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• Introduction Preference Modeling and Reasoning Uncertainty in Preference Reasoning Aggregating Preferences Stable Marriage Problems.
• (source: Nielsen Book Data)

Computational social choice is an expanding field that merges classical topics like economics and voting theory with more modern topics like artificial intelligence, multiagent systems, and computational complexity. This book provides a concise introduction to the main research lines in this field, covering aspects such as preference modelling, uncertainty reasoning, social choice, stable matching, and computational aspects of preference aggregation and manipulation. The book is centered around the notion of preference reasoning, both in the single-agent and the multi-agent setting. It presents the main approaches to modeling and reasoning with preferences, with particular attention to two popular and powerful formalisms, soft constraints and CP-nets. The authors consider preference elicitation and various forms of uncertainty in soft constraints. They review the most relevant results in voting, with special attention to computational social choice. Finally, the book considers preferences in matching problems. The book is intended for students and researchers who may be interested in an introduction to preference reasoning and multi-agent preference aggregation, and who want to know the basic notions and results in computational social choice.
(source: Nielsen Book Data)

• Introduction Preference Modeling and Reasoning Uncertainty in Preference Reasoning Aggregating Preferences Stable Marriage Problems.
• (source: Nielsen Book Data)

Computational social choice is an expanding field that merges classical topics like economics and voting theory with more modern topics like artificial intelligence, multiagent systems, and computational complexity. This book provides a concise introduction to the main research lines in this field, covering aspects such as preference modelling, uncertainty reasoning, social choice, stable matching, and computational aspects of preference aggregation and manipulation. The book is centered around the notion of preference reasoning, both in the single-agent and the multi-agent setting. It presents the main approaches to modeling and reasoning with preferences, with particular attention to two popular and powerful formalisms, soft constraints and CP-nets. The authors consider preference elicitation and various forms of uncertainty in soft constraints. They review the most relevant results in voting, with special attention to computational social choice. Finally, the book considers preferences in matching problems. The book is intended for students and researchers who may be interested in an introduction to preference reasoning and multi-agent preference aggregation, and who want to know the basic notions and results in computational social choice.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 9th International Conference on Principles and Practice of Constraint Programming, CP 2003, held in Kinsale, Ireland in September/October 2003.The 48 revised full papers and 34 revised short papers presented together with 4 invited papers and 40 abstracts of contributions to the CP 2003 doctoral program were carefully reviewed and selected from 181 submissions. A wealth of recent results in computing with constraints is addressed ranging from foundational and methodological issues to solving real-world problems in a variety of application fields.
(source: Nielsen Book Data)

• Social Choice Theory.- A Complete Conclusion-Based Procedure for Judgment Aggregation.- A Geometric Approach to Paradoxes of Majority Voting in Abstract Aggregation Theory.- Manipulating Tournaments in Cup and Round Robin Competitions.- Iterated Majority Voting.- Committee Selection with a Weight Constraint Based on Lexicographic Rankings of Individuals.- The Effects of Noise and Manipulation on the Accuracy of Collective Decision Rules.- Subset Weight Maximization with Two Competing Agents.- The Complexity of Probabilistic Lobbying.- On the Complexity of Efficiency and Envy-Freeness in Fair Division of Indivisible Goods with Additive Preferences.- On Low-Envy Truthful Allocations.- On Multi-dimensional Envy-Free Mechanisms.- Stable Rankings in Collective Decision Making with Imprecise Information.- Finding Best k Policies.- Multiple Criteria Decision Analysis.- New Hybrid Recommender Approaches: An Application to Equity Funds Selection.- A Prescriptive Approach for Eliciting Imprecise Weight Statements in an MCDA Process.- Inverse Analysis from a Condorcet Robustness Denotation of Valued Outranking Relations.- Directional Decomposition of Multiattribute Utility Functions.- The Possible and the Necessary for Multiple Criteria Group Decision.- Preferences in an Open World.- Extending Argumentation to Make Good Decisions.- Building Consistent Pairwise Comparison Matrices over Abelian Linearly Ordered Groups.- Aggregating Interval Orders by Propositional Optimization.- Circular Representations of a Valued Preference Matrix.- Decision under Uncertainty.- The First Belief Dominance: A New Approach in Evidence Theory for Comparing Basic Belief Assignments.- Interpreting GUHA Data Mining Logic in Paraconsistent Fuzzy Logic Framework.- Insuring Risk-Averse Agents.- Adversarial Risk Analysis: Applications to Basic Counterterrorism Models.- Game Theory without Decision-Theoretic Paradoxes.- Ranking Methods Based on Dominance Measures Accounting for Imprecision.- Optimisation.- Optimizing the Hurwicz Criterion in Decision Trees with Imprecise Probabilities.- Axioms for a Class of Algorithms of Sequential Decision Making.- Algorithmic Aspects of Scenario-Based Multi-stage Decision Process Optimization.- Choquet Optimization Using GAI Networks for Multiagent/Multicriteria Decision-Making.- Compact Preference Representation in Stable Marriage Problems.- Neuroevolutionary Inventory Control in Multi-Echelon Systems.- Determining a Minimum Spanning Tree with Disjunctive Constraints.- Learning.- An Inductive Methodology for Data-Based Rules Building.- A Framework for Designing a Fuzzy Rule-Based Classifier.- Anytime Self-play Learning to Satisfy Functional Optimality Criteria.
• (source: Nielsen Book Data)

