1 / 13

Artificial Intelligence

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

jorryn
Download Presentation

Artificial Intelligence

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Artificial Intelligence Tarik Booker

  2. What we will cover… • History • Artificial Intelligence as Representation and Search • Languages used in Artificial Intelligence • Applications

  3. History of Artificial Intelligence • Derives from Logic • Aristotle • Charles Babbage • George Boole • Alan Turing • Turing Test

  4. AI as Representation and Search • Predicate Calculus • State Space • Heuristic Search

  5. Predicate Calculus • Covered later in presentation (Logic Programming) • Basics: • Proposition – statement that may or may not be true

  6. 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

  7. 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

  8. 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.

  9. Heuristic Algorithms • Heuristic Measure • Best-first Search • Tic-Tac Toe (on board)

  10. 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?

  11. Languages Used in AI • LISP • PROLOG

  12. Applications of AI • Game Playing • Heuristics • Automated Reasoning and Theorem Proving • Expert Systems • Natural Language Understanding • Planning and Robotics • Machine Learning

  13. Sources Luger, George F. Stubblefield, William A. Artificial Intelligence (3rd Edition)

More Related