Machine learning methods for commonsense reasoning processes; interactive models. by Naidenova, Xenia - 2010

Information Science Reference, 2010. Hardbound. New Book. Hardbound. This book demonstrates the possibility of transforming machine learning algorithms into integrated commonsense reasoning processes in which inductive and deductive inferences correlate and support one another. Methodological approaches to the organization of data and knowledge in intelligent computer systems are discussed. Examples are given of expert system construction via machine learning mechanisms. Early chapters cover logic-based reasoning in the framework of artificial intelligence, the coordination of commonsense reasoning operations, the logical rules of commonsense reasoning, and human commonsense reasoning processes. Later chapters describe an integrative model of deductive-inductive commonsense reasoning, a model of fuzzy commonsense reasoning, object-oriented technology for expert system generation, and case technology for psycho-diagnostic system generation. The book is for logicians and mathematicians working with the theory of classification and models of logical inference. It will also interest specialists in artificial intelligence, machine learning algorithms, and knowledge engineering. Naidenova is the head of Project DIALOG: Methods of Data Mining in Psychological and Physiological Diagnostics, at the Military Medical Academy, St. Petersburg, Russia. (2010 Ringgold, Inc., Portland, OR)