Artificial Intelligence And Computing Logic
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Logic-Based Artificial Intelligence
Author: Jack Minker
language: en
Publisher: Springer Science & Business Media
Release Date: 2000-12-31
The use of mathematical logic as a formalism for artificial intelligence was recognized by John McCarthy in 1959 in his paper on Programs with Common Sense. In a series of papers in the 1960's he expanded upon these ideas and continues to do so to this date. It is now 41 years since the idea of using a formal mechanism for AI arose. It is therefore appropriate to consider some of the research, applications and implementations that have resulted from this idea. In early 1995 John McCarthy suggested to me that we have a workshop on Logic-Based Artificial Intelligence (LBAI). In June 1999, the Workshop on Logic-Based Artificial Intelligence was held as a consequence of McCarthy's suggestion. The workshop came about with the support of Ephraim Glinert of the National Science Foundation (IIS-9S2013S), the American Association for Artificial Intelligence who provided support for graduate students to attend, and Joseph JaJa, Director of the University of Maryland Institute for Advanced Computer Studies who provided both manpower and financial support, and the Department of Computer Science. We are grateful for their support. This book consists of refereed papers based on presentations made at the Workshop. Not all of the Workshop participants were able to contribute papers for the book. The common theme of papers at the workshop and in this book is the use of logic as a formalism to solve problems in AI.
Artificial Intelligence and Computing Logic
Focusing on the cutting-edge applications of AI cognitive computing from neuromorphic to quantum cognition as applied to AI business analytics, this new volume explores AI’s importance in managing cognitive processes along with ontological modeling concepts for venturing into new business frontiers. The volume presents a selection of significant new accomplishments in the areas of AI cognitive computing ranging from neurocognition perception and decision-making in the human brain—combining neurocognitive techniques and effective computing—to basic facial recognition computing models. Topics include: Agent neurocomputing techniques for facial expression recognition Computing haptic motion and ontology epistemic Characterizations of morph schemas for visual analytics Learning and perceptive computing Functional and structural neuroimaging modeling Observed links between facial recognition and affective emotional processes Interaction of cognitive and emotional processes during social decision-making Neurocognitive processing of emotional facial expressions in individuals Neurocognitive affective system for emotive robot androids Virtual reality-based affect adaptive neuromorphic computing Executive surveys indicate that cognitive adoption is very important in business strategy for success and to remain competitive. Employing cognitive-based processes provides the way to get the right information in the right hands at the right time, which is the key to winning in the digital era and to driving business value that emphasizes competitive differentiation. Several chapters of the volume address the goal of using cognitive technology to improve search capabilities, to provide personalized customer service in business and in health and wellness, and to create better workflow management. Key features: Looks at the newest frontiers on very popular AI and analytics topics Discusses new techniques for visual analytics and data filtering Shows how AI and cognitive science merges with quantum neurocognitive computing Presents ontology models with ontology preservation data filtering techniques Provides a cross-transposition on AI and digitizations for business model innovations Artificial Intelligence and Computing Logic: Cognitive Technology for AI Business Analytics is a valuable resource that informs businesses and other enterprises the value of artificial intelligence and computing logic applications.
Logic for Artificial Intelligence and Information Technology
Logic for Artificial Intelligence and Information Technology is based on student notes used to teach logic to second year undergraduates and Artificial Intelligence to graduate students at the University of London since1984, first at Imperial College and later at King's College. Logic has been applied to a wide variety of subjects such as theoretical computer science, software engineering, hardware design, logic programming, computational linguistics and artificial intelligence. In this way it has served to stimulate the research for clear conceptual foundations. Over the past 20 years many extensions of classical logic such as temporal, modal, relevance, fuzzy, probabilistic and non-monotoinic logics have been widely used in computer science and artificial intelligence, therefore requiring new formulations of classical logic, which can be modified to yield the effect of the new applied logics. The text introduces classical logic in a goal directed way which can easily deviate into discussing other applied logics. It defines the many types of logics and differences between them. Dov Gabbay, FRSC, FAvH, FRSA, FBCS, is Augustus De Morgan Professor of Logic at the University of London. He has written over 300 papers in logic and over 20 books. He is Editor-in-Chief of several leading journals and has published over 50 handbooks of logic volumes. He is a world authority on applied logics and is one of the directors and founder of the UK charity the International Federation of Computational Logic