Ioannis Hatzilygeroudis

Categorizing approaches combining rule-based and case-based reasoning

Abstract: Rule-based and case-based reasoning are two popular approaches used in intelligent systems. Rules usually represent general knowledge, whereas cases encompass knowledge accumulated from specific (specialized) situations. Each approach has advantages and disadvantages, which are proved to be complementary to a large degree. So, it is well justified to combine rules and cases to produce effective hybrid approaches, surpassing the disadvantages of each component method. In this paper, we first present advantages and disadvantages of rule-based and case-based reasoning and show that they are complementary. We then discuss the deficiencies of existing categorization schemes for integrations of rule-based and case-based representations. To deal with these deficiencies, we introduce a new categorization scheme. Finally, we briefly present representative approaches for the final categories of our scheme.

Using a hybrid rule-based approach in developing an intelligent tutoring system with knowledge acquisition and update capabilities

In this paper, we present the architecture and describe the functionality of an Intelligent Tutoring System (ITS), which uses an expert system to make decisions during the teaching process. The expert system uses neurules for knowledge representation of the pedagogical knowledge. Neurules are a type of hybrid rules integrating symbolic rules with neurocomputing. The expert system consists of three components: the user modelling unit, the pedagogical unit and the inference system. The pedagogical knowledge is distributed in a number of neurule bases within the user modelling and the pedagogical unit. Another important component of the ITS, for both its development and maintenance, is its knowledge management unit, which provides knowledge acquisition and knowledge update capabilities to the system, that is, offers expert knowledge authoring capabilities to the system.

A Web-Based Intelligent Tutoring System Using Hybrid Rules as Its Representational Basis

In this paper, we present the architecture and describe the functionality of a Web-based Intelligent Tutoring System (ITS), which uses neurules for knowledge representation. Neurules are a type of hybrid rules integrating symbolic rules with neurocomputing. The use of neurules as the knowledge representation basis of the ITS results in a number of advantages. Part of the functionality of the ITS is controlled by a neurule-based inference engine. Apart from that, the system consists of four other components: the domain knowledge, containing the structure of the domain and the educational content, the user modeling component, which records information concerning the user, the pedagogical model, which encompasses knowledge regarding the various pedagogical decisions, and the supervisor unit that controls the functionality of the whole system. The system focuses on teaching Internet technologies.

Integrating (rules, neural networks) and cases for knowledge representation and reasoning in expert systems

In this paper, we present an approach that integrates symbolic rules, neural networks and cases. To achieve it, we integrate a kind of hybrid rules, called neurules, with cases. Neurules integrate symbolic rules with the Adaline neural unit. In the integration, neurules are used to index cases representing their exceptions. In this way, the accuracy of the neurules is improved. On the other hand, due to neurule-based efficient inference mechanism, conclusions can be reached more efficiently. In addition, neurule-based inferences can be performed even if some of the inputs are unknown, in contrast to symbolic rule-based inferences. Furthermore, an existing symbolic rule-base with indexed exception cases can be converted into a neurule-base with corresponding indexed exception cases. Finally, empirical data can be used as a knowledge source, which facilitates knowledge acquisition. We also present a new high-level categorization of the approaches integrating rule-based and case-based reasoning.

NEURULES: IMPROVING THE PERFORMANCE OF SYMBOLIC RULES

In this paper, we present a method for improving the performance of classical symbolic rules. This is achieved by introducing a type of hybrid rules, called neurules, which integrate neurocomputing into the symbolic framework of production rules. Neurules are produced by converting existing symbolic rules. Each neurule is considered as an adaline unit, where weights are considered as significance factors. Each significance factor represents the significance of the associated condition in drawing the conclusion. A rule is fired when the corresponding adaline output becomes active. This significantly reduces the size of the rule base and, due to a number of heuristics used in the inference process, increases efficiency of the inferences.

Integrating Hybrid Rule-Based with Case-Based Reasoning

In this paper, we present an approach integrating neurule-based and case-based reasoning. Neurules are a kind of hybrid rules that combine a symbolic (production rules) and a connectionist representation (adaline unit). Each neurule is represented as an adaline unit. One way that the neurules can be produced is from symbolic rules by merging the symbolic rules having the same conclusion. In this way, the number of rules in the rule base is decreased. If the symbolic rules, acting as source knowledge of the neurules, do not cover the full complexities of the domain, accuracy of the produced neurules is affected as well. To improve accuracy, neurules can be integrated with cases representing their exceptions. The integration approach enhances a previous method integrating symbolic rules with cases. The use of neurules instead of symbolic rules improves the efficiency of the inference mechanism and allows for drawing conclusions even if some of the inputs are unknown.

Using a hybrid AI approach for exercise difficulty level adaptation

An intelligent and adaptive web-based education system is presented. The system uses a hybrid AI approach, a combination of an expert systems approach and a genetic algorithm approach, to determine the difficulty levels of the provided exercises. The genetic algorithm is used to extract some kind of rules from the data acquired from the interactions of the students. Those rules are used to modify expert rules provided by the Tutor. In this way, feedback from the students is taken into account for determination of the difficulty levels of the questions/exercises. Experimental results show the validity of the method.

Knowledge Representation Requirements for Intelligent Tutoring Systems

In this paper, we make a first effort to define requirements for knowledge representation (KR) in an ITS. The requirements concern all stages of an ITS’s life cycle (construction, operation and maintenance), all types of users (experts, engineers, learners) and all its modules (domain knowledge, user model, pedagogical model). We also briefly present and compare various KR formalisms used (or that could be used) in ITSs as far as the specified KR requirements are concerned. It appears that various hybrid approaches to knowledge representation can satisfy the requirements in a greater degree than that of single representations. Another finding is that there is not a hybrid formalism that can satisfy the requirements of all of the modules of an ITS, but each one individually. So, a multi-paradigm representation environment could provide a solution to requirements satisfaction.

Recognizing emotions in text using ensemble of classifiers

Emotions constitute a key factor in human nature and behavior. The most common way for people to express their opinions, thoughts and communicate with each other is via written text. In this paper, we present a sentiment analysis system for automatic recognition of emotions in text, using an ensemble of classifiers. The designed ensemble classifier schema is based on the notion of combining knowledge-based and statistical machine learning classification methods aiming to benefit from their merits and minimize their drawbacks. The ensemble schema is based on three classifiers; two are statistical (a Naive Bayes and a Maximum Entropy learner) and the third one is a knowledge-based tool performing deep analysis of the natural language sentences. The knowledge-based tool analyzes the sentences text structure and dependencies and implements a keyword-based approach, where the emotional state of a sentence is derived from the emotional affinity of the sentences emotional parts. The ensemble classifier schema has been extensively evaluated on various forms of text such as, news headlines, articles and social media posts. The experimental results indicate quite satisfactory performance regarding the ability to recognize emotion presence in text and also to identify the polarity of the emotions.

Knowledge-Based Adaptive Assessment in a Web-Based Intelligent Educational System

In this paper, we present an adaptive and intelligent Web-based educational system that uses AI techniques for personalized assessment of the learners. More specifically, we focus on a mechanism for on-line creation of a user-adapted test, which can be used alongside the predetermined test. The user can ask for such a test any time he/she is willing to do so, even if he/she has not studied all predetermined concepts of a learning goal. A small rule base is used by an expert system inference engine for making decisions on the difficulty level of the exercises to be included in the test. This is based on the evaluation of the learner during concept studying. Adaptive assessment of the learner can be repeatedly used until there is no further need. Another small rule-base is used for deciding on whether a new test is suggested or not. This is based on the learners previous test assessment results. Preliminary experimental results show that the users need less time to study a learning goal when using the adaptive assessment capability of the system