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GECCO
2008
Springer
142views Optimization» more  GECCO 2008»
15 years 5 months ago
Complexity of Max-SAT using stochastic algorithms
Hill-climbing has been shown to be more effective than exhaustive search in solving satisfiability problems.Also, it has been used either by itself or in combination with other ...
Mohamed Qasem, Adam Prügel-Bennett
117
Voted
AIPS
1994
15 years 5 months ago
Solving Time-critical Decision-making Problems with Predictable Computational Demands
In this work we present an approach to solving time-critical decision-making problems by taking advantage of domain structure to expand the amountof time available for processing ...
Thomas Dean, Lloyd Greenwald
CISSE
2009
Springer
15 years 8 months ago
Unbiased Statistics of a Constraint Satisfaction Problem - a Controlled-Bias Generator
: We show that estimating the complexity (mean and distribution) of the instances of a fixed size Constraint Satisfaction Problem (CSP) can be very hard. We deal with the main two ...
Denis Berthier
147
Voted
CDC
2010
IEEE
210views Control Systems» more  CDC 2010»
14 years 11 months ago
Advances in moving horizon estimation for nonlinear systems
In the past decade, moving horizon estimation (MHE) has emerged as a powerful technique for estimating the state of a dynamical system in the presence of nonlinearities and disturb...
Angelo Alessandri, Marco Baglietto, Giorgio Battis...
CDC
2009
IEEE
132views Control Systems» more  CDC 2009»
15 years 8 months ago
Q-learning and Pontryagin's Minimum Principle
Abstract— Q-learning is a technique used to compute an optimal policy for a controlled Markov chain based on observations of the system controlled using a non-optimal policy. It ...
Prashant G. Mehta, Sean P. Meyn