Using deep structure to locate hard problems

Authors:
Colin P. Williams;Tad Hogg
Affiliations:
Xerox Palo Alto Research Center, Palo Alto, CA;Xerox Palo Alto Research Center, Palo Alto, CA
Venue:
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Year:
1992

Citing 5
Cited 6

An assumption-based TMS

Artificial Intelligence
Phase transitions in artificial intelligence systems

Artificial Intelligence
On selecting a satisfying truth assignment (extended abstract)

SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
Where the really hard problems are

IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Search rearrangement backtracking and polynomial average time

Artificial Intelligence

Using inferred disjunctive constraints to decompose constraint satisfaction problems

IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Extracting constraint satisfaction subproblems

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Evolution of neural networks topologies and learning parameters to produce hyper-heuristics for constraint satisfaction problems

Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Neural networks to guide the selection of heuristics within constraint satisfaction problems

MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Algorithm portfolio design: theory vs. practice

UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Learning vector quantization for variable ordering in constraint satisfaction problems

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

One usually writes A.I. programs to be used on a range of examples which, although similar in kind, differ in detail. This paper shows how to predict where, in a space of problem instances, the hardest problems are to be found and where the fluctuations in difficulty are greatest Our key insight is to shift emphasis from modelling sophisticated algorithms directly to modelling a search space which captures their principal effects. This allows us to analyze complex A.I. problems in a simple and intuitive way. We present a sample analysis, compare our model's quantitative predictions with data obtained independently and describe how to exploit the results to estimate the value of preprocessing. Finally, we circumscribe the kind problems to which the methodology is suited.