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Indian Institute of Technology Madras

Artificial Intelligence Search Methods For Problem Solving

Indian Institute of Technology Madras and NPTEL via Swayam


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.In a follow up course – AI: Knowledge Representation and Reasoning – we will go into the details of how an agent can represent its world and reason with what it knows. These two courses should lay a strong foundation for artificial intelligence, which the student can build upon. A third short course – AI: Constraint Satisfaction Problems – presents a slightly different formalism for problem solving, one in which the search and reasoning processes mentioned above can operate together.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.


Week 0 : Introduction: History, Can Machines think?, Turing Test, Winograd Schema Challenge, Language and Thought, Wheels & Gears
Week 1 : Introduction: Philosophy, Mind, Reasoning, Computation, Dartmouth Conference, The Chess Saga, Epiphenomena
Week 2 : State Space Search: Depth First Search, Breadth First Search, Depth First Iterative Deepening
Week 3 : Heuristic Search: Best First Search, Hill Climbing, Solution Space, TSP, Escaping Local Optima, Stochastic Local Search
Week 4 : Population Based Methods: Genetic Algorithms, SAT, TSP, emergent Systems, Ant Colony Optimization
Week 5 : Finding Optimal Paths: Branch & Bound, A*, Admissibility of A*, Informed Heuristic Functions
Week 6 : Space Saving Versions of A*: Weighted A*, IDA*, RBFS, Monotone Condition, Sequence Alignment, DCFS, SMGS, Beam Stack Search
Week 7 : Game Playing: Game Theory, Board Games and Game Trees, Algorithm Minimax, AlphaBeta and SSS*
Week 8 : Automated Planning: Domain Independent Planning, Blocks World, Forward &Backward Search, Goal Stack Planning, Plan Space Planning
Week 9 : Problem Decomposition: Means Ends Analysis, Algorithm Graphplan, Algorithm AO*
Week 10 : Rule Based Expert Systems: Production Systems, Inference Engine, Match-Resolve-Execute, Rete Net
Week 11 : Deduction as Search: Logic, Soundness, Completeness, First Order Logic, Forward Chaining, Backward Chaining
Week 12 : Constraint Processing: CSPs, Consistency Based Diagnosis, Algorithm Backtracking, Arc Consistency, Algorithm Forward Checking

Taught by

Deepak Khemani


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