LEADER 04629nam 2200577 450 001 9910786640403321 005 20230120014728.0 010 $a1-4832-7197-8 035 $a(CKB)3710000000200668 035 $a(EBL)1888440 035 $a(SSID)ssj0001454760 035 $a(PQKBManifestationID)11792480 035 $a(PQKBTitleCode)TC0001454760 035 $a(PQKBWorkID)11498255 035 $a(PQKB)11088429 035 $a(MiAaPQ)EBC1888440 035 $a(EXLCZ)993710000000200668 100 $a20150112h19801980 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAlgorithmic graph theory and perfect graphs /$fMartin Charles Golumbic 210 1$aNew York, New York ;$aLondon, England :$cAcademic Press,$d1980. 210 4$dİ1980 215 $a1 online resource (307 p.) 225 1 $aComputer Science and Applied Mathematics 300 $aDescription based upon print version of record. 311 $a1-322-47765-5 311 $a0-12-289260-7 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aFront Cover; Algorithmic Graph Theory and Perfect Graphs; Copyright Page; Dedication; Table of Contents; Foreword; Preface; Acknowledgments; List of Symbols; Chapter 1. Graph Theoretic Foundations ; 1. Basic Definitions and Notations; 2. Intersection Graphs; 3. Interval Graphs-A Sneak Preview of the Notions Coming Up; 4. Summary; Exercises; Bibliography; Chapter 2. The Design of Efficient Algorithms; 1. The Complexity of Computer Algorithms; 2. Data Structures; 3. How to Explore a Graph; 4. Transitive Tournaments and Topological Sorting; Exercises; Bibliography; Chapter 3. Perfect Graphs 327 $a1. The Star of the Show2. The Perfect Graph Theorem; 3. p-Critical and Partitionable Graphs; 4. A Polyhedral Characterization of Perfect Graphs; 5. A Polyhedral Characterization of p-Critical Graphs; 6. The Strong Perfect Graph Conjecture; Exercises; Bibliography; Chapter 4. Triangulated Graphs; 1. Introduction; 2. Characterizing Triangulated Graphs; 3. Recognizing Triangulated Graphs by Lexicographic Breadth-First Search; 4. The Complexity of Recognizing Triangulated Graphs; 5. Triangulated Graphs as Intersection Graphs; 6. Triangulated Graphs Are Perfect 327 $a7. Fast Algorithms for the COLORING, CLIQUE, STABLE SET, and CLIQUE-COVER Problems on Triangulated GraphsExercises; Bibliography; Chapter 5. Comparability Graphs; 1. ?-Chains and Implication Classes; 2. Uniquely Partially Orderable Graphs; 3. The Number of Transitive Orientations; 4. Schemes and G-Decompositions-An Algorithm for Assigning Transitive Orientations; 5. The ?*-Matroid of a Graph; 6. The Complexity of Comparability Graph Recognition; 7. Coloring and Other Problems on Comparability Graphs; 8. The Dimension of Partial Orders; Exercises; Bibliography; Chapter 6. Split Graphs 327 $a1. An Introduction to Chapters 6-8: Interval, Permutation, and Split Graphs2. Characterizing Split Graphs; 3. Degree Sequences and Split Graphs; Exercises; Bibliography; Chapter 7. Permutation Graphs; 1. Introduction; 2. Characterizing Permutation Graphs; 3. Permutation Labelings; 4. Applications; 5. Sorting a Permutation Using Queues in Parallel; Exercises; Bibliography; Chapter 8. Interval Graphs; 1. How It All Started; 2. Some Characterizations of Interval Graphs; 3. The Complexity of Consecutive 1's Testing; 4. Applications of Interval Graphs; 5. Preference and Indifference 327 $a6. Circular-Arc GraphsExercises; Bibliography; Chapter 9. Superperfect Graphs; 1. Coloring Weighted Graphs; 2. Superperfection; 3. An Infinite Class of Superperfect Noncomparability Graphs; 4. When Does Superperfect Equal Comparability?; 5. Composition of Superperfect Graphs; 6. A Representation Using the Consecutive 1's Property; Exercises; Bibliography; Chapter 10. Threshold Graphs; 1. The Threshold Dimension; 2. Degree Partition of Threshold Graphs; 3. A Characterization Using Permutations; 4. An Application to Synchronizing Parallel Processes; Exercises; Bibliography 327 $aChapter 11. Not So Perfect Graphs 330 $aAlgorithmic Graph Theory and Perfect Graphs 410 0$aComputer science and applied mathematics. 