Finding New Rules for Incomplete Theories: Induction with Explicit Biases in Varying Contexts

Many AI problem solvers possess explicitly encoded knowledge - a domain theory ““ that they use to solve problems. If these problem solvers are to be autonomous, they must be able to detect and to fill gaps in their own knowledge. The field of machine learning addresses this issue. Recently two disparate machine learning approaches have emerged as predominant in the field: explanation-based learning (EBL) and similarity-based learning (SBL), EBL and SBL have been applied to problems in a variety of domains. Both methods have clear problems, however, EBL assumes that a system is given an explicit theory of the domain that is complete, correct, and tractable. These assumptions are clearly unrealistic for most complex, real-world problems. SBL suffers because of its lack of an explicit theory of the domain. The simplicity of the method requires that human intervention playa large role in tailoring input examples and the features describing them in such a way as to allow a system to choose an appropriate set of features to define a concept. Biasing a system in this way may result in its being unable to discover all concepts in even a Single domain. Less tailoring of the examples leaves a system open to the possibility of not converging on the best definition for a concept, or any at all, due to the computational complexity. The research described in this proposal addresses a number of the problems found in explanation-based and similarity-based learning. The major focus of the research is the elimination of the assumption that the domain theory of an EBL system is complete. In particular, it considers the problem of working with an incomplete theory by suggesting a method by which gaps in an EBL system's knowledge can be detected and filled. We suggest that when EBL cannot derive a complete explanation, the partial explanation focus a context in which learning takes place. Information extracted from partial explanations, as well as from complete explanations, can be exploited by SBL to do better induction of the missing domain knowledge. The extracted information constitutes an explicit bias for similarity-based learning. A second problem to be addressed is that of making the biases of SBL explicit. Finally, all testing of the claims made in this proposal is to be done in the Gemini learning system. The development of the system addresses the goal of constructing an integrated learning architecture utilizing both EBL and SBL

A Survey of Machine Learning Systems Integrating Explanation-Based and Similarity-Based Methods

Two disparate machine learning approaches have received considerable attention. These are explanation-based and similarity-based learning. The basic goal of an explanation-based learning system is to more efficiently recognize concepts that it is already capable of recognizing. The learning process involves a knowledge-intensive analysis of an environment-provided example of a concept in order to extract its characteristic features. The basic goal of a similarity-based system, on the other hand, is to acquire descriptions that allow the system to recognize concepts it does not yet know. Although they have been applied with some success to problems in a variety of domains, both methods have clear deficiencies. Explanation-based learning assumes that a system will be provided with an explicit domain theory that is complete, correct, and tractable. This assumption is unrealistic for many complex, real-world domains. Similarity-based learning suffers because of its lack of an explicit theory. Since the two methods are complementary in nature, an obvious solution is to augment systems using one approach with techniques from the other. This survey discusses machine learning systems that integrate explanation-based and similarity-based learning methods such that one is incorporated primarily to handle a deficiency of the other. Although sufficient background material is provided that the reader need not be familiar with machine learning, general knowledge of AI is assumed

Extraction and Use of Contextual Attributes for Theory Completion: An Integration of Explanation-Based and Similarity-Based Learning

Extraction and Use of Contextual Attributes for Theory Completion: An Integration of Explanation-Based and Similarity-Based Learning Andrea Pohoreckyj Danyluk This research investigates the use of contextual cues to address problems in machine learning that arise from assumptions about the initial knowledge that is necessary for the acquisition of new information. Machine learning approaches may be placed along a spectrum describing purely inductive to purely deductive techniques. Inductive systems possess essentially no explicit knowledge that can be used in acquiring new facts, while deductive systems are assumed to contain a complete theory of the domain. Most work in machine learning has concentrated on approaches at the two ends of the spectrum. This dissertation describes an approach that integrates inductive and deductive methods. It provides a mechanism by which induction can be used in order to detect and acquire knowledge missing from the domain theory of a deductive sys..

