Artificial Intelligence MIT

 Technologie
In these lectures, Prof. Patrick Winston introduces the 6.034 material from a conceptual, bigpicture perspective. Topics include reasoning, search, constraints, learning, representations, architectures, and probabilistic inference. In these megarecitations, teaching assistant Mark Seifter works through problems from previous exams in a lecturestyle setting. Students are asked to participate, and emphasis is placed on being able to work the algorithms by hand.

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Lecture 1: Introduction and Scope
In this lecture, Prof. Winston introduces artificial intelligence and provides a brief history of the field. The last ten minutes are devoted to information about the course at MIT.

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Lecture 2: Reasoning: Goal Trees and Problem Solving
This lecture covers a symbolic integration program from the early days of AI. We use safe and heuristic transformations to simplify the problem, and then consider broader questions of how much knowledge is involved, and how the knowledge is represented.

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Lecture 3: Reasoning: Goal Trees and RuleBased Expert Systems
We consider a blockstacking program, which can answer questions about its own behavior, and then identify an animal given a list of its characteristics. Finally, we discuss how to extract knowledge from an expert, using the example of bagging groceries.

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Lecture 4: Search: DepthFirst, Hill Climbing, Beam
This lecture covers algorithms for depthfirst and breadthfirst search, followed by several refinements: keeping track of nodes already considered, hill climbing, and beam search. We end with a brief discussion of commonsense vs. reflective knowledge.

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Lecture 5: Search: Optimal, Branch and Bound, A*
This lecture covers strategies for finding the shortest path. We discuss branch and bound, which can be refined by using an extended list or an admissible heuristic, or both (known as A*). We end with an example where the heuristic must be consistent.

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Lecture 6: Search: Games, Minimax, and AlphaBeta
In this lecture, we consider strategies for adversarial games such as chess. We discuss the minimax algorithm, and how alphabeta pruning improves its efficiency. We then examine progressive deepening, which ensures that some answer is always available.