Constructive Induction-based Learning Agents: An Architecture and Preliminary Experiments

This paper introduces a new type of intelligent agent called a constructive induction-based learning agent (CILA). This agent differs from other adaptive agents because it has the ability to not only learn how to assist a user in some task, but also to incrementally adapt its knowledge representation space to better fit the given learning task. The agent's ability to autonomously make problem-oriented modifications to the originally given representation space is due to its constructive induction (CI) learning method. Selective induction (SI) learning methods, and agents based on these methods, rely on a good representation space. A good representation space has no misclassification noise, inter-correlated attributes or irrelevant attributes. Our proposed CILA has methods for overcoming all of these problems. In agent domains with poor representations, the CIbased learning agent will learn more accurate rules and be more useful than an SI-based learning agent. This paper gives an archit..

Learning Hybrid Concept Descriptions

Most symbolic learning methods are concerned with learning concept descriptions in the form of a decision tree or a set of rules expressed in terms of the originally given attributes. For some practical problems, these methods are inadequate because they cannot learn conditions that require counting of some object properties. Such problems occur, for example, in engineering, economy, medicine and software engineering. This paper describes a method for learning hybrid descriptions that combine logic-type and arithmetic-type properties. The presented method builds hybrid descriptions in the form of conditional counting rules, which are logic-type (DNF) expressions with counting conditions (expressing a relationship involving a count of some object properties). The method employs a constructive induction approach in which the learning system performs two intertwined searches: one—for the most appropriate knowledge representation space, and second—for the "best " hypothesis in the space. The first search is done by determining maximum symmetry classes of binary attributes in the initial DNF-type hypotheses, and extending the initial representation space by counting attributes that correspond to these symmetry classes. The search for the "best" hypothesis in so extended representation space is done by a standard AQ inductive rule learning program. It our experiments, the proposed method learned simple and accurate concept descriptions when conventional learning methods failed.