Artificial Intelligence For MU Semester 5 (Data Science) AIDS AIML Data Engineering & 6 Electronics And Computer Science & 7 Electronics Engineering :Information Technology (Code :: ITDO8013) Academic Year 2022-2023  (Paperback, Prof. R. M. Baphana)

1 Introduction to Artificial Intelligence 3 1.1 Artificial Intelligence (AI), AI Perspectives : Acting and Thinking humanly, Acting and Thinking rationally. 1.2 History of AI, Applications of AI, The present state of AI, Ethics in AI. (Refer Chapter 1) 2 Intelligent Agents 4 2.1 Introduction of agents, Structure of Intelligent Agent, Characteristics of Intelligent Agents. 2.2 Types of Agents : Simple Reflex, Model Based, Goal Based, Utility Based Agents. 2.3 Environment Types : Deterministic, Stochastic, Static, Dynamic, Observable, Semi-observable, Single Agent, Multi Agent. (Refer Chapter 2) 3 Solving Problems by Searching 12 3.1 Definition, State space representation, Problem as a state space search, Problem formulation, Well-defined problems. 3.2 Solving Problems by Searching, Performance evaluation of search strategies, Time Complexity, Space Complexity, Completeness, Optimality. 3.3 Uninformed Search : Depth First Search, Breadth First Search, Depth Limited Search, Iterative Deepening Search, Uniform Cost Search, Bidirectional Search 3.4 Informed Search : Heuristic Function, Admissible Heuristic, Informed Search Technique, Greedy Best First Search, A* Search, Local Search : Hill Climbing Search, Simulated Annealing Search, Optimization : Genetic Algorithm. 3.5 Game Playing, Adversarial Search Techniques, Mini-max Search, Alpha-Beta Pruning. (Refer Chapter 3) 4 Knowledge and Reasoning 10 4.1 Definition and importance of Knowledge, Issues in Knowledge Representation, Knowledge Representation Systems, Properties of Knowledge Representation Systems. 4.2 Propositional Logic (PL) : Syntax, Semantics, Formal logic-connectives, truth tables, tautology, validity, well-formed-formula, Introduction to logic programming (PROLOG). 4.3 Predicate Logic : FOPL, Syntax, Semantics, Quantification, Inference rules in FOPL. 4.4 Forward Chaining, Backward Chaining and Resolution in FOPL. (Refer Chapter 4) 5 Reasoning Under Uncertainty 5 5.1 Handling Uncertain Knowledge, Random Variables, Prior and Posterior Probability, Inference using Full Joint Distribution. 5.2 Bayes' Rule and its use, Bayesian Belief Networks, Reasoning in Belief Networks. (Refer Chapter 5) 6 Planning and Learning 5 6.1 The planning problem, Partial order planning, total order planning. 6.2 Learning in AI, Learning Agent, Concepts of Supervised, Unsupervised, Semi-Supervised Learning, Reinforcement Learning, Ensemble Learning. 6.3 Expert Systems, Components of Expert System : Knowledge base, Inference engine, user interface, working memory, Development of Expert Systems. (Refer Chapter 6) Total 39