Sowa, John F.

Abstract: Abstract. No human being can understand every text or dialog in his or her native language, and no one should expect a computer to do so. However, people have a remarkable ability to learn and to extend their understanding without explicit training. Fundamental to human understanding is the ability to learn and use language in social interactions that Wittgenstein called language games. Those language games use and extend prelinguistic knowledge learned through perception, action, and social interactions. This article surveys the technology developed for natural language processing and the successes and failures of various attempts. Although many useful applications have been implemented, the original goal of language understanding seems as remote as ever. Fundamental to understanding is the ability to recognize an utterance as a move in a social game and to respond in terms of a mental model of the game, the players, and the environment. Those models use and extend the prelinguistic models learned through perception, action, and social interactions. Secondary uses of language, such as reading a book, are derivative processes that elaborate and extend the mental models originally acquired by interacting with people and the environment. A computer system that relates language to virtual models might mimic some aspects of understanding, but full understanding requires the ability to learn and use new knowledge in social and sensory-motor interactions. These issues are illustrated with an analysis of some NLP systems and a recommended strategy for the future. None of the systems available today can understand language at the level of a child, but with a shift in strategy there is hope of designing more robust and usable systems in the future.

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Abstract: According to Heraclitus, panta rhei — everything is in flux. But what gives that flux its form is the logos — the words or signs that enable us to perceive patterns in the flux, remember them, talk about them, and take action upon them even while we ourselves are part of the flux we are acting in and on. Modern physics is essentially a theory of flux in which the ultimate building blocks of matter maintain some semblance of stability only because of conservation laws of energy, momentum, spin, charge, and more exotic notions like charm and strangeness. Meanwhile, the concepts of everyday life are derived from experience with objects and processes that are measured and classified by comparisons with the human body, its parts, and its typical movements. Yet despite the vast differences in sizes, speeds, and time scale, the languages and counting systems of our stone-age ancestors have been successfully adapted to describe, analyze, and predict the behavior of everything from subatomic particles to clusters of galaxies that span the universe. Any system of ontology that is adequate for defining the concepts used in natural languages must be at least as flexible as the languages themselves: it must be able to accommodate all the categories of thought that are humanly conceivable and relate them to all possible experiences, either directly by human senses or indirectly by whatever instrumentation any scientist or engineer may invent. As a foundation for such an ontology, this paper proposes the philosophies of three logicians who understood the limitations of logic in dealing with the both the flux and the logos: Charles Sanders Peirce, Alfred North Whitehead, and Ludwig Wittgenstein.

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Abstract: In very large knowledge bases, global consistency is almost impossible to achieve, yet local consistency is essential for deduction and problem solving. To preserve local consistency in an environment of global inconsistency, this article proposes a two-level structure: a large reservoir of loosely organized encyclopedic knowledge, called knowledge soup; and floating in the soup, much smaller, tightly organized theories that resemble the usual microworlds of AI. The two kinds of knowledge require two distinct kinds of reasoning: abduction uses associative search, measures of salience, and theory revision for finding chunks of knowledge in the soup and assembling them into consistent theories; and deduction uses theorem-proving techniques for reasoning within a theory. The resulting two-level system can attain the goals of nonmonotonic logic, while retaining the simplicity of classical logic; it can use statistics for dealing with uncertainty, while preserving the precision of logic in dealing with hard-edged facts; and it can relate logics with discrete symbols to models of continuous systems.

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