Expert Systems Components Knowledge Base and Working Memory

Lecture 24 of CS607 Artificial Intelligence provides a detailed architectural analysis of the internal mechanics of Rule-Based Expert Systems (Production Systems). The Knowledge Base contains domain-specific knowledge represented as a collection of Production Rules formatted as IF (antecedent / condition) THEN (consequent / action). The Working Memory (or fact base) maintains the current state of the world, storing transient facts asserted by the user or derived by the inference engine during a problem-solving session. Students study the fundamental Recognize-Act Cycle: the inference engine matches working memory facts against rule antecedents, adds all matching rules to an Agenda (the conflict set), selects one rule using conflict resolution, fires the rule executing its action (which asserts, retracts, or modifies working memory facts), and repeats the cycle until no further rules match. The Explanation Facility, which answers 'HOW' conclusions were reached and 'WHY' specific user questions are asked, is analyzed. Alpha-beta pruning mathematical cutoffs are analyzed, proving that identical game decisions are reached with significantly fewer evaluated board states. By engaging with solved past papers by Moaaz and Waqar Siddhu, learners gain actionable insights into recurrent university exam questions. Interactive quiz assessments on VU SIGMA facilitate rapid knowledge verification and retention of critical artificial intelligence terminology. Comprehensive coverage of IEEE and AAAI standards ensures learners communicate with technical precision across global computational intelligence organizations. Detailed analysis of semantic networks and frame systems illustrates how hierarchical inheritance models capture structured real-world domain knowledge. The strategic role of expert system shells is evaluated as an instrument for decoupling domain knowledge bases from generic inference engines. Certainty factor calculi and probabilistic reasoning provide systematic mechanisms for managing incomplete and noisy real-world evidentiary data. The culminating synthesis highlights emerging frontiers in autonomous systems, deep learning architectures, and ethical artificial intelligence governance. In this lecture, students examine fundamental artificial intelligence concepts, rational agent architectures, and computational problem-solving paradigms. Through detailed state-space graphs and algorithmic walkthroughs, the lesson explores search completeness, time complexity, and heuristic evaluation metrics.