Biased random walks, Lyapunov functions, and stochastic analysis of best fit bin packing
Journal of Algorithms
On Forward Checking for Non-binary Constraint Satisfaction
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
Beyond NP: Arc-Consistency for Quantified Constraints
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Online Stochastic Combinatorial Optimization
Online Stochastic Combinatorial Optimization
Value ordering for quantified CSPs
Constraints
Regrets only! online stochastic optimization under time constraints
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
CSP properties for quantified constraints: definitions and complexity
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
QCSP made practical by virtue of restricted quantification
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
QCSP-solve: a solver for quantified constraint satisfaction problems
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Mixed constraint satisfaction: a framework for decision problems under incomplete knowledge
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Set based robust design of mechanical systems using the quantifier constraint satisfaction algorithm
Engineering Applications of Artificial Intelligence
Real-time solving of quantified CSPs based on Monte-Carlo game tree search
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
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We define Realtime Online solving of Quantified Constraint Satisfaction Problems (QCSPs) as a model for realtime online CSP solving. We use a combination of propagation, lookahead and heuristics and show how all three improve performance. For adversarial opponents we show that we can achieve promising results through good lookahead and heuristics and that a version of alpha beta pruning performs strongly. For random opponents, we show that we can frequently achieve solutions even on problems which lack a winning strategy and that we can improve our success rate by using Existential Quantified Generalised Arc Consistency, a lower level of consistency than SQGAC, to maximise pruning without removing solutions. We also consider the power of the universal opponent and show that through good heuristic selection we can generate a significantly stronger player than a static heuristic provides.