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...

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Κύριος συγγραφέας: Wahbi, Mohamed
Μορφή: Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: London : ISTE ; 2013.
Hoboken : Wiley, 2013.
Σειρά:FOCUS Series.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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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. 
264 1 |a London :  |b ISTE ;  |c 2013. 
264 1 |a Hoboken :  |b Wiley,  |c 2013. 
300 |a 1 online resource (xvi, 157 pages). 
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490 1 |a FOCUS Series 
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. 
650 0 |a Computer algorithms. 
650 0 |a Image processing  |x Digital techniques. 
650 0 |a Neural computers. 
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650 7 |a Image processing  |x Digital techniques.  |2 fast  |0 (OCoLC)fst00967508 
650 7 |a Neural computers.  |2 fast  |0 (OCoLC)fst01036251 
653 |a Multi-User. 
655 4 |a Electronic books. 
655 7 |a Electronic books.  |2 local 
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://doi.org/10.1002/9781118753620  |z Full Text via HEAL-Link 
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