Über den Autor
John Hooker is a leading researcher in both the Optimization and Constraint Programming research communities. He has been an instrumental principal for this integration, and over the years, he has given numerous presentations and tutorials on the integration of these two areas. It is felt by many in the field that the future Optimization courses will increasingly be taught from this integrated framework.
Prof. Hooker has published two earlier books on the methodologies of Optimization and Constraint Programming. The first was Optimization Methods for Logical Inference (Wiley 1999) and the second was Logic Based Methods for Optimization: Combining Optimization and Constraints Satisfaction (Wiley 2000). This book will be his third book in this evolving area and it is the book that completes the process of integrating these two methodologies into a single set of methods
Preface.- Introduction.- Search.- The solution process.- Branching search.- Constraint-directed search.- Local search.- Bibliographic notes.- Inference.- Completeness.- Inference duality.- Linear inequalities.- General inequality constraints.- Propositional logic.- 0-1 linear inequalities.- Integer linear inequalities.- The element constraint.- The all-different constraint.- The cardinality and Nvalues constraints.- The circuit constraint.- The stretch constraint.- Disjunctive scheduling.- Cumulative scheduling.- Bibliographic notes.- Relaxation.- Relaxation duality.- Linear inequalities.- Semicontinuous piecewise linear functions.- 0-1 linear inequalities.- Integer linear inequalities.- Lagrangean and surrogate relaxations.- Disjunctions of linear systems.- Disjunctions of nonlinear systems.- MILP modeling.- Propositional Logic.- The element constraint.- The all-different constraint.- The cardinality constraint.- The circuit constraint.- Disjunctive scheduling.- Cumulative scheduling.- Bibliographic notes.- Dictionary of constraints.- References.- Index.
This book integrates the key concepts of mathematical programming (MP) and constraint programming (CP) into a unified framework that allows them to be generalized and combined. The unification of MP and CP creates optimization methods that have much greater modeling power, increased computational speed, and a sizeable reduction computational coding. This integration along with constraint programming being incorporated into a number of programming languages, brings the field a step closer to being able to simply state a problem and having the computer solve it.
PEACH (Personal Experience with Active Cultural Heritage) is a large, interdisciplinary development project that explores the use of novel technologies for physical museum visits. Put together by editors and authors who are leading experts on the underlying AI technologies and their application, this book provides a comprehensive survey of the subject. Coverage includes reports on mobile guides, infrastructure and user modeling, the use of stationary devices, collaborative storytelling, 3D modeling, evaluation and usability, and future perspectives. It will be of benefit to AI researchers engaged with interface design as well as practitioners in the area of cultural heritage support and marketing.