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Using CBR to select solution strategies in constraint programming
 In ICCBR
, 2005
"... Abstract. Constraint programming is a powerful paradigm that offers many different strategies for solving problems. Choosing a good strategy is difficult; choosing a poor strategy wastes resources and may result in a problem going unsolved. We show how CaseBased Reasoning can be used to select good ..."
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Cited by 19 (0 self)
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Abstract. Constraint programming is a powerful paradigm that offers many different strategies for solving problems. Choosing a good strategy is difficult; choosing a poor strategy wastes resources and may result in a problem going unsolved. We show how CaseBased Reasoning can be used to select good strategies. We design experiments which demonstrate that, on two problems with quite different characteristics, CBR can outperform four other strategy selection techniques. 1
Input Feature Approximation Using Instance Sampling
"... Features (or properties) of problem inputs can provide information not only for classifying input but also for selecting the right algorithm for a particular instance. Using these input properties can help close the gap between problem complexity and algorithm efficiency, but enumerating the feature ..."
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Features (or properties) of problem inputs can provide information not only for classifying input but also for selecting the right algorithm for a particular instance. Using these input properties can help close the gap between problem complexity and algorithm efficiency, but enumerating the features is often at least as costly as solving the problem itself. An approximation of such features can be useful, though. This work defines the notion of group input properties and proposes an efficient solution for their approximation through input sampling. Using common statistical techniques, we show that samples of inputs for sorting and graph problems retain the general properties of the inputs themselves.
A New Class of NatureInspired . . .
"... We present, and evaluate benefits of, a design methodology for translating natural phenomena represented as mathematical models, into novel, selfadaptive, peertopeer (p2p) distributed computing algorithms (“protocols”). Concretely, our first contribution is a set of techniques to translate discre ..."
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We present, and evaluate benefits of, a design methodology for translating natural phenomena represented as mathematical models, into novel, selfadaptive, peertopeer (p2p) distributed computing algorithms (“protocols”). Concretely, our first contribution is a set of techniques to translate discrete “sequence equations ” (also known as difference equations) into new p2p protocols called “sequence protocols”. Sequence protocols are selfadaptive, scalable, and faulttolerant, with applicability in p2p settings like Grids. A sequence protocol is a set of probabilistic local and messagepassing actions for each process. These actions are translated from terms in a set of source sequence equations. Individual processes do not simulate the source sequence equations completely. Instead, each process executes probabilistic local and message passing actions, so that the emergent roundtoround behavior of the sequence protocol in a p2p system can be probabilistically predicted by the source sequence equations. The paper’s second contribution is the design and evaluation of a set of sequence protocols for detection of two global triggers in a distributed system: threshold detection and interval detection. This paper’s third contribution is a new selfadaptive Grid computing protocol called “HoneyAdapt”. HoneyAdapt is derived from sequence equations modeling adaptive bee foraging behavior in nature. HoneyAdapt is intended