Boolean satisfiability is probably the most studied of the combinatorial optimization/search problems. Significant effort has been devoted to trying to provide practical solutions to this problem for problem instances encountered in a range of applications in electronic design automation (EDA), as well as in artificial intelligence (AI). This study has culminated in the development of several SAT packages, both proprietary and in the public domain (e.g. GRASP, SATO) which find significant use in both research and industry. Most existing complete solvers are variants of the Davis-Putnam (DP) search algorithm. In this paper we describe the development of a new complete solver, Chaff which achieves significant performance gains through careful engineering of all aspects of the search-especially a particularly efficient implementation of Boolean constraint propagation (BCP) and a novel low overhead decision strategy. Chaff has been able to obtain one to two orders of magnitude performance improvement on difficult SAT benchmarks in comparison with other solvers (DP or otherwise), including GRASP and SATO.

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Chaff: engineering an efficient SAT solver

One of the most important features of current state-of-the-art SAT solvers is the use of conflict based backtracking and learning techniques. In this paper, we generalize various conflict driven learning strategies in terms of different partitioning schemes of the implication graph. We re-examine the learning techniques used in various SAT solvers and propose an array of new learning schemes. Extensive experiments with real world examples show that the best performing new learning scheme has at least a 2/spl times/ speedup compared with learning schemes employed in state-of-the-art SAT solvers.

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Efficient conflict driven learning in a boolean satisfiability solver

Artificial Intelligence and Tutoring Systems

Business data mining-a machine learning perspective

Qualitative representation of positional information

The Vanderbilt Schedule Optimizer Prototype (VSOP), which uses genetic algorithms as search methods for job shop scheduling problems, is discussed A job shop is a facility that produces goods according to prespecified process plans, under several domain-dependent and common sense constraints The scheduling of orders in a job shop is a multifaceted problem VSOP uses domain-specific chromosome representations, recombination operators, and local enumerative search to increase efficiency Experimental results from a fully implemented VSOP package are presented >

Managing genetic search in job shop scheduling

Model-based diagnosis in intensive care monitoring: The YAQ approach

The authors describe SIMON (signal interpretation and monitoring), an approach which combines static domain-specific information, which relates variables and alarm events, with dynamic information provided by a model. It is currently being tested for the monitoring of neonates in the intensive care unit. The model component is responsible for estimating the state of the monitored system, predicting the evolution of the system's variables and parameters, and establishing a monitoring contest. This information is then used by the DA (data abstraction) and the data acquisition modules to plan a monitoring strategy to filter, rank, and abstract incoming data. Faults and artifact models included in the DA permit the low-level detection of noise-contaminated episodes. The adaptation of the monitoring strategy to these changes in the environment effectively shields the model from untrustworthy information and thus increases the reliability and robustness of the system. The scheduling mechanism included in the DA permits a continuous evaluation of the system load as well as an ability to process all its tasks. >

The SIMON project: model-based signal acquisition, analysis, and interpretation in intelligent patient monitoring

Intelligent real-time patient monitoring encompasses data acquisition and reduction, sensor validation, diagnosis, therapy advice, and selective display of information. This paper describes the architecture and the functionality of a prototype intelligent patient monitoring system, named SIMON, designed to meet these requirements. In SIMON, the various aspects of a monitoring task are performed by three semi-independent modules running asynchronously: the feature extraction, the patient model, and the display modules. Central to SIMON is the notion of context sensitivity which permits (a) the adaptation of the monitoring strategy in response to changes either in the patient state or in the monitoring equipment and (b) the contextual interpretation of incoming data. SIMON is currently applied to the task of monitoring newborn infants with respiratory distress syndrome (RDS) and undergoing assisted ventilation.

Serdar Uckun

Papers

Managing genetic search in job shop scheduling

Model-based diagnosis in intensive care monitoring: The YAQ approach

The SIMON project: model-based signal acquisition, analysis, and interpretation in intelligent patient monitoring

SIMON: A distributed computer architecture for intelligent patient monitoring

Qualitative modeling as a paradigm for diagnosis and prediction in critical care environments