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77
Efficient methods for qualitative spatial reasoning
 Proceedings of the 13th European Conference on Artificial Intelligence
, 1998
"... The theoretical properties of qualitative spatial reasoning in the RCC8 framework have been analyzed extensively. However, no empirical investigation has been made yet. Our experiments show that the adaption of the algorithms used for qualitative temporal reasoning can solve large RCC8 instances, ..."
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Cited by 52 (12 self)
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The theoretical properties of qualitative spatial reasoning in the RCC8 framework have been analyzed extensively. However, no empirical investigation has been made yet. Our experiments show that the adaption of the algorithms used for qualitative temporal reasoning can solve large RCC8 instances, even if they are in the phase transition region  provided that one uses the maximal tractable subsets of RCC8 that have been identified by us. In particular, we demonstrate that the orthogonal combination of heuristic methods is successful in solving almost all apparently hard instances in the phase transition region up to a certain size in reasonable time.
Beyond NP: the QSAT phase transition
, 1999
"... We show that phase transition behavior similar to that observed in NPcomplete problems like random 3Sat occurs further up the polynomial hierarchy in problems like random 2Qsat. The differences between Qsat and Sat in phase transition behavior that Cadoli et al report are largely due to the ..."
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Cited by 50 (7 self)
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We show that phase transition behavior similar to that observed in NPcomplete problems like random 3Sat occurs further up the polynomial hierarchy in problems like random 2Qsat. The differences between Qsat and Sat in phase transition behavior that Cadoli et al report are largely due to the presence of trivially unsatisfiable problems. Once they are removed, we see behavior more familiar from Sat and other NPcomplete domains. There are, however, some differences. Problems with short clauses show a large gap between worst case behavior and median, and the easyhardeasy pattern is restricted to higher percentiles of search cost. We compute
Exact Phase Transitions in Random Constraint Satisfaction Problems
 Journal of Artificial Intelligence Research
, 2000
"... In this paper we propose a new type of random CSP model, called Model RB, which is a revision to the standard Model B. It is proved that phase transitions from a region where almost all problems are satis able to a region where almost all problems are unsatis able do exist for Model RB as the number ..."
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Cited by 42 (9 self)
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In this paper we propose a new type of random CSP model, called Model RB, which is a revision to the standard Model B. It is proved that phase transitions from a region where almost all problems are satis able to a region where almost all problems are unsatis able do exist for Model RB as the number of variables approaches in nity. Moreover, the critical values at which the phase transitions occur are also known exactly. By relating the hardness of Model RB to Model B, it is shown that there exist a lot of hard instances in Model RB.
An empirical study of the manipulability of single transferable voting
 In: Proc. of the 19th ECAI, IOS Press
, 2010
"... Abstract. Voting is a simple mechanism to combine together the preferences of multiple agents. Agents may try to manipulate the result of voting by misreporting their preferences. One barrier that might exist to such manipulation is computational complexity. In particular, it has been shown that it ..."
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Cited by 27 (13 self)
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Abstract. Voting is a simple mechanism to combine together the preferences of multiple agents. Agents may try to manipulate the result of voting by misreporting their preferences. One barrier that might exist to such manipulation is computational complexity. In particular, it has been shown that it is NPhard to compute how to manipulate a number of different voting rules. However, NPhardness only bounds the worstcase complexity. Recent theoretical results suggest that manipulation may often be easy in practice. In this paper, we study empirically the manipulability of single transferable voting (STV) to determine if computational complexity is really a barrier to manipulation. STV was one of the first voting rules shown to be NPhard. It also appears one of the harder voting rules to manipulate. We sample a number of distributions of votes including uniform and real world elections. In almost every election in our experiments, it was easy to compute how a single agent could manipulate the election or to prove that manipulation by a single agent was impossible. 1
Models for random Constraint Satisfaction Problems.
 Proceedings of STOC 2002, 209
, 2000
"... We introduce a class of models for random Constraint Satisfaction Problems. This class includes and generalizes many previously studied models. We characterize those models from our class which are asymptotically interesting in the sense that the limiting probability of satisfiability changes si ..."
