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.

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.

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.

Develops a general framework for processes of matching fuzzy quantities. Indicates how different hierarchy levels of matching indices are constructed from a relational way of description of the matching process. Making use of max‐min and min‐max fuzzy relation equations, respectively, clarifies how the entire matching proceeds. Moreover formulates an inverse problem. The method provided here enables us to distinguish regions of the universe of discourse in which the quantities are specified, which are considered as fully supporting the given concept (completely matching observed); completely excluded with respect to this concept; and being of a borderline character. As a consequence the results of matching can have a thorough interpretation.

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.

The purpose of this paper is to examine whether there is a cultural gap in student perceptions of online collaborative learning and to investigate to what extent student perceptions, motivation, and learning strategies change over time due to the actual involvement in a collaborative e‐learning environment (ELE).

A parallel e‐learning environment for a first‐year university course is implemented for a Flemish group (n = 217) at Ghent University and a Chinese group (n = 165) at Beijing Normal University. Student perceptions of the online collaborative learning environment and their motivation and learning strategies before and after the ELE are measured.

The findings show that the Flemish group perceive the online collaborative learning environment more positively compared to the Chinese group. However, Chinese students' motivation, and learning strategies change significantly towards a way that is more in line with a social‐constructivist learning approach after the online collaborative learning experience.

The current results indicate that students from different cultural contexts perceive online collaborative learning environments differently. Specific cultural adaptations in e‐learning design could be considered when an ELE is to be implemented cross‐culturally.

This study can help both Chinese and Western instructors to become aware of different perceptions of online collaborative learning and to be more supportive to the students.

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.

Learning objects strive for reusability in e‐Learning to reduce cost and allow personalization of content. We show why learning objects require adapted Information Retrieval systems. In the spirit of the Semantic Web, we discuss the semantic description, discovery, and composition of learning objects. As part of our project, we tag learning objects with both objective (e.g., title, date, and author) and subjective (e.g., quality and relevance) metadata. We present the RACOFI (Rule‐Applying Collaborative Filtering) Composer prototype with its novel combination of two libraries and their associated engines: a collaborative filtering system and an inference rule system. We developed RACOFI to generate context‐aware recommendation lists. Context is handled by multidimensional predictions produced from a database‐driven scalable collaborative filtering algorithm. Rules are then applied to the predictions to customize the recommendations according to user profiles. The RACOFI Composer architecture has been developed into the contextaware music portal inDiscover.

The paper aims to describe ideas and implementation of the computer tool for computer‐aided and recycling‐oriented design. Currently, there is a strong tendency to take into account the impact of a product on the natural environment. The authors concentrated on the issue of the recycling process of the product, taking into account the phase of its design. The purpose, structure, technology and example results are presented in this paper.

It was assumed that analysis will be performed based on a set of measures of a product. These measures (developed by the authors), describing the product from the recycling point of view, are calculated automatically, according to the changes (variants) in the product's model. The presented tool is based on agent technology. The structure of the system – the agents, its roles and communication between them – has been described.

The main achievement of the work presented in this paper is the method supporting eco‐design based on agent technology. Based on the analysis of the process of recycling‐oriented eco‐design, the authors designed and then implemented the tool that aids designers' activities in the area of eco‐design. The drawn‐up method supports decision making concerning designing environmental‐friendly products. Thanks to agent technology, the design process can be conducted in the distributed design environment.

The usage of the presented computer‐aided and recycling‐oriented system during the design process requires the duplication of some of the engineer's work. The analysis is performed within the presented tool, outside the typical contemporary design environment, such as CAD 3D systems. As a consequence, there is a need to enter the product structure into the described system, which can be inconvenient. The further work of the authors assumes implementation of this concept into a CAD 3D system.

There has been a limited amount of research work regarding application agent technology in the field of end‐of‐life oriented design. The structure of the system, the measurements and idea of extension of product structure are the original results of the work. The results could be implemented into commercial computer‐aided design systems, especially into its PLM (product lifecycle management) group, due to the weak representation of end‐of‐life phases in these tools.