PPT-A Program Transformation For Faster Goal-Directed Search

Akash Lal Shaz Qadeer Microsoft Research Optimizations In the context of compilers an optimization is A program transformation that preserves semantics Aimed at improving the execution time of the program. Adjacency Lists. A: F G. B: A H. C: A D. D: C F. E: C D G. F: E. :. G: . :. H: B. :. I: H. :. F. A. B. C. G. D. E. H. I. 2. Directed Depth First Search. F. A. B. C. G. D. E. H. I. dfs(A). A-F A-G. This Lecture. Read Chapter 3.1-3.4. Next Lecture. Read Chapter 3.5-3.7. (Please read lecture topic material before and after each lecture on that topic). You will be expected to know. Overview of uninformed search methods. Building Goal-Based Agents. 2. We have a . goal. to reach. Driving from point A to point B. Put 8 queens on a chess board such that no one attacks another. Prove that John is an ancestor of Mary. We have information about where we are now at the . Andrew Steele. twitter: . ahsteele. blog: http://steelebit.com. andrew.steele@sandia.gov. Sandia National Laboratories is a multi-program laboratory operated by Sandia Corporation, a wholly owned . subsidiary of Lockheed Martin company, for the U.S. Department of Energy’s National Nuclear Security Administration . Akash Lal, Shaz Qadeer. Microsoft Research. Optimizations. In the context of compilers, an optimization is:. A program transformation that preserves semantics. Aimed at improving the execution time of the program. Tamara Berg. CS 560 Artificial Intelligence. Many slides throughout the course adapted from Dan Klein, Stuart Russell, Andrew Moore, Svetlana . Lazebnik. , Percy Liang, Luke . Zettlemoyer. Course Information. Lessons Learned from Experienced Practices. Aimee Ducharme, Family Care Southwest. Dr. Kelley . Glancey. , Byers Peak Family Medicine. Beth King, Associates in Family Medicine. Presenters. Facilitator - Emilie Buscaj, HealthTeamWorks. Felner. .. Ben-Gurion University of The Negev. Department of Information Systems Engineering. Israel. The . increasing cost tree search . for . optimal . multi-agent . pathfinding. 1. Background. In . Many slides throughout the course adapted from Dan Klein, Stuart Russell, Andrew Moore, Svetlana . Lazebnik. , Percy Liang, Luke . Zettlemoyer. Course Information. Instructor: Tamara Berg (. tlberg@cs.unc.edu. Models To Be Studied in CS 540. State-based Models. Model task as a graph of all possible states. Called a “. state-space graph. ”. A state captures all the relevant information about the past in order to act (optimally) in the future. Winter 2018. Introduction to Artificial Intelligence. Prof. Richard Lathrop. Reading: R&N 3.1-3.4. Uninformed search strategies. Uninformed (blind):. You have no clue whether one non-goal state is better than any other. Your search is blind. You don’t know if your current exploration is likely to be fruitful.. http://cs.indiana.edu/~hauserk. 1. Agenda. Local search, optimization. Branch and bound search. Online . search. Local Search. Light-memory search methods . No search tree; only the current state is represented!. Prof. O. . Nierstrasz. © Oscar Nierstrasz. Program Transformation. Roadmap. Program Transformation. Refactoring. Aspect-Oriented Programming. Outlook. 2. Links. Program Transformation:. http://swerl.tudelft.nl/bin/view/Pt. Some material adopted from notes by Charles R. Dyer, University of Wisconsin-Madison. Today’. s topics. Goal-based agents. Representing states and actions. Example problems. Generic state-space search algorithm.