Stochastic Search And Phase Transitions: AI Meets Physics Bart Selman AT&T Bell Laboratories Murray Hill, N.J. USA

Stochastic Search And Phase Transitions: AI Meets Physics

Computational Challenges In AI

A Few Examples

Complexity Results, Cont.

What Is The Impact Of These Results?

Recent Developments

Overview

PART A. Computationally Hard Instances

Satisfiability

Some Example Applications Of SAT

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Average-Case Analysis

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Aside: Small Hard Instances Do Exist!

The Instance

Generating Hard Random Formulas

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Intuition

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Theoretical Status Of Threshold

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The Physics Of Thresholds

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Summary Phase Transition Effect

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PART B. Fast Stochastic Methods

Standard Procedures For SAT

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Randomized Greedy Local Search: GSAT

How Well Does It Work?

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Improvements Of Basic Local Search

Simulated Annealing

Random Walk

Biased Random Walk

Experimental Results: Hard Random 3SAT

Other Applications Of GSAT

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Showing UNSAT / Inconsistencies

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Length Of Proofs

Limitations Of Resolution

Stochastic Search For Proofs

Recap Of Results

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Impact And Future Directions

Impact, Cont.

Some Challenges

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