JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Course Objectives:

• To learn the difference between optimal reasoning Vs human like reasoning
• To understand the notions of state space representation, exhaustive search, heuristic search along with the time and space complexities.
• To learn different knowledge representation techniques
• To understand the applications of AI: namely game playing, theorem proving, expert systems, machine learning and natural language processing.

Course Outcomes:

• Possess the ability to formulate an efficient problem space for a problem expressed in English
• Possess the ability to select a search algorithm for a problem and characterize its time and space complexities.
• Possess the skill for representing knowledge using the appropriate technique.
• Possess the ability to apply AI techniques to solve problems of Game Playing, Expert Systems, Machine Learning and Natural Language Processing.

UNIT - I

Introduction: AI problems, foundation of AI and history of AI intelligent agents: Agents and environments, the concept of rationality, the nature of environments, structure of agents, problem solving agents, problem formulation. Searching: Searching for solutions, uniformed search strategies-breadth first search, depth first search, depth limited search, iterative deepening depth first search bi-direction search comparison. Search with partial information greedy best first search, A* search, Memory bounded heuristic search, Heuristic functions.

UNIT - II

Local search Algorithms, Hill climbing, simulated, annealing search, local beam search, genetical algorithms. Constrain satisfaction problems: Backtracking search for CSPs local search for constraint satisfaction problems. Game Playing: Adversial search, games, minimax, algorithm, optimal decisions in multiplayer games, Alpha-Beta pruning, Evaluation functions and cutting of search.

UNIT - III

Knowledge Representation & reasons logical agents, knowledge- Based agents, the wumpus world, logic, propositional logic, resolution patterns in proposional logic, resolution, forward & backward, chaining. First order logic. Inference in first order logic, propositional Vs first order inference, unification & lifts forward chaining, backward chaining, resolution.

UNIT - IV

Planing- Classical planning problem, Language of planning problems, expressiveness and extension, planning with state-space search, forward states spare search, backward states space search, Heuristics for stats space search, planning search, planning with state space search, partial order planning graphs.

R19 M.Tech. Mechatronics

UNIT - V

Learning-Forms of learning, induction leaning, learning decision tree, statistical learning methods, learning with complex data, learning with hidden variables- the EM algorithm. Instance based learning, neural networks.

TEXT BOOKS:

• 1. Artificial Intelligence- A Modern a[[roach, second edition, Stuart ressel, peter nerving, PHI/Pearson Education.
• 2. Artificial Intelligence, 3rd Edition, Patrick Henry Winston., Pearson Edition.

REFERENCE BOOKS:

• 1. Artificial Intelligence, 2nd Edition, E.Rich and K. Knight (TMH).
• 2. Artificial Intelligence and Expert Systems-Patterson PHI
• 3. Expert Systems: Principles and Programming-Fourth Edition, Giarrantana /Riley, Thomson
• 4. PROLOG Programming for Artificial Intelligence. Ivan Bratka-Third Edition-Pearson Education. R19 M.Tech. Mechatronics M. TECH. I Year I Sem. (MECHATRONICS)