Table of ContentsCompute-Intensive Methods in AI: New Opportunities for Reasoning and Search Bart Selman Cornell University selman@cs.cornell.edu Introduction Factors in Progress Applications: Methodology PPT Slide PPT Slide Outline I. Example Application: Planning PPT Slide Some Applications of Planning State-space Planning Abdundance of Negative Complexity Results Practice Progression Approach SATPLAN SAT Encodings Solution to a Planning Problem Satisfiability Testing Procedures Walksat Procedure Planning Benchmark Test Set Solution of Logistics Problems What SATPLAN Shows II. Current Themes in Sat Solvers SAT Solvers Background Preview of Strategy Cost Distributions PPT Slide PPT Slide PPT Slide Heavy-Tailed Distributions Decay of Distributions PPT Slide How to Check for “Heavy Tails”? PPT Slide PPT Slide Heavy Tails Randomized Restarts Rapid Restart on LOG.D SATPLAN Results PPT Slide Heavy-Tailed Distributions in Other Domains PPT Slide SAT Solvers: Themes, cont. PPT Slide PPT Slide SAT Solvers: Recent Theory IV. Current Themes in Encodings Add Declarative Domain Knowledge Kinds of Knowledge Expressing Knowledge Logical Status of Heuristics Axiomatic Form Questions Experiment: Logistics ntab solution of logistics Answers How to Generate Control Knowledge --- Automatically Encodings: Themes cont. Conclusions Conclusions, cont. |
Author: Bart Selman
Email: selman@cs.cornell.edu Home Page: www.cs.cornell.edu/home/selman |