Dynamic Flexible Constraint Satisfaction and its Application to AI Planning
First, I would like to thank my principal supervisor Dr Qiang Shen for all his help, advice and friendship throughout. Many thanks also to my second supervisor Dr Peter Jarvis for his enthusiasm, help and friendship. I would also like to thank the other members of the Approximate and Qualitative Reasoning group at Edinburgh who have also helped and inspired me. This project has been funded by an EPSRC studentship, award num ber 97305803. I would like, therefore, to extend my gratitude to EPSRC for supporting this work. Many thanks to the staff at Edinburgh University for all their help and support and for promptly fixing any technical problems that I have had. My whole family have been both encouraging and supportive throughout the completion of this book, for which I am forever indebted. York, April 2003 Ian Miguel Contents List of Figures XV 1 Introduction. 1 1. 1 Solving Classical CSPs. 2 1. 2 Applicat ions of Classical CSP. 3 1. 3 Limitations of Classical CSP. 6 1. 3. 1 Flexible CSP 6 1. 3. 2 Dynamic CSP. 7 1. 4 Dynamic Flexible CSP. 7 1. 5 Flexible Planning: a DFCSP Application. 8 1. 6 Structure. 9 1. 7 Contributions and their Significance 11 2 The Constraint Satisfaction Problem 13 2. 1 Constraints and Constraint Graphs. 13 2. 2 Tree Search Solution Techniques for Classical CSP. 16 2. 2. 1 Backtrack. 17 2. 2. 2 Backjumping. 18 2. 2. 3 Conflict-Directed Backjumping. 19 2. 2. 4 Backmarking.
Methods are developed which, for the first time, are able to solve problems which both contain a dynamic component and are open to compromise if a ‘perfect’ solution does not existClassical artificial intelligence planning is extended to incorporate preferences so that it too can support compromiseA trade-off between the length of a plan versus the number and severity of the compromises it contains is now possibleAn extensive empirical analysis of the new dynamic-flexible problem solving methods and the development of a new flexible planning