# InfoCoBuild

## Artificial Intelligence

Artificial Intelligence. Instructor: Prof. P. Dasgupta, Department of Computer Science and Engineering, IIT Kharagpur. This course provides an introduction to artificial intelligence, covering topics: Problem Solving by Search, Searching with Costs, Heuristic Search: A* and Beyond, Searching Game Trees, Knowledge Based Systems: Logic and Deduction, First Order Logic, Inference in First Order Logic, Logic Programming: Prolog, Prolog: Exercising Control, GraphPLAN and SATPlan, Reasoning with Bayes Networks. (from nptel.ac.in)

 Lecture 18 - Planning Algorithms: Partial Order Planning

Go to the Course Home or watch other lectures:

 Lecture 01 - Introduction to Artificial Intelligence Lecture 02 - Problem Solving by Search: State Space Search Lecture 03 - Searching with Costs Lecture 04 - Informed State Space Search: The Notion of Heuristics, Algorithm A* Lecture 05 - Heuristic Search: A* and Beyond Lecture 06 - Problem Reduction Search: AND/OR Graphs Lecture 07 - Searching Game Trees: Shallow and Deep Pruning, Alpha-Beta Pruning Lecture 08 - Knowledge Based Systems: Logic and Deduction Lecture 09 - First Order Logic Lecture 10 - Inference in First Order Logic Lecture 11 - Resolution-Refutation Proofs Lecture 12 - Resolution Refutation Proofs Lecture 13 - Logic Programming: Prolog Lecture 14 - Prolog Programming Lecture 15 - Prolog: Exercising Control Lecture 16 - Additional Topics: Constraint Logic Programming, Iterative Refinement Search, Memory Bounded Search, Multi-Objective Search Lecture 17 - Introduction to Planning Lecture 18 - Planning Algorithms: Partial Order Planning Lecture 19 - Planning Algorithms: GraphPLAN and SATPlan Lecture 20 - Planning Algorithms: SATPlan Lecture 21 - Reasoning under Uncertainty Lecture 22 - Bayesian Networks Lecture 23 - Reasoning with Bayes Networks Lecture 24 - Reasoning with Bayes Networks (cont.) Lecture 25 - Reasoning under Uncertainty: Issues and Other Approaches Lecture 26 - Learning: Decision Trees Lecture 27 - Learning: Neural Networks Lecture 28 - Back Propagation Learning