emotional learning framework.

One of the limitations in the research was the use of computer-assisted cognitive remediation in the form of the HappyNeuron software. The value and relevance of computer assisted needs are to be further examined. It seems that the implementation of the course that explicitly address cognitive challenges creates a supportive environment can be helpful.

Despite the mixed results and the few limitations associated with the CRT intervention reported in the research, the study offers reminders of the complexity of cognitive remediation and all the factors involved that need to be taken into consideration.

This research created explicit space for addressing some of the implicit assumptions about the cognitive abilities when in post-secondary education.

This work is based on author’s previous work on cognitive remediation research within the supported education setting.

Environmental impact and changes are becoming essential in textile and yarn industries, where reliable measurement of parameters related to processing harmful substances needs to be examined. Such findings can be cumulated using smart assessment like life cycle analysis. The ecological impact category, supply chain, and climate-changing factors were considered for the necessary assessment.

This paper applies the Life Cycle Assessment technique in the textile and yarn industry to estimate critical environmental potentials. The critical input for the fabric and yarn industry was put in the GaBi software model to estimate various environmental potentials.

Global warming potential, electricity, and raw cotton consumption in the fabric and yarn industry were critical concerns where attention should be focused on minimizing environmental potentials from cradle to gate assessment.

This qualitative study is made via the industry case-wise inputs and outputs, which can vary with demographic conditions. Some machine and human constraints have not been implemented in modelling life cycle model for smart simulation. Smart simulation helps in linking different parameters and simulates their combined effects on the product life cycle.

This modelling approach will help access pollution constituents in different supply chain production processes and optimize them simultaneously.

The raw data used in this analysis are collected from an Indian small scale textile industry. In the textile fabrication industry, earlier assessments were carried out in cotton generation, impact of PET, cradle to grave assessment of textile products and garment processing only. In this research the smart model is drawn to consider each input parameter of yarn and textile fabric to determine the criticality of each input in this assessment. This article mainly talks about life cycle and circular supply assessment applied to first time for both cotton to yarn processing and yarn to fabric industry for necessary estimation of environment potentials.

The study aims to define a research agenda for creating resource-efficient supply chains (RESCs) by identifying and analysing their key characteristics as well as future research opportunities.

We follow a systematic review method to analyse the literature and to understand RESC, taking a substantive theory approach. Our approach is grounded in a specific domain, the agri-food sector, because it is an intensive user of an extensive range of resources.

The review shows that works of literature has looked at the use of resources primarily from the environmental impact perspective. There is a need to explore whether or not and how logistics/supply chain decisions will affect the overall configuration of future food supply chains in an era of resource scarcity and depletion and what the trade-offs will be.

The paper proposes an agenda for future research in the area of RESC. The framework proposed along with the key characteristics identified for RESC can be applied to other sectors.

Our research should facilitate further understanding of the implications and trade-offs of supply chain decisions taken on the use of resources by supply chain managers.

The paper explores the interaction between supply chains and natural resources and defines the key characteristics of RESC.