Rina Dechter and Daniel Frost. Backtracking algorithms for constraint satisfaction problems. Technical Report 56, UC-Irvine, 1999.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....Semantic Branching example (e) 4. No good Recording No good recording (also called no good learning) is a powerful pruning technique for solving general CSPs [Dechter 1990; Frost and Dechter 1994; Ginsberg and McAllester 1994; Schiex and Veffaillie 1994; Schiex and Veffaillie 1994; Yokoo 1994; Dechter and Frost 1999] and SAT problems [Roberto J. Bayardo and Schrag 1977] In this section, we adapt this technique to DTP solving. Intuitively, a no good is an assignment of the variables that cannot lead to a solution, and is thus either an induced or explicit constraint of the CSP. It is important not to confuse ....

Dechter, R. and D. Frost (1999). Backtracking algorithms for constraint satisfaction problems. Technical Report, University of California at Irvine.

....of the above problems in section 4. We present our conclusions in section 5. 2 Constraint Satisfaction Problems Constraint satisfaction has been used to model a large class of problems with applications in engineering design, planning, scheduling, resource allocation, and fault diagnosis [7]. In a constraint satisfaction problem (CSP) there are a number of variables, each of which has an associated domain of values. A number of constraints are specified on subsets of these variables restricting the set of values they can take on jointly. The objective of a CSP is to find out if each ....

....rule can be interpreted as the construction of a search tree. In the next section we will see some examples of the running of DLL. Although the DLL algorithm only works for the (Boolean) SAT problem, there exist other, similar, complete systematic search algorithms that work for more general CSPs [7]. Stochastic local search algorithms are alternatives to complete algorithms that obtain the solution through a series of local, randomized, moves through the search space[19] Local search algorithms are often faster at solving satisfiable instances, but cannot detect if a problem has no ....

R. Dechter and D. Frost. Backtracking algorithms for constraint satisfaction problems. Technical report, http://www.ics.uci.edu/#csp/r56-backtracking.pdf, Information and Computer Science Department, UC Irvine, 1999.

....of an NP hard constraint satisfaction problem that arises naturally in the context of ad hoc wireless networks. Constraint satisfaction is a useful formalism for modelling a large class of problems with applications in engineering design, planning, scheduling, resource allocation, fault diagnosis [6]. In a constraint satisfaction problem (CSP) there are a number of variables, each of which has an associated domain of values. A number of constraints are specified on subsets of these # This author is with the Intelligent Information Systems Institute, Department of Computer Science, Cornell ....

R. Dechter and D. Frost. Backtracking algorithms for constraint satisfaction problems. Technical report, http://www.ics.uci.edu/#csp/r56backtracking. pdf, Information and Computer Science Department, UC Irvine, 1999.

....The sensor centered model may require less computation at each node, at the expense of greater communication costs, as compared to the mobile node centered model. 4 Distributed Solution of SensorCSP Complete search algorithms to solve constraint satisfaction problems are based on backtracking [3]. Recently such algorithms have been modified for use in solving distributed CSPs as well. Two such approaches are the asynchronous backtracking algorithm (ABT) 13] and the distributed backtracking algorithm (DIBT) 4] These are both known to be sound and complete algorithms. In these ....

R. Dechter and D. Frost. Backtracking algorithms for constraint satisfaction problems. Technical report, http://www.ics.uci.edu/#csp/r56backtracking. pdf, Information and Computer Science Department, UC Irvine, 1999.

.... Satisfaction problem is NPcomplete (all NP complete problems have no known sub exponential solution algorithms, see [12] Much of the research in to constraint satisfaction problems is concerned with managing intractable problem instances either via heuristics, for better search procedures (see [30, 6, 11]) or by characterising tractable subinstances (see [24] A natural question then arises: for which sub classes of the general constraint satisfaction problem are there tractable algorithms for nding solutions. Early work ( 9, 10, 5] sought to nd conditions that could be imposed on the ....

