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- Carla P. Gomes, Bart Selman, Henry A. Kautz
- AAAI/IAAI
- 1998

Unpredictability in the running time of complete search procedures can often be explained by the phenomenon of " heavy-tailed cost distributions " , meaning that at any time during the experiment there is a non-negligible probability of hitting a problem that requires exponentially more time to solve than any that has been encountered before (Gomes et al.… (More)

- Carla P. Gomes, Bart Selman
- Artif. Intell.
- 2001

- Carla P. Gomes, Jörg Hoffmann, Ashish Sabharwal, Bart Selman
- IJCAI
- 2007

We introduce a new technique for counting models of Boolean satisfiability problems. Our approach incorporates information obtained from sampling the solution space. Unlike previous approaches, our method does not require uniform or near-uniform samples. It instead converts local search sampling without any guarantees into very good bounds on the model… (More)

- Carla P. Gomes, Bart Selman, Nuno Crato, Henry A. Kautz
- J. Autom. Reasoning
- 2000

We study the runtime distributions of backtrack procedures for propositional satisfiability and constraint satisfaction. Such procedures often exhibit a large variability in performance. Our study reveals some intriguing properties of such distributions: They are often characterized by very long tails or " heavy tails ". We will show that these… (More)

- Carla P. Gomes, Bart Selman
- AAAI/IAAI
- 1997

Recent progress on search and reasoning procedures has been driven by experimentation on computation-ally hard problem instances. Hard random problem distributions are an important source of such instances. Challenge problems from the area of nite algebra have also stimulated research on search and reasoning procedures. Nevertheless, the relation of such… (More)

- Carla P. Gomes, Ashish Sabharwal, Bart Selman
- NIPS
- 2006

We propose a new technique for sampling the solutions of combinatorial problems in a near-uniform manner. We focus on problems specified as a Boolean formula , i.e., on SAT instances. Sampling for SAT problems has been shown to have interesting connections with probabilistic reasoning, making practical sampling algorithms for SAT highly desirable. The best… (More)

- Ryan Williams, Carla P. Gomes, Bart Selman
- IJCAI
- 2003

There has been significant recent progress in reasoning and constraint processing methods. In areas such as planning and finite model-checking, current solution techniques can handle combinatorial problems with up to a million variables and five million constraints. The good scaling behavior of these methods appears to defy what one would expect based on a… (More)

- Dimitris Achlioptas, Carla P. Gomes, Henry A. Kautz, Bart Selman
- AAAI/IAAI
- 2000

A major difficulty in evaluating incomplete local search style algorithms for constraint satisfaction problems is the need for a source of hard problem instances that are guaranteed to be satisfiable. A standard approach to evaluate incomplete search methods has been to use a general problem generator and a complete search method to filter out the… (More)

We describe research and results centering on the construction and use of Bayesian models that can predict the run time of problem solvers. Our efforts are motivated by observations of high variance in the time required to solve instances for several challenging problems. The methods have application to the decision-theoretic control of hard search and… (More)

- Daniel Sheldon, Bistra N. Dilkina, +8 authors William Vaughan
- UAI
- 2010

We introduce a new optimization framework to maximize the expected spread of cascades in networks. Our model allows a rich set of actions that directly manipulate cascade dynamics by adding nodes or edges to the network. Our motivating application is one in spatial conservation planning, where a cascade models the dispersal of wild animals through a… (More)