Commonsense Reasoning: Overviews

Logic and Artificial Intelligence (Stanford Encyclopedia of Philosophy)

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Artificial Intelligence (which I'll refer to hereafter by its nickname, "AI") is the subfield of Computer Science devoted to developing programs that enable computers to display behavior that can (broadly) be characterized as intelligent.[1] Most research in AI is devoted to fairly narrow applications, such as planning or speech-to-speech translation in limited, well defined task domains. But substantial interest remains in the long-range goal of building generally intelligent, autonomous agents,[2] even if the goal of fully human-like intelligence is elusive and is seldom pursued explicitly and as such. Throughout its relatively short history, AI has been heavily influenced by logical ideas. AI has drawn on many research methodologies: the value and relative importance of logical formalisms is questioned by some leading practitioners, and has been debated in the literature from time to time.[3]

Cyc and the Big C: Reading that Produces and Uses Hypotheses about Complex Molecular Biology Mechanisms

AAAI Conferences

Systems biology, the study of the intricate, ramified, com-plex and interacting mechanisms underlying life, often proves too complex for unaided human understanding, even by groups of people working together. This difficulty is ex-acerbated by the high volume of publications in molecular biology. The Big C (‘C’ for Cyc) is a system designed to (semi-)automatically acquire, integrate, and use complex mechanism models, specifically related to cancer biology, via automated reading and a hyper-detailed refinement pro-cess resting on Cyc’s logical representations and powerful inference mechanisms. We aim to assist cancer research and treatment by achieving elements of biologist-level reason-ing, but with the scale and attention to detail that only com-puter implementations can provide.

Bridging Common Sense Knowledge Bases with Analogy by Graph Similarity

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Present-day programs are brittle as computers are notoriously lacking in common sense. While significant progress has been made in building large common sense knowledge bases, they are intrinsically incomplete and inconsistent. This paper presents a novel approach to bridging the gaps between multiple knowledge bases, making it possible to answer queries based on knowledge collected from multiple sources without a common ontology. New assertions are found by computing graph similarity with principle component analysis to draw analogies across multiple knowledge bases. Experiments are designed to find new assertions for a Chinese commonsense knowledge base using the OMCS ConceptNet and similarly for WordNet. The assertions are voted by online users to verify that 75.77% / 77.59% for Chinese ConceptNet / WordNet respectively are good, despite the low overlap in coverage among the knowledge bases.

developed by de Kleer [l&Z]. has provided a framework for most subsequent research on causal reasoning. A physical svstern is described by a

AAAI Conferences

ABSTRACT The central component of commonsense reasoning about causdity is the envisionment: a description of the behavior of a phvsical system that is derived from its structural description by qualitative simulation. Two problems with creating the envisionmcnt are the qualitative representation of quentlty and the detection of previously-unsuspcctcd points ot qualitative change. The representation presetlted here has the expressive power of differenil;ll equations, and the qualitarive envisionment strategy needed ior commonsense knowledge. A detailed example shows IICW it is able to detect a previously unsuspected point at which the system is in stable equilibrium. THE ENVISIONblENT Causal reasoning --- the ability to reason about how things work ___ is central to expert performance at problem-solving and expinnatii:n in many different areas.

Representations of Commonsense Knowledge


A full book, available for free in PDF form.From the preface:A major problem in artificial intelligence is to endow computers with commonsense knowledge of the world and with the ability to use that knowledge sensibly. A large body of research has studied this problem through careful analysis of typical examples of reasoning in a variety of commonsense domains. The immediate aim of this research is to develop a rich language for expressing commonsense knowledge, and inference techniques for carrying out commonsense reasoning. This book provides an introduction and a survey of this body of research. It is, to the best of my knowledge, the first book to attempt this.The book is designed to be used as a textbook for a one-semester graduate course on knowledge representation.Morgan Kaufmann