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A novel approach to hybrid AI aimed at developing trustworthy agent collaborators. The vast majority of current AI relies wholly on machine learning (ML). However, the past thirty years of effort in this paradigm have shown that, despite the many things that ML can achieve, it is not an all-purpose solution to building human-like intelligent systems. One hope for overcoming this limitation is hybrid AI: that is, AI that combines ML with knowledge-based processing. In Agents in the Long Game of AI, Marjorie McShane, Sergei Nirenburg, and Jesse English present recent advances in hybrid AI with special emphases on content-centric computational cognitive modeling, explainability, and development...
Ellipsis is the non-expression of one or more sentence elements whose meaning can be reconstructed either from the context or from a person's knowledge of the world. In speech and writing, ellipsis is pervasive, contributing in various ways to the economy, speed, and style of communication. Resolving ellipsis is a particularly challenging issue in natural language processing, since not only must meaning be gleaned from missing elements but the fact that something meaningful is missing must be detected in the first place. Marjorie McShane presents a comprehensive theory of ellipsis that supports the formal, cross-linguistic description of elliptical phenomena taking into account the various f...
A human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems. One of the original goals of artificial intelligence research was to endow intelligent agents with human-level natural language capabilities. Recent AI research, however, has focused on applying statistical and machine learning approaches to big data rather than attempting to model what people do and how they do it. In this book, Marjorie McShane and Sergei Nirenburg return to the original goal of recreating human-level intelligence in a machine. They present a human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems that emphasizes meaning--the deep, context-sensitive meaning that a person derives from spoken or written language.
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