Methods and Tools for Applied Artificial Intelligence
This work provides a comprehensive and coherent introduction to the expanding field of Artificial Intelligence (Al), explaining how knowledge-based systems are built, what tools and technologies are relevant and available, and how to employ them in specific situations. It pays special attention to the commercial intelligence systems that emerged in the '80s, as well as projecting the likely developments of the '90s.
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actions algorithm analysis and/or applied approach Artificial lntelligence attributes automated backtracking basic behaviour belief building certainty factors classification clauses clustering complex concept learning conceptual clustering connectionist constraints database decision defined Dempster-Shafer theory described domain expert domain knowledge efficient evaluation examples expert system shell expert systems facts formal frame function fuzzy set given goal heuristic hierarchical implementation inference input instance intelligent interface knowledge acquisition knowledge base knowledge engineer knowledge representation knowledge-based systems LlSP ln addition ln order logic Machine Learning matching metaknowledge methods Michalski natural language nodes objects operators optimal output paradigm parallel pattern perceptron planning possible predicate predicate logic problem solving Proc procedural production rules production systems programming languages PROLOG proposition reasoning repertory grid representation schemes represented robot rule-based selection semantic semantic networks slots Smalltalk solution space specific stored strategy structure technique theorem theory values variables