Titlebook: Autonomous Search; Youssef Hamadi,Eric Monfroy,Frédéric Saubion Book 2012 Springer-Verlag Berlin Heidelberg 2012 Adaptation.Artificial lif

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The BG Family: Facts, Rules and Constraintsl constraint-solving metrics and the potential power available from a mixture of heuristics that references them. It describes a weighted-mixture decision process, and explains how one autonomous learner constructs its own labeled training examples from its search experience, and then learns a weigh

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Basic Algorithms for BG Homomorphismeinforcement learning methods for the online tuning of parameters in stochastic local search algorithms. In particular, the reactive tuning is obtained by learning a (near-)optimal policy in a Markov decision process where the states summarize relevant information about the recent history of the sea

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https://doi.org/10.1007/978-1-84800-286-9at is, the variation of parameter settings while solving an instance of a problem. We classify these approaches according to the main classes of online parameter-adaptation techniques. One conclusion of this review is that the available approaches do not exploit an in-depth understanding of the effe

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Das Quelltexttransformations- Werkzeug,ng. Indeed, it allows clause sharing between multiple processing units working on related (sub-)problems. However, without limitation, sharing clauses might lead to an exponential blow up in communication or to the sharing of irrelevant clauses. This chapter, proposes new innovative policies to dyna

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Conceptual Graphs with Negation describe more conceptually the concept of Autonomous Search from the previously described related works. The purpose of this book is to provide a clear overview of recent advances in autonomous tools for optimization and constraint satisfaction problems. In order to be as exhaustive as possible, ke

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Basic Algorithms for BG Homomorphismttings to performance, and use this model to iteratively identify promising settings. We also briefly survey other algorithm configuration and parameter tuning procedures, as well as related approaches, such as instance-based algorithm selection and configuration.

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Graph-based Knowledge Representationplanning — and domains — from medicine to law to recommender systems — is testimony to its generality. In this chapter, we review the application of CBR to search and especially to constraint solving. We present CPHYDRA, a recent successful application of CBR to autonomous constraint solving. In CPH