Algorithmic decision theory is a new interdisciplinary research area that aims to bring together researchers from di?erent ?elds such as decision theory, d- crete mathematics, theoretical computer science and arti?cial intelligence, in order to improve decision support in the presence of massive databases, com- natorial structures, partial and/or uncertain information and distributed, p- sibly interoperating, decision makers. Such problems arise in several real-world decision-making scenarios such as humanitarian logistics, epidemiology, risk assessment and management, e-government, electronic commerce, and rec- mender systems. In 2007, the EU-funded COST Action IC0602 on Algorithmic Decision T- ory was started, networking a number of researchers and research laboratories around Europe (and beyond). The COST Action IC0602 now gathers over 100 participants from more than 30 countries (including non-COST countries such as Australia, South Africa and the USA). For more details see www.algodec.org. Within the Action, and in cooperation with the EURO Working Group on Pr- erences, itwasdecidedtostartanewseriesofconferencesonalgorithmicdecision theory, the goal being to provide a forum for researchers interested in this area. This volume contains the papers presented at ADT 2009, the ?rst Inter- tional Conference on Algorithmic Decision Theory. The conference was held in SanServolo, a smallislandintheVenetianLagoon, onOctober20-23,2009.The programoftheconferenceincludedoralpresentations, posters, invitedtalks, and tutorials (for more information see www.adt2009.org).
(source: Nielsen Book Data)

• From Marriages to Coalitions: A Soft CSP Approach.- Solving CSPs with Naming Games.- An Efficient Decision Procedure for Functional Decomposable Theories Based on Dual Constraints.- Challenges in Constraint-Based Analysis of Hybrid Systems.- From Rules to Constraint Programs with the Rules2CP Modelling Language.- Combining Symmetry Breaking and Global Constraints.- Iterative Flattening Search on RCPSP/max Problems: Recent Developments.- Robust Solutions in Unstable Optimization Problems.- IDB-ADOPT: A Depth-First Search DCOP Algorithm.
• (source: Nielsen Book Data)

Constraint programming (CP) is a powerful programming paradigm for the declarativedescription and the e?ective solving of largecombinatorialproblems. Basedonastrongtheoreticalfoundation, itisincreasinglyattractingcommercial interest. Since the 1990s, CP has been deployed by many industry leaders, in particular to model heterogeneous optimization and satisfaction problems. - amples of application domains where such problems naturally arise, and where constraint programming has made a valuable contribution, are scheduling, p- duction planning, communication networks, routing, planning of satellite m- sions, robotics, and bioinformatics. This volumecontainsthe papers selectedfor the post-proceedingsof the13th International Workshop on Constraint Solving and Constraint Logic Progr- ming(CSCLP2008)heldduringJune18-20,2008inRome, Italy.Thisworkshop was organized as the 13th meeting of the working group on Constraints of the EuropeanResearchConsortiumforInformaticsandMathematics(ERCIM), c- tinuing a series of workshops organized since the creation of the working group in 1997. A selection of papers of these annual workshops plus some additional contributions have been published since 2002 in a series of volumes which ill- trate the evolutioninthe ?eld, under the title "RecentAdvances in Constraints" in the Lecture Notes in Arti?cial Intelligence series.
(source: Nielsen Book Data)

