Detecting and correcting errors in ruled-based expert systems : an integration of empirical and explanation-based learning

In this paper, we argue that techniques proposed for combining empirical and explanation-based learning methods can also be used to detect errors in rule-based expert systems, to isolate the blame for these errors to a small number of rules and suggest revisions to the rules to eliminate these errors. We demonstrate that FOCL, an extension to Quinlan's FOIL program, can learn in spite of an incorrect domain theory (e.g., a knowledge base of an expert system that contains some erroneous rules). A prototype knowledge acquisition tool, KR-FOCL, has been constructed that can utilize a trace of FOCL to suggest revisions to a rule base

An information-based approach to integrating empirical and explanation-based learning

We describe a new approach to integrating explanation-based and empirical learning methods for learning relational concepts. The approach uses an information-based heuristic to evaluate components of a hypothesis that are proposed either by explanation-based or empirical methods. Providing domain knowledge to the integrated system can decrease the amount of search required during learning and increase the accuracy of learned concepts, even when the domain knowledge is incorrect and incomplete and there is noise in the training data

Detecting and correcting errors in ruled-based expert systems : an integration of empirical and explanation-based learning

In this paper, we argue that techniques proposed for combining empirical and explanation-based learning methods can also be used to detect errors in rule-based expert systems, to isolate the blame for these errors to a small number of rules and suggest revisions to the rules to eliminate these errors. We demonstrate that FOCL, an extension to Quinlan's FOIL program, can learn in spite of an incorrect domain theory (e.g., a knowledge base of an expert system that contains some erroneous rules). A prototype knowledge acquisition tool, KR-FOCL, has been constructed that can utilize a trace of FOCL to suggest revisions to a rule base

Knowledge Acquisition with a Knowledge-Intensive Machine Learning System

In this paper, we investigate the integration of knowledge acquisition and machine learning techniques. We argue that existing machine learning techniques can be made more useful as knowledge acquisition tools by allowing the expert to have greater control over and interaction with the learning process. We describe a number of extensions to FOCL (a multistrategy Horn-clause learning program) that have greatly enhanced its power as a knowledge acquisition tool, paying particular attention to the utility of maintaining a connection between a rule and the set of examples explained by the rule. The objective of this research is to make the modification of a domain theory analogous to the use of a spread sheet. A prototype knowledge acquisition tool, FOCL-1-2-3, has been constructed in order to evaluate the strengths and weaknesses of this approach. 1 1.0 Introduction The emphasis of our research has been on the integration of machine learning and knowledge acquisition techniques to fac..

An information-based approach to integrating empirical and explanation-based learning

We describe a new approach to integrating explanation-based and empirical learning methods for learning relational concepts. The approach uses an information-based heuristic to evaluate components of a hypothesis that are proposed either by explanation-based or empirical methods. Providing domain knowledge to the integrated system can decrease the amount of search required during learning and increase the accuracy of learned concepts, even when the domain knowledge is incorrect and incomplete and there is noise in the training data

Traps and pitfalls when learning logical theories : a case study with FOIL and FOCL

Supervised concept learning based on Horn clauses is one of the most active areas of machine learning research. Two popular systems in this area are FOIL (First Order Inductive Learner) and FOCL (First Order Combined Learner). This paper points out sorne conceptual traps and pitfalls in which these two systems fall when they cope with some tasks taken both from the machine learning literature and from real world domains. An interpretation of the obtained results is provided. It is based on a comparison between the search space that these two systems should explore and the search space that they actually explore and on considerations about the representation of the examples as tuples in a relational database. Theoretically-founded solutions to the detected problems are suggested. Moreover, a more manageable practical solution is proposed and its strengths and weaknesses in comparison with the theoretically-founded ones are evaluated. Such a solution has been satisfactorily implemented in a new version of FOCL

Traps and pitfalls when learning logical theories : a case study with FOIL and FOCL

Supervised concept learning based on Horn clauses is one of the most active areas of machine learning research. Two popular systems in this area are FOIL (First Order Inductive Learner) and FOCL (First Order Combined Learner). This paper points out sorne conceptual traps and pitfalls in which these two systems fall when they cope with some tasks taken both from the machine learning literature and from real world domains. An interpretation of the obtained results is provided. It is based on a comparison between the search space that these two systems should explore and the search space that they actually explore and on considerations about the representation of the examples as tuples in a relational database. Theoretically-founded solutions to the detected problems are suggested. Moreover, a more manageable practical solution is proposed and its strengths and weaknesses in comparison with the theoretically-founded ones are evaluated. Such a solution has been satisfactorily implemented in a new version of FOCL