A Comparison of Data Sources for Machine Learning in a Telephone Trouble Screening Expert System

This paper describes a domain where the application of machine learning, specifically inductive learning, could have enormous positive impact. The domain possesses attributes that would indicate that inductive learning would easily succeed for this domain. In particular, data for this domain are abundant. In spite of this, numerous machine learning methods -- both inductive and otherwise -- have failed to learn a knowledge base having high accuracy. This paper presents a comparison of the data sources available for this domain. It focuses primarily on a survey system that was ultimately designed for the purpose of collecting data best suited to this task. Keywords: knowledge acquisition for expert systems; knowledge elicitation; data collection; data collection interfaces This research was performed while the author was an employee of NYNEX Science and Technology, Inc. 1 Introduction Many machine learning techniques, most notably inductive methods, rely upon data from which they ..

Problem Definition, Data Cleaning, and Evaluation: A Classifier Learning Case Study

This paper is a case study of this process based on a long-term project addressing the automatic dispatch of technicians to fix faults in the local loop of a telephone network. The bottom line of the project is that simple learning techniques can be effective. However, constructing a convincing argument to that effect is far from simple. In particular, we had to consult multiple sources to obtain class labels, use domain knowledge to clean up data, compare with existing methods, and evaluate with data from multiple locations. Finally, it was necessary to use decision-analytic techniques to evaluate the cost-effectiveness of the learned classifiers, because evaluation based on classification accuracy is misleading without an analysis of cost-effectiveness. Our view is that application studies should be helpful in guiding future research. Therefore, we conclude by outlining useful directions suggested by our experience on this long-term project. 1 Introductio

Andrea Pohoreckyj Danyluk

Introductory Artificial Intelligence is a particularly challenging course to teach. It is difficult to present a picture of AI that is both broad and detailed. It is difficult to represent the varying viewpoints within the AI community. Additional challenges arise when an AI course is intended to be a student's introduction to Computer Science. I am in the process of designing such a course. Background and Issues I am in the process of creating an Introductory AI course. This course will be unusual---and thus challenging---in that it is to be both an Introductory Computer Science course and an AI course. It is not an advanced Introduction to AI that assumes the student has a fair amount of background in Computer Science. The following are my goals for the course: ffl Teach about AI. ffl Teach programming principles; give the students interesting and challenging programming assignments. 1 ffl Make the course sufficiently exciting that the students would be interested in continuing ..

Feature Selection vs Theory Reformulation: a Study of Genetic Refinement of Knowledge-based Neural Networks

Expert classification systems have proven themselves effective decision makers for many types of problems. However, the accuracy of such systems is often highly dependent upon the accuracy of a human expert's domain theory. When human experts learn or create a set of rules they are subject to a number of hindrances. Most significantly experts are, to a greater or lesser extent, restricted by the tradition of scholarship which has preceded them and by an inability to examine large amounts of data in a rigorous fashion without the effects of boredom or frustration. As a result, human theories are often erroneous or incomplete. To escape this dependency, many machine learning systems have been developed to automatically refine and correct an expert's domain theory. When theory revision systems are applied to expert theories, they often concentrate on the reformulation of the knowledge provided rather than on the reformulation or selection of input features. The general assumption seems to..

Theory Refinement Through Knowledge-Based Feature Set Selection

Classification systems depend upon having the best (i.e., most relevant) set of input features from which a classification decision can then be made. This is true both for the classifiers themselves, and for the inductive learners that might be used to build them. This paper describes indigent (Improving kNown Domains with Genetically Engineered feaTures). indigent utilizes genetic search to perform knowledge-based feature selection. It assumes that it will be provided with a knowlegebased neural network. It then performs a genetic search for better network topologies, focusing entirely on the incorporation (or deletion) of input features. indigent can be viewed in one of two ways: (1) as a theory refinement system, or (2) as a feature selection system, guided by expertprovided domain knowledge. It can be used either alone or in conjunction with more extensive theory refinement systems. Introduction Classification systems depend upon having the best (i.e., most relevant) set of input..