R. Dechter and D. Frost. Backtracking algorithms for constraint satisfaction problems. Technical report, Informations and Computer Science, University of California, Irvine, Septemeber 1999.

....has some drawbacks, too, and it only addresses one of the many causes for thrashing behaviour. Therefore, numerous approaches are out there trying to be much more intelligent about eliminating virtually any kind of thrashing. For further reading on intelligent backtracking, you may refer e.g. to [Dec97, Bru81, Kon94, Pro93] But be aware that, as [Kum92] puts it, a simple intelligent backtracking scheme may turn out to have less overall complexity than a more complicated intelligent backtracking ; this is due to a tendency towards an increasing expense on the maintenance of auxiliary data ....

R. Dechter. Backtracking algorithms for constraint satisfaction problems - a survey. Technical Report, University of California, Irvine, January 1997. ftp://ftp.ics.uci.edu/ pub/CSP-repository/papers/backtracking.ps.

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R. Dechter and D. Frost. Backtracking algorithms for constraint satisfaction problems, a tutorial survey. Technical report, UCI, 1997.

....has the important virtue of requiring only linear space. Only the currently pursued partial assignment needs to be maintained. Intensive research in the last two decades has been done on improving the basic backtracking search for solving constraint satisfaction problems. For a recent survey see [18]. The most well known version of backtracking for propositional satisfiability is the Davis Logemann Loveland (DPLL) algorithm [10] 13 O( n ) w O( n exp( w n w O( n exp( w n Same as worst case Elimination Conditioning Average time Space worst case better than exp( n ) ....

R. Dechter and D. Frost. Backtracking algorithms for constraint satisfaction problems -- a tutorial survey. In UCI technical report. Also on web page www.ics.uci.edu/~dechter, 1998.

.... algorithms are arc consistency (i = 2) and path consistency (i = 3) 40, 26, 14] Indeed, the recent success of constraint processing algorithms can be attributed primarily to this class of algorithms, either used as stand alone, incomplete algorithms, or incorporated within backtracking search [19, 21]. The idea, visualized in Figure 1, shows that while exact algorithms may record arbitrarily large constraints (depicted by large cliques) i consistency algorithms enforce consistency over smaller subproblems, recording constraints of size i or less. The mini bucket approximation presented in ....

R. Dechter and D. Frost. Backtracking algorithms for constraint satisfaction problems, a tutorial survey. Technical report, UCI, 1997.

.... special cases are arc consistency (i = 2) and path consistency (i = 3) 31, 21, 10] Indeed, the recent success of constraint processing algorithms can be attributed primarily to this class of algorithms, either used as standalone incomplete algorithms or incorporated within backtracking search [15, 16]. The idea and benefit of local consistency algorithms is visualized in Figure 1. The figure shows that while complete algorithms deciding consistency of the whole problem may record arbitrarily large constraints (depicted by large clusters) i consistency algorithms (deciding consistency of ....

R. Dechter and D. Frost. Backtracking algorithms for constraint satisfaction problems, a tutorial survey. Technical report, UCI, 1997.

.... special cases are arc consistency (i = 2) and path consistency (i = 3) 31, 21, 10] Indeed, the recent success of constraint processing algorithms can be attributed primarily to this class of algorithms, either used as standalone, incomplete algorithms or incorporated within backtracking search [15, 16]. The idea and benefit of local consistency algorithms are demonstrated in Figure 1. The figure shows that while exact algorithms may record arbitrarily large constraints, i consistency algorithms decide consistency of smaller subproblems, recording constraints of size i or less. In this paper we ....

R. Dechter and D. Frost. Backtracking algorithms for constraint satisfaction problems, a tutorial survey. Technical report, UCI, 1997.

