Artificial Intelligence III
IIT Madras, , Prof. Deepak Khemani
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Updated On 02 Feb, 19
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.
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Artificial Intelligence by Prof. Deepak Khemani,Department of Computer Science and Engineering,IIT Madras.For more details on NPTEL visit httpnptel.ac.in
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 Method
Sep 12, 2018
Excellent course helped me understand topic that i couldn't while attendinfg my college.
March 29, 2019
Great course. Thank you very much.