606 $aPerfect graphs 615 0$aPerfect graphs. 676 $a511/.5 700 $aGolumbic$b Martin Charles$0104097 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910786640403321 996 $aAlgorithmic graph theory and perfect graphs$9437139 997 $aUNINA LEADER 03812nam 22007095 450 001 9910913794103321 005 20241203115306.0 010 $a9789819792863 010 $a981979286X 024 7 $a10.1007/978-981-97-9286-3 035 $a(CKB)36812710000041 035 $a(MiAaPQ)EBC31812223 035 $a(Au-PeEL)EBL31812223 035 $a(DE-He213)978-981-97-9286-3 035 $a(EXLCZ)9936812710000041 100 $a20241203d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAI-Driven Mechanism Design /$fby Weiran Shen, Pingzhong Tang, Song Zuo 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (135 pages) 225 1 $aArtificial Intelligence: Foundations, Theory, and Algorithms,$x2365-306X 311 08$a9789819792856 311 08$a9819792851 327 $aChapter 1. Introduction -- Chapter 2. Multi-Dimensional Mechanism Design via AI-Driven Approaches -- Chapter 3. Dynamic Mechanism Design via AI-Driven Approaches -- Chapter 4. Multi-Objective Mechanism Design via AI-Driven Approaches -- Chapter 5. Summary and Future Directions. 330 $aDue to its huge success in industry, mechanism design has been one of the central research topics at the interface of economics and computer science. However, despite decades of effort, there are still numerous challenges, in terms of both theory and applications. These include the problem of how to design mechanisms for selling multiple items, dynamic auctions, and balancing multiple objectives, given the huge design space and buyer strategy space; and the fact that in practice, the most widely applied auction format (the generalized second price auction) is neither truthful nor optimal. Furthermore, many theoretical results are based upon unrealistic assumptions that do not hold in real applications. This book presents the AI-driven mechanism design framework, which aims to provide an alternative way of dealing with these problems. The framework features two abstract models that interact with each other: the agent model and the mechanism model. By combining AI techniques with mechanism design theory, it solves problems that cannot be solved using tools from either domain alone. For example, it can reduce the mechanism space significantly, build more realistic buyer models, and better balance different objectives. The book focuses on several aspects of mechanism design and demonstrates that the framework is useful in both theoretical analysis and practical applications. 410 0$aArtificial Intelligence: Foundations, Theory, and Algorithms,$x2365-306X 606 $aComputational intelligence 606 $aElectronic commerce 606 $aMultiagent systems 606 $aMachine learning 606 $aGame theory 606 $aComputational Intelligence 606 $ae-Commerce and e-Business 606 $aMultiagent Systems 606 $aMachine Learning 606 $aGame Theory 615 0$aComputational intelligence. 615 0$aElectronic commerce. 615 0$aMultiagent systems. 615 0$aMachine learning. 615 0$aGame theory. 615 14$aComputational Intelligence. 615 24$ae-Commerce and e-Business. 615 24$aMultiagent Systems. 615 24$aMachine Learning. 615 24$aGame Theory. 676 $a006.3 700 $aShen$b Weiran$01777604 701 $aTang$b Pingzhong$01777605 701 $aZuo$b Song$01777606 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910913794103321 996 $aAI-Driven Mechanism Design$94299212 997 $aUNINA