Artificial Intelligence & Expert system

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Artificial Intelligence: Introduction, AI Paradigms and Hypothesis, Intelligent Agents. Difference between Cybernetic Intelligence and Artificial Intelligence, Objectives and Scope of Weak AI and Strong AI, Problem-solving: Solving Problems by Searching, Informed Search and Exploration, Constraint Satisfaction Problems, Adversarial Search. Knowledge and reasoning: Logical Agents, First-Order Logic, Inference in First-Order Logic, Knowledge Representation. Planning and Acting in the Real World. Uncertain knowledge and reasoning: Uncertainty, Probabilistic Reasoning, Probabilistic Reasoning over Time, Making Simple Decisions, Making Complex Decisions. Learning: Learning from Observations, Knowledge in Learning; Learning Methods, Reinforcement Learning. Communicating, perceiving, and acting: Communication, Probabilistic Language Processing, Perception and Robotics. Introduction to LISP/PROLOG and Expert Systems (ES) and Applications; Artificial General Intelligence, Issues in Safe AI, Introduction to Cognitive and Conscious Systems. Introduction of expert systems, Review of knowledge representation, Review of inference techniques, Study of logic, rule-based expert systems, Review of course expert system development software, Demonstration of a rulebased expert system, Workshop: Building a small rule-based expert system, Advance expert system programming techniques, Review of typical programming errors, Review of MYCIN, Overview of inexact reasoning, Study of inexact classification, intelligent database management, intelligent distributed problem solving.

Course Syllabus