bibliography - springer978-3-322-90178-1/1.pdf · bibliography [1] adelson-velsky, g ......

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243 Bibliography [1] Adelson-Velsky, G. M. and Arlazarov, V. L. and Donskoy, M. V. (1979). Algorithms of adaptive search. Machine Intelligence 9, J. E. Hayes and D. Michie and L.1. Mikulich (eds.), pp. 373-384, Ellis Horwood, ISBN 0-470-26714-3/0-853-12112-5. [2] Adelson-Velsky, G. M. and Arlazarov, V. L. and Bitman, A. R. and Zhivotovsky, A. A. and Uskov, A. V. (1969). Programming a computer to play chess. 1st Summer School on Mathematical Programming, Proceedings Vo!' II, pp. 216-252 [another translation of the Russian original appeared in Russian Mathematical Surveys, Vo!' 25, pp. 221-262, London Mathematical Society, 1970]. [3] Akl, S. G. and Newborn, M. M. (1977). The principal continuation and the killer heuristic. ACM '77 National Conference, Proceedings, pp. 466-473, ACM Press. [4] Alth6fer, 1. (1991). Data compression using an intelligent generator: The storage of chess games as an example. Artificial Intelligence, Vo!' 52, No.1, pp. 109-114. [5] Alth6fer, I. (1990). Compressing chess games with the help of a fast deterministic chess program. ICCA Journal, Vo!' 13, No.4, pp. 200-203. [6] Anantharaman, T. S. (1997). Evaluation tuning for computer chess: Linear discrim- inant methods. ICCA Journal, Vo!' 20, No.4, pp. 224-242. [7] Anantharaman, T. S. (1991). Extension heuristics. ICCA Journal, Vo!' 14, No.2, pp.47-65. [8] Anantharaman, T. S. and Campbell, M. S. and Hsu, F.-H. (1988). Singular exten- sions: Adding selectivity to brute-force searching. ICCA Journal, Vo!' 11, No.4, pp. 135-143. [9] Arlazarov, V. L. and Futer, A. L. (1979). Computer analysis of a Rook end- game. Machine Intelligence 9, J. E. Hayes and D. Michie and L.1. Mikulich (eds.), pp. 361-371, Ellis Horwood, ISBN 0-470-26714-3/0-853-12112-5 [reprinted in Com- puter Chess Compendium, D. N. L. Levy (ed.), pp. 330-336, Springer, 1989, ISBN 0-387-91331-9]. [10] Bain, M. and Srinivasan, A. (1995). Inductive logic programming with large-scale unstructured data. Machine Intelligence 14, K. Furukawa and D. Michie and S. Muggleton (eds.), pp. 233-267, Oxford University Press, ISBN 0-198-53860-X. [11] Bain, M. (1994). Learning Logical Exceptions in Chess. Ph.D. Thesis, University of Strathclyde [printed as Thesis No. 7866, Department of Statistics and Modeling Science, University of Strathclyde]. [12] Bain, M. and Muggleton, S. (1994). Learning optimal chess strategies. Machine Intelligence 13, K. Furukawa and D. Michie and S. Muggleton (eds.), pp. 291-309, Oxford University Press, ISBN 0-198-53850-2.

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Page 1: Bibliography - Springer978-3-322-90178-1/1.pdf · Bibliography [1] Adelson-Velsky, G ... Programming a computer to play chess. 1st Summer School on Mathematical Programming, Proceedings

243

Bibliography

[1] Adelson-Velsky, G. M. and Arlazarov, V. L. and Donskoy, M. V. (1979). Algorithms of adaptive search. Machine Intelligence 9, J. E. Hayes and D. Michie and L.1. Mikulich (eds.), pp. 373-384, Ellis Horwood, ISBN 0-470-26714-3/0-853-12112-5.

[2] Adelson-Velsky, G. M. and Arlazarov, V. L. and Bitman, A. R. and Zhivotovsky, A. A. and Uskov, A. V. (1969). Programming a computer to play chess. 1st Summer School on Mathematical Programming, Proceedings Vo!' II, pp. 216-252 [another translation of the Russian original appeared in Russian Mathematical Surveys, Vo!' 25, pp. 221-262, London Mathematical Society, 1970].

[3] Akl, S. G. and Newborn, M. M. (1977). The principal continuation and the killer heuristic. ACM '77 National Conference, Proceedings, pp. 466-473, ACM Press.

[4] Alth6fer, 1. (1991). Data compression using an intelligent generator: The storage of chess games as an example. Artificial Intelligence, Vo!' 52, No.1, pp. 109-114.

[5] Alth6fer, I. (1990). Compressing chess games with the help of a fast deterministic chess program. ICCA Journal, Vo!' 13, No.4, pp. 200-203.

[6] Anantharaman, T. S. (1997). Evaluation tuning for computer chess: Linear discrim­inant methods. ICCA Journal, Vo!' 20, No.4, pp. 224-242.

[7] Anantharaman, T. S. (1991). Extension heuristics. ICCA Journal, Vo!' 14, No.2, pp.47-65.

[8] Anantharaman, T. S. and Campbell, M. S. and Hsu, F.-H. (1988). Singular exten­sions: Adding selectivity to brute-force searching. ICCA Journal, Vo!' 11, No.4, pp. 135-143.

[9] Arlazarov, V. L. and Futer, A. L. (1979). Computer analysis of a Rook end­game. Machine Intelligence 9, J. E. Hayes and D. Michie and L.1. Mikulich (eds.), pp. 361-371, Ellis Horwood, ISBN 0-470-26714-3/0-853-12112-5 [reprinted in Com­puter Chess Compendium, D. N. L. Levy (ed.), pp. 330-336, Springer, 1989, ISBN 0-387-91331-9].

[10] Bain, M. and Srinivasan, A. (1995). Inductive logic programming with large-scale unstructured data. Machine Intelligence 14, K. