Artificial Intelligence Search Methods For Problem Solving
Indian Institute of Technology Madras and NPTEL via Swayam
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Overview
INTENDED AUDIENCE: This is a first course on Artificial Intelligence. While the intended audience is both UG and PG students studying Computer Science, in fact anyone comfortable with talking about algorithms should be able to do the course.PRE-REQUISITES: NilINDUSTRY SUPPORT: Any industry that is involved in development of AI applications. This not only includes software companies (like Microsoft, Google, and Facebook) but also manufacturing companies like Ford and General Electric, and retail companies like Amazon and Flipkart.
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, Sussman’s 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 Method
Taught by
Prof.Deepak Khemani
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