The knowledge acquisition problem is a continuing problem in expert system development. The knowledge base (domain theory) initially formulated by the expert is usually only an approximation to the correct theory for the application domain. This initial knowledge base must be refined (usually manually) as problems are discovered. This research addresses the knowledge base refinement problem for classification tasks. The research provides an automatic method for correcting a domain theory in the light of incorrect performance on a set of training examples. The method uses attempted explanations to focus the correction on the failing part of the knowledge base. It then uses induction to supply a correction to the knoweledge base that will render it consistent with the training examples Using this technique, it is possible to correct overly general and overly specific theories, theories with multiple faults at various levels in the theory hierarchy, and theories involving multiple concepts. Methods have been developed for making corrections even in the presence of noisy data. Theoretical justification for the method is given in the form of convergence results that predict that the method will eventually converge to a hypothesis that is within a small error of the correct hypothesis, given sufficient examples. Because the technique currently relies on theorem proving for much of the analysis, it is quite expensive to computationally and heuristic methods for reducing the computational burden have been implemented. The system developed as part of the research is called EITHER (Explanation-based Inductive THeory Extension and Revision). EITHER uses propositional Horn clause logic as its knowledge representation, with examples expressed as attribute-value lists .The system has been tested in a variety of domains including revising a theory for the identification of promoters in DNA sequences and a theory for soybean disease diagnosis, where it has been shown to outperform a purely inductive approach.
ML ID: 277
Recent learning systems have combined explanation-based and inductive learning techniques to revise propositional domain theories (e.g. EITHER, RTLS, KBANN). Inductive systems working in first order logic have also been developed (e.g. CIGOL, FOIL, FOCL). This paper presents a theory revision system, Forte, that merges these two developments. Forte provides theory revision capabilities similar to those of the propositional systems, but works with domain theories stated in first-order logic.
ML ID: 256
Despite the fact that many symbolic and neural network (connectionist) learning algorithms address the same problem of learning from classified examples, very little is known regarding their comparative strengths and weaknesses. Experiments comparing the ID3 symbolic learning algorithm with the perception and backpropagation neural learning algorithms have been performed using five large, real-world data sets. Overall, backpropagation performs slightly better than the other two algorithms in terms of classification accuracy on new examples, but takes much longer to train. Experimental results suggest that backpropagation can work significantly better on data sets containing numerical data. Also analyzed empirically are the effects of (1) the amount of training data, (2) imperfect training examples, and (3) the encoding of the desired outputs. Backpropagation occasionally outperforms the other two systems when given relatively small amounts of training data. It is slightly more accurate than ID3 when examples are noisy or incompletely specified. Finally, backpropagation more effectively utilizes a distributed output encoding.
ML ID: 8
This paper presents an algorithm for first-order Horn-clause abduction that uses an ATMS to avoid redundant computation. This algorithm is either more efficient or more general than any other previous abduction algorithm. Since computing all minimal abductive explanations is intractable, we also present a heuristic version of the algorithm that uses beam search to compute a subset of the simplest explanations. We present empirical results on a broad range of abduction problems from text understanding, plan recognition, and device diagnosis which demonstrate that our algorithm is at least an order of magnitude faster than an alternative abduction algorithm that does not use an ATMS.
ML ID: 7
This paper presents an approach to improving the classification performance of a multiple category theory by correcting intermediate rules which are shared among the categories. Using this technique, the performance of a theory in one category can be improved through training in an entirely different category. Examples of the technique are presented and experimental results are given.
ML ID: 6
This paper presents constructive induction techniques recently added to the EITHER theory refinement system. These additions allow EITHER to handle arbitrary gaps at the ``top,'' ``middle,'' and/or ``bottom'' of an incomplete domain theory. Intermediate concept utilization employs existing rules in the theory to derive higher-level features for use in induction. Intermediate concept creation employs inverse resolution to introduce new intermediate concepts in order to fill gaps in a theory that span multiple levels. These revisions allow EITHER to make use of imperfect domain theories in the ways typical of previous work in both constructive induction and theory refinement. As a result, EITHER is able to handle a wider range of theory imperfections than does any other existing theory refinement system.
ML ID: 5
This paper presents a method for revising an approximate domain theory based on noisy data. The basic idea is to avoid making changes to the theory that account for only a small amount of data. This method is implemented in the EITHER propositional Horn-clause theory revision system. The paper presents empirical results on artificially corrupted data to show that this method successfully prevents over-fitting. In other words, when the data is noisy, performance on novel test data is considerably better than revising the theory to completely fit the data. When the data is not noisy, noise processing causes no significant degradation in performance. Finally, noise processing increases efficiency and decreases the complexity of the resulting theory.
ML ID: 4