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LEADER: 03221mam a2200325 a 4500
001 1392001
005 20220602023918.0
008 931004t19931993enka b 001 0 eng d
010 $agb 93066472
015 $aGB93-66472
020 $a0127016104
035 $a(OCoLC)ocm28932191
035 $9AHQ2213CU
035 $a1392001
040 $aEUE$cEUE$dCIN$dUKM
082 04 $a006.33$220
100 1 $aTsang, Edward.$0http://id.loc.gov/authorities/names/no94034626
245 10 $aFoundations of constraint satisfaction /$cEdward Tsang.
260 $aLondon ;$aSan Diego :$bAcademic Press,$c[1993], ©1993.
300 $axviii, 421 pages :$billustrations ;$c24 cm
336 $atext$2rdacontent
337 $aunmediated$2rdamedia
338 $avolume$2rdacarrier
504 $aIncludes bibliographical references (p. [383]-403) and index.
505 2 $aCh. 1. Introduction. 1.1. What is a constraint satisfaction problem? 1.2. Formal Definition of the CSP. 1.3. Constraint Representation and Binary CSPs. 1.4. Graph-related Concepts. 1.5. Examples and Applications of CSPs. 1.6. Constraint Programming. 1.7. Structure Of Subsequent Chapters -- Ch. 2. CSP solving - An overview. 2.2. Problem Reduction. 2.3. Searching For Solution Tuples. 2.4. Solution Synthesis. 2.5. Characteristics of Individual CSPs -- Ch. 3. Fundamental concepts in the CSP. 3.2. Concepts Concerning Satisfiability and Consistency. 3.3. Relating Consistency to Satisfiability. 3.4. (i, j)-consistency. 3.5. Redundancy of Constraints. 3.6. More Graph-related Concepts -- Ch. 4. Problem reduction. 4.2. Node and Arc-consistency Achieving Algorithms. 4.3. Path-consistency Achievement Algorithms. 4.4. Post-conditions of PC Algorithms. 4.5. Algorithm for Achieving k-consistency. 4.6. Adaptive-consistency. 4.7. Parallel/Distributed Consistency Achievement.
505 0 $aCh. 5. Basic search strategies for solving CSPs. 5.2. General Search Strategies. 5.3. Lookahead Strategies. 5.4. Gather-information-while-searching Strategies. 5.5. Hybrid Algorithms and Truth Maintenance. 5.6. Comparison of Algorithms -- Ch. 6. Search orders in CSPs. 6.2. Ordering of Variables in Searching. 6.3. Ordering of Values in Searching. 6.4. Ordering of Inferences in Searching -- Ch. 7. Exploitation of problem-specific features. 7.2. Problem Decomposition. 7.3. Recognition and Searching in k-trees. 7.4. Problem Reduction by Removing Redundant Constraints. 7.5. Cycle-cutsets, Stable Sets and PseudoT̲reeS̲earch. 7.6. The Tree-clustering Method. 7.7. j-width and Backtrack-bounded Search. 7.8. CSPs with Binary Numerical Constraints -- Ch. 8. Stochastic search methods for CSPs. 8.2. Hill-climbing. 8.3. Connectionist Approach -- Ch. 9. Solution synthesis. 9.2. Freuder's Solution Synthesis Algorithm. 9.3. Seidel's Invasion Algorithm. 9.4. The Essex Solution Synthesis Algorithms.
505 0 $a9.5. When to Synthesize Solutions -- Ch. 10. Optimization in CSPs. 10.2. The Constraint Satisfaction Optimization Problem. 10.3. The Partial Constraint Satisfaction Problem.
650 0 $aConstraints (Artificial intelligence)$0http://id.loc.gov/authorities/subjects/sh92001623
653 0 $aArtificial intelligence$aRelated to$aMind
852 00 $boff,psy$hQ340$i.T78 1993g