Artificial Intelligence : Search Methods For Problem solving

Indian Institute of Technology Madras and NPTEL via Swayam

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Overview

For an autonomous agent to behave in an intelligent manner it must be able to solve problems. This means it should be able to arrive at decisions that transform a given situation into a desired or goal situation. The agent should be able to imagine the consequence of its decisions to be able to identify the ones that work. In this first course on AI we study a wide variety of search methods that agents can employ for problem solving....

Syllabus

COURSE LAYOUT Week 1: Introduction: Overview and Historical Perspective, Turing Test, Physical Symbol Systems and the scope of Symbolic AI, Agents.Week 2: State Space Search: Depth First Search, Breadth First Search, DFIDWeek 3: Heuristic Search: Best First Search, Hill Climbing, Beam SearchWeek 4: Traveling Salesman Problem, Tabu Search, Simulated AnnealingWeek 5: Population Based Search: Genetic Algorithms, Ant Colony OptimizationWeek 6: Branch & Bound, Algorithm A*, Admissibility of A*Week 7: Monotone Condition, IDA*, RBFS, Pruning OPEN and CLOSED in A*Week 8: Problem Decomposition, Algorithm AO*, Game PlayingWeek 9: Game Playing: Algorithms Minimax, AlphaBeta, SSS*Week 10: Rule Based Expert Systems, Inference Engine, Rete AlgorithmWeek 11: Planning: Forward/Backward Search, Goal Stack Planning, Sussmans AnomalyWeek 12: Plan Space Planning, Algorithm GraphplanThe following topics are not part of evaluation for this course, and are included for the interested student. These topics will be covered in detail in two followup courses "AI: Knowledge Representation and Reasoning" and "AI: Constraint Satisfaction Problems".A1 Constraint Satisfaction Problems, Algorithm AC-1, Knowledge Based SystemsA2 Propositional Logic, Resolution Refutation MethodA3 Reasoning in First Order Logic, Backward Chaining, Resolution Meth