.... special cases are arc consistency (i = 2) and path consistency (i = 3) 31, 21, 10] Indeed, the recent success of constraint processing algorithms can be attributed primarily to this class of algorithms, either used as standalone, incomplete algorithms or incorporated within backtracking search [15, 16]. The idea and benefit of local consistency algorithms is visualized in Figure 1. The figure shows that while complete algorithms deciding consistency of the whole problem may record arbitrarily large constraints (depicted by large clusters) i consistency algorithms (deciding consistency of ....

R. Dechter and D. Frost. Backtracking algorithms for constraint satisfaction problems, a tutorial survey. Technical report, UCI, 1997.

....has the important virtue of requiring only linear space. Only the currently pursued partial assignment needs to be maintained. Intensive research in the last two decades, had been done on improving the basic backtracking search for solving constraint satisfaction problems. For a recent survey see [18]. The most well known version of backtracking for propositional satisfiability is the Davis Logemann Loveland algorithm [10] 13 2.5 Summary We have given an overview of three well known procedures in constraint satisfaction, solving linear inequalities and propositional satisfiability, ....

R. Dechter and D. Frost. Backtracking algorithms for constraint satisfaction problems-- a tutorial survey. In UCI technical report. Also on web page www.ics.uci.edu/~dechter, 1998.

.... special cases are arc consistency (i = 2) and path consistency (i = 3) 34, 22, 11] Indeed, the recent success of constraint processing algorithms can be attributed primarily to this class of algorithms, either used as stand alone, incomplete algorithms, or incorporated within backtracking search [16, 17]. The idea and benefit of local consistency algorithms are demonstrated in Figure 1. The figure shows that while exact algorithms may record arbitrarily large constraints, i consistency algorithms decide consistency of smaller subproblems, recording constraints of size i or less. In this paper we ....

R. Dechter and D. Frost. Backtracking algorithms for constraint satisfaction problems, a tutorial survey. Technical report, UCI, 1997.

....order to maximize future options for instantiations [10, 3, 18, 21] Look back schemes are invoked when the algorithm encounters a deadend. These schemes perform two functions: One, decide how far to backtrack, by analyzing the reasons for the dead end, a process often referred to as backjumping [8]. Two, record the reasons for the dead end in the form of new constraints so that the same conflicts will not arise again, known as constraint learning and no good recording [23, 2] Stochastic local search strategies have been recently reintroduced into the satisfiability and constraint ....

....A sufficient condition for backtrack free search. Journal of the ACM, 29(1) 24 32, 1982. 7] D. Frost and R. Dechter. In search of best search: An empirical evaluation. In AAAI 94: Proceedings of the Twelfth National Conference on Artificial Intelligence, pages 301 306, Seattle, WA, 1994. [8] J. Gaschnig. Performance measurement and analysis of search algorithms. Technical Report CMU CS 79 124, Carnegie Mellon University, 1979. 9] M. C. Golumbic and R. Shamir. Complexity and algorithms for reasoning about time: a graph theoretic approach. Journal of the ACM, 40:1108 1133. 10] M. ....

R. Dechter and D. Frost. Backtracking algorithms for constraint satisfaction problems; a survey. Constraints, International Journal, to appear 1998.

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Rina Dechter and Daniel Frost. Backtracking algorithms for constraint satisfaction problems. Technical Report 56, UC-Irvine, 1999.

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R. Dechter and D. Frost. Backtracking algorithms for constraint satisfaction problems. Technical report, University of California, Irvine, 1999.

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R. Dechter and D. Frost, Backtracking algorithms for constraint satisfaction problems, Technical Report, Information and Computer Science Department, UC Irvine (1999); http://www.ics.uci. edu/#csp/r56-backtracking.pdf.

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R. Dechter and D. Frost. Backtracking algorithms for constraint satisfaction problems. Technical report, University of California, Irvine, 1999.

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R. Dechter, D. Frost. Backtracking Algorithms for Constraint Satisfaction Problems. Technical report, Department of Information and Computer Science, University of California, Irvine, California, USA, 1997.

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