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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Constraints are a natural means of knowledage representation in many disparate fields. A constraint often takes the form of an equation or inequality, but in the most abstract senseis simply a logical relation among several variables expressing a set of admissable value combinations. The following are simple examples: the sum of two variables must equal 30; no two adjacent countries on the map may be coloured the same. It is this generality and simplicity of structure which underly the ubiquity of the constraint-based representation in Artificial Intelligence.

This two-volume set of LNCS 12736-12737 constitutes the refereed proceedings of the 7th International Conference on Artificial Intelligence and Security, ICAIS 2021, which was held in Dublin, Ireland, in July 2021. The conference was formerly called “International Conference on Cloud Computing and Security” with the acronym ICCCS. The total of 93 full papers and 29 short papers presented in this two-volume proceedings was carefully reviewed and selected from 1013 submissions. Overall, a total of 224 full and 81 short papers were accepted for ICAIS 2021; the other accepted papers are presented in CCIS 1422-1424. The papers were organized in topical sections as follows: Part I: Artificial intelligence; and big data Part II: Big data; cloud computing and security; encryption and cybersecurity; information hiding; IoT security; and multimedia forensics

This volume contains the proceedings of the First International Conference on IntegrationofAIandORTechniquesinConstraintProgrammingforCombina- rialOptimisation Problems.This new conferencefollows the seriesof CP-AI-OR International Workshops on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimisation Problems held in Ferrara (1999), Paderborn (2000), Ashford (2001), Le Croisic (2002), and Montreal (2003). The success of the previous workshops has demonstrated that CP-AI-OR is bec- ing a major forum for exchanging ideas and methodologiesfrom both ?elds. The aim of this new conference is to bring together researchersfrom AI and OR, and to give them the opportunity to show how the integration of techniques from AI and OR can lead to interesting results on large scale and complex problems. The integration of techniques from Arti?cial Intelligence and Operations - search has provided e?ective algorithms for tackling complex and large scale combinatorial problems with signi?cant improvements in terms of e?ciency, scalability and optimality. The bene?t of this integration has been shown in applications such as hoist scheduling, rostering, dynamic scheduling and vehicle routing. At the programming and modelling levels, most constraint languages embed OR techniques to reason about collections of constraints, so-calledglobal constraints. Some languages also provide support for hybridization allowing the programmer to build new integrated algorithms. The resulting multi-paradigm programmingframeworkcombines the ?exibility and modelling facilities of C- straint Programming with the special purpose and e?cient methods from - erations Research

This two-volume set is assembled following the 2008 International Conference on Computational Science and Its Applications, ICCSA 2008, a premium int- national event held in Perugia, Italy, from June 30 to July 3, 2008. The collection of fully refereed high-quality original works accepted as theme papers for presentation at ICCSA 2008 are published in this LNCS proceedings set. This outstanding collection complements the volume of workshop papers, traditionally published by IEEE Computer Society. The continuous support of computational science researchers has helped ICCSA to become a ?rmly established forum in the area of scienti?c computing and the conference itself become a recurring scienti?c and professional meeting that cannot be given up. The computational science ?eld, based on fundamental disciplines such as mathematics, physics, and chemistry, is ?nding new computational approaches to foster the human progress in heterogeneous and fundamental areas such as aerospace and automotive industries, bioinformatics and nanotechnology studies, networks and grid computing, computational geometry and biometrics, computer education, virtual reality, and art. Due to the growing complexity of many ch- lenges in computational science, the use of sophisticated algorithms and eme- ing technologies is inevitable. Together, these far-reaching scienti?c areas help to shape this conference in the areas of state-of-the-art computational science research and applications, encompassing the facilitating theoretical foundations and the innovative applications of such results in other areas.

This book constitutes the thoroughly refereed post-proceedings of the 4th International Conference on Machine Learning and Cybernetics, ICMLC 2005, held in Guangzhou, China in August 2005. The 114 revised full papers of this volume are organized in topical sections on agents and distributed artificial intelligence, control, data mining and knowledge discovery, fuzzy information processing, learning and reasoning, machine learning applications, neural networks and statistical learning methods, pattern recognition, vision and image processing.

Thisvolumecontainsaselectionofpapersfromthe5thInternationalConference on the Practice and Theory of Automated Timetabling (PATAT 2004) held in Pittsburgh, USA, August 18-20, 2004. Indeed, as we write this preface, in the Summer of 2005, we note that we are about one month away from the tenth anniversary of the very?rst PATAT conference in Edinburgh. Since those very early days, the conference series has gone from strength to strength and this volume represents the latest in a series of?ve rigorously refereed volumes which showcase a broad spectrum of ground-breaking timetabling research across a very wide range of timetabling problems and applications. Timetabling is an area that unites a number of disparate?elds and which cuts across a number of diverse academic disciplines. While the most obvious instances of timetabling occur in educational institutions, timetabling also - pears in sports applications, transportation planning, project scheduling, and many other?elds. Viewing timetabling as a unifying theme enables researchers fromthesevariousareastolearnfromeachotherandtoextendtheirown- searchandpracticeinnewandinnovativeways. Thisvolumecontinuesthetrend of the conference series to extend the de?nition of timetabling beyond its edu- tional roots. In this volume, seven of the 19 papers involve domains other than education. Of course, educationaltimetabling remains at the coreof timetabling research, and the papers in this volume represent the full range of this area including exam timetabling, room scheduling, and class rostering.