A Tool for Automatic Creation of Rule-Based Expert Systems with CFs

International audienceThis paper introduces a tool, namely ACRES (Automatic CReator of Expert Systems), which can automatically produce rule-based expert systems as CLIPS scripts from a dataset containing knowledge about a problem domain in the form of a large number of cases. The rules are created via a simple systematic approach and make use of certainty factors (CFs). CFs of same conclusions can be combined either using the MYCIN method or a generalization of MYCIN's method. This latter method requires calculation of some weights, based on a training dataset, via the use of a genetic algorithm. Creation of an expert system is outlined. Small scale experimental results comparing the above methods with each other and a neural network are finally presented

The Usability of E-learning Platforms in Higher Education: A Systematic Mapping Study

The use of e-learning in higher education has increased significantly in recent years, which has led to several studies being conducted to investigate the usability of the platforms that support it. A variety of different usability evaluation methods and attributes have been used, and it has therefore become important to start reviewing this work in a systematic way to determine how the field has developed in the last 15 years. This paper describes a systematic mapping study that performed searches on five electronic libraries to identify usability issues and methods that have been used to evaluate e-learning platforms. Sixty-one papers were selected and analysed, with the majority of studies using a simple research design reliant on questionnaires. The usability attributes measured were mostly related to effectiveness, satisfaction, efficiency, and perceived ease of use. Furthermore, several research gaps have been identified and recommendations have been made for further work in the area of the usability of online learning

Fuzzy and Neuro-Symbolic Approaches to Assessment of Bank Loan Applicants

Part 4: Fuzzy SystemsInternational audienceIn this paper, we present the design, implementation and evaluation of intelligent methods that assess bank loan applications. Assessment concerns the ability/possibility of satisfactorily dealing with loan demands. Different loan programs from different banks may be proposed according to the applicant’s characteristics. For each loan program, corresponding attributes (e.g. interest, amount of money that can be loaned) are also calculated. For these tasks, two separate intelligent systems have been developed and evaluated: a fuzzy expert system and a neuro-symbolic expert system. The former employs fuzzy rules based on knowledge elicited from experts. The latter is based on neurules, a type of neuro-symbolic rules that combine a symbolic (production rules) and a connectionist (adaline unit) representation. Neurules were produced from available patterns. Evaluation showed that performance of both systems is close although their knowledge bases were derived from different types of source knowledge

Multi-inference with Multi-neurules

Neurules are a type of hybrid rules combining a symbolic and a connectionist representation. There are two disadvantages of neurules. The first is that the created neurule bases usually contain multiple representations of the same piece of knowledge. Also, the inference mechanism is rather connectionism oriented than symbolism oriented, thus reducing naturalness. To remedy these deficiencies, we introduce an extension to neurules, called multineurules, and an alternative inference process, which is rather symbolism oriented. Experimental results comparing the two inference processes are also presented