An Introduction to Autonomous Search

An Autonomous Search system should have the ability to advantageously modify its internal components when exposed to changing external forces and opportunities. It corresponds to a particular case of adaptive systems, with the objective of improving its problem-solving performance by adapting its search strategy to the problem at hand. Internal components correspond to the various algorithms involved in the search process: heuristics, inference mechanisms, etc. External forces correspond to the evolving information collected during this search process: search landscape analysis (quality, diversity, entropy, etc), external knowledge (prediction models, rules, etc) and so on.

In 2007, we organized the first workshop on Autonomous Search in Providence, RI, USA, in order to present relevant works aimed at building more intelligent solvers for the constraint programming community. We tried to 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, keeping the focus on constrained problem solving, this book includes ten chapters that cover different solving techniques from metaheuristics to tree-based search and that illustrate how these solving techniques may benefit from intelligent tools by improving their efficiency and adaptability to solve the problems at hand.