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Michael Lebowitz

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8 papers
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8

AAAI Conference 1986 Conference Paper

Not the Path to Perdition: The Utility of Similarity-Based Learning

  • Michael Lebowitz

A large portion of the research in machine learning has involved a paradigm of comparing many examples and analyzing them in terms of similarities and differences, assuming that the resulting generalizations will have applicability to new examples. While such research has been very successful, it is by no means obvious why similarity-based generalizations should be useful, since they may simply reflect coincidences. Proponents of explanation-based learning, a new, knowledge-intensive method of examining single examples to derive generalizations based on underlying causal models, could contend that their methods are more fundamentally grounded, and that there is no need to look for similarities across examples. In this paper, we present the issues, and then show why similarity-based methods are important. We present four reasons why robust machine learning must involve the integration of similarity-based and explanation-based methods. We argue that: 1) it may not always be practical or even possible to determine a causal explanation; 2) similarity usually implies causality; 3) similarity-based generalizations can be refined over time; 4) similarity-based and explanation-based methods complement each other in important ways.

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IJCAI Conference 1983 Conference Paper

Creating a Story-Telling Universe

  • Michael Lebowitz

Extended story generation, such as the creation of soap opera stories, is a difficult and interesting problem for Artificial Intelligence. We present here the first phase of the development of a program, UNIVERSE, to tell such stories. In particular, we introduce a method for creating universes of characters appropriate for extended story generation. This method concentrates on the need to keep story-telling universes consistent and coherent. We describe "the information that must be maintained for characters and interpersonal relations, and the use of stereotypical information about people to help motivate trait values.

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AAAI Conference 1983 Conference Paper

RESEARCHER: An Overview

  • Michael Lebowitz

Described in this paper is a computer system, RESEARCHER, beine: develoDed at Columbia that reads natural languagk text-in th(? form of patent abstracts and creates. a permanent long-term memory based on concepts generaliz+ from these texts, forming an intelligent mformatlon s stem. This pa er is intended to give an overview of RESEARCHER & e will describe briefly the four main areas dealt ’with in the desi n % of RESEARCHER: 1) knowledge representation w ere a canonical scheme for representing physical objects has been developed, generalization 2) memory-based text processing, 3) and generalization-based organization that treats conce part of understanding, a t formation as an E? ?g% an question answering. 4) generalization-based

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