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Artificial Intelligence Tarik Booker What we will cover… History Artificial Intelligence as Representation and Search Languages used in Artificial Intelligence Applications History of Artificial Intelligence Derives from Logic Aristotle Charles Babbage George Boole Alan Turing Turing Test AI as Representation and Search Predicate Calculus State Space Heuristic Search Predicate Calculus Covered later in presentation (Logic Programming) Basics: Proposition – statement that may or may not be true State Space The structure of the state that you are in A four-tuple [N, A, S, GD] Where: N is the set of nodes (or states) of the graph A is the set of arcs (links) between nodes S, a non-empty subset of N, contains the start state(s) of the problem GD, a non-empty subset of N contains the goal state(s) of the problem A solution path is a path through this graph from a node S to a node in GD Heuristics (From Greek “eurisco” meaning “to discover”) A strategy for selectively searching a problem space Searches along lines that have a high probability of success Not guaranteed to find correct solution Why use Heuristics? Problem may not have an exact solution because of ambiguities Ex: Medical Diagnosis Problem may have exact solution, but the computational cost of finding it may be prohibitive Ex: Chess Heuristics are at the core of AI. Heuristic Algorithms Heuristic Measure Best-first Search Tic-Tac Toe (on board) Heuristics Terms Admissibility Heuristics that find the shortest path to a goal whenever it exists are said to be admissible Informedness Are any heuristics better that the one we are using? Monotonicity When a state is discovered using heuristic search, is there a guarantee that the same state won’t be reached with a cheaper cost? Languages Used in AI LISP PROLOG Applications of AI Game Playing Heuristics Automated Reasoning and Theorem Proving Expert Systems Natural Language Understanding Planning and Robotics Machine Learning Sources Luger, George F. Stubblefield, William A. Artificial Intelligence (3rd Edition)