E. T. Richards and B. Richards. Non-systematic search and no-good learning. Journal of Automated Reasoning, 24(4):483--533, 2000. Home/Search Document Details and Download
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Efficient Solution Techniques for Disjunctive Temporal.. - Tsamardinos, Pollack (2002) (Correct)
....Problem, it is more convenient to encode and use as justifications sets of variables than sets of constraints, espedaJly since in the DTP the constraints are implidt) The two definitions of no goods are equivalent for aJl purposes of this paper. For a more thorough discussion on the subject see [Richards 1998]. 15 failure of all the extensions of A. In other words, we assume that invoking a search on the successor A v u, returns either a solution or the no good A v u, J, By Theorem 5, if all successors of A fail returning A v u ,J , then we can infer the new no good A, J , which can be ....
Richards, E. T. (1998). Non-systematic Search and No-good Learning. Ph.D. Thesis IC Parc. London, Imperial College. 48
Automatic Generation of Implied Clauses for SAT - Drake, Frisch, Walsh (Correct)
....we hope to be able to classify other problem instances using those features in order to make a decision on whether inference will be worthwhile. 4. Investigate other forms of resolution and inference. For example, con ict analysis (or nogood learning) is a very useful technique for many problems [12, 14]. It is used in both GRASP [13] and SATO [16] which appear to be the fastest publicly available SAT solvers. Con ict analysis is a form of inference, and we want to compare con ict analysis and resolution to see if they are useful on the same problems, and if so whether they are orthogonal to ....
E Thomas Richards and Barry Richards. Non-systematic Search and No-good Learning, pages 107-151. Frontiers in Articial Intelligence and Applications. IOS Press, 2000.
Stochastic Systematic Search Algorithms for Satisfiability - Lynce, Baptista.. (2001) (3 citations) (Correct)
....[11, 2] In backtrack search, current state of the art SAT solvers extensively resort to randomization, most often for selecting variable assignments but (and as a result) also within search restart strategies. Moreover, the recent work by S. Prestwich [13] though preceeded by the work of others [6, 14]) has motivated the utilization of randomly picked backtrack points in SAT algorithms. 1.1 Objectives This paper has four main objectives. First, to propose the utilization of random backtracking in backtrack search SAT algorithms. Second, to introduce the more general form of unrestricted ....
....combined GSAT and dynamic backtracking in an algorithm which enables arbitrary search movement [6] starting with any complete assignment and evolving by ipping values of variables obtained from the con icts. Similarly, Richards 1 and Richards implemented another algorithm, learn SAT [14], that starts from a any consistent partial labeling to the variables. In learn SAT there is no notion of important early choices, since there is no backtracking. Here, learning is incorporated by recording the causes of the con icts. Moreover, CLS, recently proposed by Prestwich [13] involves ....
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E. T. Richards and B. Richards. Non-systematic search and no-good learning. Journal of Automated Reasoning, 24(4):483-533, 2000.
Unrestricted Backtracking Algorithms for Satisfiability - Lynce, Baptista.. (Correct)
....strategies. Search restarts are a well known strategy for coping with hard real world satis able (and often unsatis able) instances that is already being used by di erent state of the art SAT solvers [10, 13] Moreover, the recent work by S. Prestwich [14] though preceded by the work of others [6, 15]) has motivated the utilization of randomly picked backtrack points in SAT algorithms. 1.1 Objectives This paper has three main objectives. First, to propose a general form of backtracking search strategy, referred to as unrestricted backtracking. Second, to describe and analyze several more ....
....McAllester combined GSAT and dynamic backtracking in an algorithm which enables arbitrary search movement [6] starting with any complete assignment and evolving by ipping values of variables obtained from the con icts. Similarly, Richards and Richards implemented another algorithm, learn SAT [15], that starts from a any consistent partial labeling to the variables. In learn SAT there is no notion of important early choices, since there is no backtracking. Here, learning is incorporated by recording the causes of the con icts. Moreover, CLS, recently proposed by Prestwich [14] involves ....
[Article contains additional citation context not shown here]
E. T. Richards and B. Richards. Non-systematic search and no-good learning. Journal of Automated Reasoning, 24(4):483-533, 2000.
Heuristic-Based Backtracking for Propositional.. - Bhalla, Lynce, de.. (Correct)
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E. T. Richards and B. Richards. Non-systematic search and no-good learning. Journal of Automated Reasoning, 24(4):483--533, 2000.
Heuristic-Based Backtracking for Propositional.. - Bhalla, Lynce, de.. (Correct)
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E. T. Richards and B. Richards. Non-systematic search and no-good learning. Journal of Automated Reasoning, 24(4):483--533, 2000.
Heuristic Backtracking Algorithms for SAT - Bhalla, Lynce, de Sousa.. (Correct)
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E. T. Richards and B. Richards. Non-systematic search and no-good learning. Journal of Automated Reasoning, 24(4):483--533, 2000.
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