Expert System Architecture: The structure of an expert system that typically includes a knowledge base and an inference engine, designed to simulate the decision-making ability of a human expert.
Knowledge Base: A repository of specialized facts and rules that represent the expertise required for problem-solving within the system.
Inference Engine: The component that applies logical rules to the knowledge base to deduce new information or make decisions, mimicking human reasoning.
Expert systems are composed of two main elements: a knowledge base and an inference engine. The knowledge base stores the expert's knowledge, while the inference engine processes this knowledge to simulate decision-making. Development of expert systems involves phases such as knowledge acquisition, system design, implementation, and testing. These systems are widely used in diagnostics, decision support, and troubleshooting across various industries. The key advantages of expert systems include consistency in decision-making and availability at all times. However, they face challenges like a lack of common sense and difficulties in acquiring and encoding expert knowledge.
1. How do the knowledge base and inference engine in expert systems architecture fundamentally differ from each other?
2. What is the primary purpose of analyzing language at different NLP levels such as phonological, morphological, lexical, syntactic, semantic, and pragmatic?
3. What is computer vision primarily concerned with?
Expert System Architecture — components?
Knowledge base and inference engine
Knowledge Base — role?
Stores expert’s facts and rules
Inference Engine — function?
Applies logic to deduce decisions
NLP analysis levels — order?
Phonological, morphological, lexical, syntactic, semantic, pragmatic
Robotics — main components?
Sensors, actuators, control systems
Computer Vision — key techniques?
Filtering, feature extraction, object detection
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