Universal subgoaling and chunking: the automatic generation and learning of goal hierarchies
Universal subgoaling and chunking: the automatic generation and learning of goal hierarchies
Operations research: an introduction, 4th ed.
Operations research: an introduction, 4th ed.
Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
Practical planning: extending the classical AI planning paradigm
Practical planning: extending the classical AI planning paradigm
Random search in the presence of noise, with application to machine learning
SIAM Journal on Scientific and Statistical Computing
The general utility problem in machine learning
Proceedings of the seventh international conference (1990) on Machine learning
A Critical Look at Experimental Evaluations of EBL
Machine Learning
ML92 Proceedings of the ninth international workshop on Machine learning
Detecting novel classes with applications to fault diagnosis
ML92 Proceedings of the ninth international workshop on Machine learning
Measuring utility and the design of provably good EBL algorithms
ML92 Proceedings of the ninth international workshop on Machine learning
In search of the best constraint satisfaction search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
The hazards of fancy backtracking
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Statistical Methods for Analyzing Speedup Learning Experiments
Machine Learning
Artificial Intelligence - Special volume on planning and scheduling
Learning Search Control Knowledge: An Explanation-Based Approach
Learning Search Control Knowledge: An Explanation-Based Approach
On the Efficient Allocation of Resources for Hypothesis Evaluation: A Statistical Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Meta-heuristic for Subset Problems
PADL '01 Proceedings of the Third International Symposium on Practical Aspects of Declarative Languages
ECAI '00 Proceedings of the Workshop on Local Search for Planning and Scheduling-Revised Papers
Towards Inferring Labelling Heuristics for CSP Application Domains
KI '01 Proceedings of the Joint German/Austrian Conference on AI: Advances in Artificial Intelligence
Automated discovery of composite SAT variable-selection heuristics
Eighteenth national conference on Artificial intelligence
Speedup learning for repair-based search by identifying redundant steps
The Journal of Machine Learning Research
Active preference learning for personalized calendar scheduling assistance
Proceedings of the 10th international conference on Intelligent user interfaces
Automated discovery of local search heuristics for satisfiability testing
Evolutionary Computation
Automatic generation of heuristics for scheduling
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
Optimisation and generalisation: footprints in instance space
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Choosing the fittest subset of low level heuristics in a hyperheuristic framework
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
Automated configuration of mixed integer programming solvers
CPAIOR'10 Proceedings of the 7th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms
Genetic Programming and Evolvable Machines
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Although most scheduling problems are NP-hard, domain specific techniques perform well in practice but are quite expensive to construct. In adaptive problem-solving, domain specific knowledge is acquired automatically for a general problem solver with a flexible control architecture. In this approach, a learning system explores a space of possible heuristic methods for one well-suited to the eccentricities of the given domain and problem distribution. In this article, we discuss an application of the approach to scheduling satellite communications. Using problem distributions based on actual mission requirements, our approach identifies strategies that not only decrease the amount of CPU time required to produce schedules, but also increase the percentage of problems that are solvable within computational resource limitations.