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Cited by 26 (4 self)
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We introduce a class of models for random Constraint Satisfaction Problems. This class includes and generalizes many previously studied models. We characterize those models from our class which are asymptotically interesting in the sense that the limiting probability of satisfiability changes significantly as the number of constraints increases. We also discuss models which exhibit a sharp threshold for satisfiability in the sense that the limiting probability jumps from 0 to 1 suddenly as the number of constraints increases.
A simple model to generate hard satisfiable instances
 In Proceedings of IJCAI’05
, 2005
"... In this paper, we try to further demonstrate that the models of random CSP instances proposed by [Xu and Li, 2000; 2003] are of theoretical and practical interest. Indeed, these models, called RB and RD, present several nice features. First, it is quite easy to generate random instances of any arity ..."
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Cited by 26 (5 self)
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In this paper, we try to further demonstrate that the models of random CSP instances proposed by [Xu and Li, 2000; 2003] are of theoretical and practical interest. Indeed, these models, called RB and RD, present several nice features. First, it is quite easy to generate random instances of any arity since no particular structure has to be integrated, or property enforced, in such instances. Then, the existence of an asymptotic phase transition can be guaranteed while applying a limited restriction on domain size and on constraint tightness. In that case, a threshold point can be precisely located and all instances have the guarantee to be hard at the threshold, i.e., to have an exponential treeresolution complexity. Next, a formal analysis shows that it is possible to generate forced satisfiable instances whose hardness is similar to unforced satisfiable ones. This analysis is supported by some representative results taken from an intensive experimentation that we have carried out, using complete and incomplete search methods. 1
Trying Again to FailFirst
 In: Recent Advances in Constraints. Papers from the 2004 ERCIM/CologNet WorkshopCSCLP 2004. LNAI No. 3419
, 2005
"... In ECAI 1998 Smith & Grant performed a study [1] of the failfirst principle of Haralick & Elliott [2]. The failfirst principle states that "To succeed, try first where you are most likely to fail." For constraint satisfaction problems (CSPs), Haralick & Elliott realized th ..."
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Cited by 22 (7 self)
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In ECAI 1998 Smith & Grant performed a study [1] of the failfirst principle of Haralick & Elliott [2]. The failfirst principle states that "To succeed, try first where you are most likely to fail." For constraint satisfaction problems (CSPs), Haralick & Elliott realized this principle by minimizing branch depth.
Random constraint satisfaction: easy generation of hard (satisfiable) instances
 Artificial Intelligence
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Many Hard Examples in Exact Phase Transitions with Application to Generating Hard Satisfiable Instances
 in Theoretical Computer Science
, 2003
"... This paper analyzes the resolution complexity of two random CSP models, i.e. Model RB/RD for which we can establish the existence of phase transitions and identify the threshold points exactly. By encoding CSPs into CNF formulas, this paper proves that almost all instances of Model RB/RD have no tre ..."
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Cited by 20 (5 self)
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This paper analyzes the resolution complexity of two random CSP models, i.e. Model RB/RD for which we can establish the existence of phase transitions and identify the threshold points exactly. By encoding CSPs into CNF formulas, this paper proves that almost all instances of Model RB/RD have no treelike resolution proofs of less than exponential size. Thus, we not only introduce new families of CNF formulas hard for resolution, which is a central task of ProofComplexity theory, but also propose models with both many hard instances and exact phase transitions. Moreover, it is shown both theoretically and experimentally that an application of RB/RD might be in the generation of hard satisfiable instances, which is not only of practical importance but also related to some open problems in cryptography such as generating oneway functions. Finally, conclusions are presented, as well as a detailed comparison of RB/RD with some wellstudied models such as the Hamiltonian cycle problem and random 3SAT.
The Resolution Complexity of Random Constraint Satisfaction Problems
"... We consider random instances of constraint satisfaction problems where each variable has domain size d, and each constraint contains t restrictions on k variables. For each (d; k; t) we determine whether the resolution complexity is a.s. constant, polynomial or exponential in the number of variables ..."
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Cited by 20 (6 self)
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We consider random instances of constraint satisfaction problems where each variable has domain size d, and each constraint contains t restrictions on k variables. For each (d; k; t) we determine whether the resolution complexity is a.s. constant, polynomial or exponential in the number of variables. For a particular range of (d; k; t), we determinea sharp threshold for resolution complexity where the resolution complexity drops from a.s. exponential to a.s. polynomial when the clause density passes a specific value.