• A Comparison of the Notions of Optimality in Soft Constraints and Graphical Games
• Temporal Reasoning in Nested Temporal Networks with Alternatives
• SCLP for Trust Propagation in Small-World Networks
• Improving ABT Performance by Adding Synchronization Points
• On the Integration of Singleton Consistencies and Look-Ahead Heuristics
• Combining Two Structured Domains for Modeling Various Graph Matching Problems
• Quasi-Linear-Time Algorithms by Generalisation of Union-Find in CHR
• Preference-Based Problem Solving for Constraint Programming
• Generalizing Global Constraints Based on Network Flows
• A Global Filtration for Satisfying Goals in Mutual Exclusion Networks.

This book constitutes the thoroughly refereed and extended post-workshop proceedings of the 12th Annual ERCIM International Workshop on Constraint Solving and Constraint Logic Programming, CSCLP 2007, held in Rocquencourt, France, in June 2007. The 10 revised full papers presented were carefully reviewed and selected from 16 initial submissions. The papers address all aspects of constraint and logic programming, including foundational issues, implementation techniques, new applications as well as teaching issues. Particular emphasis is placed on assessing the current state of the art and identifying future directions.
(source: Nielsen Book Data)

• Foreword (Ugo Montanari)
• Part I : Foundations Chapter 1. Introduction (Francesca Rossi, Peter van Beek, Toby Walsh)
• Chapter 2. Constraint Satisfaction: An Emerging Paradigm (Eugene C. Freuder, Alan K. Mackworth) Chapter 3. Constraint Propagation (Christian Bessiere)
• Chapter 4. Backtracking Search Algorithms (Peter van Beek) Chapter 5. Local Search Methods (Holger H. Hoos, Edward Tsang) Chapter 6. Global Constraints (Willem-Jan van Hoeve, Irit Katriel) Chapter 7. Tractable Structures for CSPs (Rina Dechter) Chapter 8. The Complexity of Constraint Languages (David Cohen, Peter Jeavons)
• Chapter 9. Soft Constraints (Pedro Meseguer, Francesca Rossi, Thomas Schiex) Chapter 10. Symmetry in Constraint Programming (Ian P. Gent, Karen E. Petrie, Jean-Francois Puget) Chapter 11. Modelling (Barbara M. Smith) Part II : Extensions, Languages, and Applications Chapter 12. Constraint Logic Programming (Kim Marriott, Peter J. Stuckey, Mark Wallace) Chapter 13. Constraints in Procedural and Concurrent Languages (Thom Fruehwirth, Laurent Michel, Christian Schulte) Chapter 14. Finite Domain Constraint Programming Systems (Christian Schulte, Mats Carlsson) Chapter 15. Operations Research Methods in Constraint Programming (John Hooker) Chapter 16. Continuous and Interval Constraints(Frederic Benhamou, Laurent Granvilliers) Chapter 17. Constraints over Structured Domains (Carmen Gervet) Chapter 18. Randomness and Structure (Carla Gomes, Toby Walsh) Chapter 19. Temporal CSPs (Manolis Koubarakis) Chapter 20. Distributed Constraint Programming (Boi Faltings) Chapter 21. Uncertainty and Change (Kenneth N. Brown, Ian Miguel) Chapter 22. Constraint-Based Scheduling and Planning (Philippe Baptiste, Philippe Laborie, Claude Le Pape, Wim Nuijten) Chapter 23. Vehicle Routing (Philip Kilby, Paul Shaw) Chapter 24. Configuration (Ulrich Junker) Chapter 25. Constraint Applications in Networks (Helmut Simonis) Chapter 26. Bioinformatics and Constraints (Rolf Backofen, David Gilbert).
• (source: Nielsen Book Data)

