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Algorithms and ordering heuristics for distributed constraint satisfaction problems /
DisCSP (Distributed Constraint Satisfaction Problem) is a general framework for solving distributed problems arising in Distributed Artificial Intelligence. A wide variety of problems in artificial intelligence are solved using the constraint satisfaction problem paradigm. However, there are several...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London : Hoboken :
ISTE ; Wiley,
2013.
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| Colección: | FOCUS Series.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Wahbi, Mohamed. | |
| 245 | 1 | 0 | |a Algorithms and ordering heuristics for distributed constraint satisfaction problems / |c Mohamed Wahbi. |
| 260 | |a London : |b ISTE ; |a Hoboken : |b Wiley, |c 2013. | ||
| 300 | |a 1 online resource (xvi, 157 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 505 | 0 | |a Title Page; Contents; Preface; Introduction; Part 1: Background on Centralized and Distributed Constraint Reasoning; Chapter 1. Constraint Satisfaction Problems; 1.1. Centralized constraint satisfaction problems; 1.1.1. Preliminaries; 1.1.2. Examples of CSPs; 1.2. Algorithms and techniques for solving centralized CSPs; 1.2.1. Algorithms for solving centralized CSPs; 1.2.2. Variable ordering heuristics for centralized CSPs; 1.3. Summary; Chapter 2. Distributed Constraint Satisfaction Problems; 2.1. Distributed constraint satisfaction problems; 2.1.1. Preliminaries; 2.1.2. Examples of DisCSPs. | |
| 505 | 8 | |a 2.1.3. Distributed meeting scheduling problem (DisMSP)2.1.4. Distributed sensor network problem (SensorDCSP); 2.2. Methods for solving DisCSPs; 2.2.1. Synchronous search algorithms on DisCSPs; 2.2.2. Asynchronous search algorithms on DisCSPs; 2.2.3. Dynamic ordering heuristics on DisCSPs; 2.2.4. Maintaining arc consistency on DisCSPs; 2.3. Summary; Part 2: Synchronous Search Algorithms for DisCSPs; Chapter 3. Nogood-based Asynchronous Forward Checking (AFC-ng); 3.1. Introduction; 3.2. Nogood-based asynchronous forward checking; 3.2.1. Description of the algorithm. | |
| 505 | 8 | |a 3.2.2. A simple example of the backtrack operation on AFC-like algorithms3.3. Correctness proofs; 3.4. Experimental evaluation; 3.4.1. Uniform binary random DisCSPs; 3.4.2. Distributed sensor-target problems; 3.4.3. Distributed meeting scheduling problems; 3.4.4. Discussion; 3.5. Summary; Chapter 4. Asynchronous Forward-Checking Tree(AFC-tree); 4.1. Introduction; 4.2. Pseudo-tree ordering; 4.3. Distributed depth-first search tree construction; 4.4. The AFC-tree algorithm; 4.4.1. Description of the algorithm; 4.5. Correctness proofs; 4.6. Experimental evaluation. | |
| 505 | 8 | |a 4.6.1. Uniform binary random DisCSPs4.6.2. Distributed sensor-target problems; 4.6.3. Distributed meeting scheduling problems; 4.6.4. Discussion; 4.7. Other related works; 4.8. Summary; Chapter 5. Maintaining Arc Consistency Asynchronously in Synchronous Distributed Search; 5.1. Introduction; 5.2. Maintaining arc consistency; 5.3. Maintaining arc consistency asynchronously; 5.3.1. Enforcing AC using del messages (MACA-del); 5.3.2. Enforcing AC without additional kind of message (MACA-not); 5.4. Theoretical analysis; 5.5. Experimental results; 5.5.1. Discussion; 5.6. Summary. | |
| 505 | 8 | |a Part 3: Asynchronous Search Algorithms and Ordering Heuristics for DisCSPsChapter 6. Corrigendum to "Min-Domain Retroactive Ordering for Asynchronous Backtracking"; 6.1. Introduction; 6.2. Background; 6.3. ABT_DO-Retro may not terminate; 6.4. The right way to compare orders; 6.5. Summary; Chapter 7. Agile Asynchronous Backtracking(Agile-ABT); 7.1. Introduction; 7.2. Introductory material; 7.2.1. Reordering details; 7.2.2. The backtracking target; 7.2.3. Decreasing termination values; 7.3. The algorithm; 7.4. Correctness and complexity; 7.5. Experimental results. | |
| 500 | |a 7.5.1. Uniform binary random DisCSPs. | ||
| 520 | |a DisCSP (Distributed Constraint Satisfaction Problem) is a general framework for solving distributed problems arising in Distributed Artificial Intelligence. A wide variety of problems in artificial intelligence are solved using the constraint satisfaction problem paradigm. However, there are several applications in multi-agent coordination that are of a distributed nature. In this type of application, the knowledge about the problem, that is, variables and constraints, may be logically or geographically distributed among physical distributed agents. | ||
| 588 | 0 | |a Print version record. | |
| 504 | |a Includes bibliographical references and index. | ||
| 546 | |a English. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Computer algorithms. | |
| 650 | 0 | |a Image processing |x Digital techniques. | |
| 650 | 0 | |a Neural computers. | |
| 650 | 2 | |a Algorithms | |
| 650 | 6 | |a Algorithmes. | |
| 650 | 6 | |a Traitement d'images |x Techniques numériques. | |
| 650 | 6 | |a Ordinateurs neuronaux. | |
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| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Optics. |2 bisacsh | |
| 650 | 7 | |a Computer algorithms |2 fast | |
| 650 | 7 | |a Image processing |x Digital techniques |2 fast | |
| 650 | 7 | |a Neural computers |2 fast | |
| 653 | |a Multi-User. | ||
| 758 | |i has work: |a Algorithms and ordering heuristics for distributed constraint satisfaction problems (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFDPRR4CmhkcfcMhQwvd33 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Wahbi, Mohamed. |t Algorithms and ordering heuristics for distributed constraint satisfaction problems. |d Hoboken : Wiley, ©2013 |z 9781848215948 |w (DLC) 2013937865 |
| 830 | 0 | |a FOCUS Series. | |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1272348 |z Texto completo |
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