Rule-Based Systems in Knowledge Representation: How Explicit Rules Drive AI Decisions

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This chapter from the STATERA Library's Knowledge Representation volume introduces rule-based systems as a foundational approach to artificial intelligence. Such systems make decisions by applying explicitly defined rules to known facts rather than relying solely on statistical learning from data. A key design feature is the separation of the knowledge base, which holds domain rules, from the inference engine, which determines how those rules are applied. The text describes forward and backward chaining as two core reasoning strategies, suited to different tasks such as monitoring, diagnosis, and goal-directed problem solving. It also places rule-based systems within the broader tradition of symbolic artificial intelligence.

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This chapter from the STATERA Library's Volume 02 on Knowledge Representation presents an integrated project for building a logic inference engine. The engine derives new knowledge from explicitly stated facts and logical rules, aiming to turn formal reasoning principles into executable operations for symbolic AI. Its architecture comprises a knowledge base, a logical expression parser, an inference controller, working memory, and a query interface, with an emphasis on correctness, transparency, modularity, and extensibility. The material covers forward and backward chaining, unification, and resolution-based refutation as core reasoning mechanisms.

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