Constraint programming is a powerful paradigm for solving combinatorial search problems that draws on a wide range of techniques from artificial intelligence, computer science, databases, programming languages, and operations research. Constraint programming is currently applied with success to many domains, such as scheduling, planning, vehicle routing, configuration, networks, and bioinformatics. The aim of this handbook is to capture the full breadth and depth of the constraint programming field and to be encyclopedic in its scope and coverage. While there are several excellent books on constraint programming, such books necessarily focus on the main notions and techniques and cannot cover also extensions, applications, and languages. The handbook gives a reasonably complete coverage of all these lines of work, based on constraint programming, so that a reader can have a rather precise idea of the whole field and its potential. Of course each line of work is dealt with in a survey-like style, where some details may be neglected in favor of coverage. However, the extensive bibliography of each chapter will help the interested readers to find suitable sources for the missing details. Each chapter of the handbook is intended to be a self-contained survey of a topic, and is written by one or more authors who are leading researchers in the area. The intended audience of the handbook is researchers, graduate students, higher-year undergraduates and practitioners who wish to learn about the state-of-the-art in constraint programming. No prior knowledge about the field is necessary to be able to read the chapters and gather useful knowledge. Researchers from other fields should find in this handbook an effective way to learn about constraint programming and to possibly use some of the constraint programming concepts and techniques in their work, thus providing a means for a fruitful cross-fertilization among different research areas. The handbook is organized in two parts. The first part covers the basic foundations of constraint programming, including the history, the notion of constraint propagation, basic search methods, global constraints, tractability and computational complexity, and important issues in modeling a problem as a constraint problem. The second part covers constraint languages and solver, several useful extensions to the basic framework (such as interval constraints, structured domains, and distributed CSPs), and successful application areas for constraint programming. It covers the whole field of constraint programming. It contains survey-style chapters, and five chapters on applications.
(source: Nielsen Book Data)

• Background
• Introduction to Prolog
• About Prolog, present and future
• Types, modes and so much more ⁰́₃ the Prolog way
• Manuel Carro Prolog as a Knowledge Representation Language
• Prolog: Past, Present, and Future
• Writing Correct Prolog Programs
• Demonstrating Multiple Prolog Programming Techniques through a Single Operation
• Logical Semantics for Prolog
• The Janus System: A Bridge to New Prolog Applications
• Teaching Prolog
• Some Thoughts on How to Teach Prolog
• Simultaneously Teaching Mathematics and Prolog in School Curricula: A Mutual Benefit
• Logic Programming at Elementary School: Why, what and how should we teach Logic Programming to children
• Prolog Education in Selected High Schools in Bulgaria
• Introducing Prolog in Language-Informed Ways
• Tools for teaching Prolog
• Teaching Prolog with Active Logic Documents
• Simply Logical - The First Three Decades
• Prolog-based languages and systems Dynamic Logic Programming
• Combining Prolog and Imperative Computing in LPS
• Quintero Ergo: A Quest for Declarativity in Logic Programming
• Prolog applications: finalists for the Colmerauer prize ProB: Harnessing the Power of Prolog to Bring Formal Models and Mathematics to Life
• Pacioli: a PROLOG system for financial report validation
• Logic Model Processing
• Symbium: Using logic programming to streamline citizen-to-government interactions
• PROLEG: Practical Legal Reasoning System
• Contributed Prolog applications
• Logical English for Law and Education
• Exploiting Logic Programming for Runtime Verification: Current and Future Perspectives
• Prolog meets Biology
• Prolog in Automated Reasoning in Geometry
• Logic-based Explainable and Incremental Machine Learning
• Reflections on Automation, Learnability and Expressiveness in Logic-based Programming Languages
• Prolog for Scientific Explanation
• Machines as Thought Partners: Reflections on 50 Years of Prolog

This volume was motivated by the Year of Prolog initiative, launched to celebrate the 50th anniversary of the emergence of Prolog through the work of Alain Colmerauer⁰́₉s team in Marseille. The volume editors, authors, and scientific advisors and reviewers have been the leading researchers and programmers in this field over decades, and the book represents an excellent overview of the field, its successes, and its future. After a first chapter that gently introduces the Prolog programming language using examples, the next 7 papers discuss general views of the language, possible extensions for the future, and how Prolog can generally be used to solve problems; the next 5 papers explore ideas and experiences of teaching Prolog programming and then 2 papers discuss technology that has been developed for help in that teaching; the next 3 papers describe new languages based on Prolog which show future directions for logic programming; the next 5 chapters explain the applications that were the finalists for the 2022 Alain Colmerauer Prize; and the final 8 papers describe applications developed using the Prolog language, demonstrating the language⁰́₉s range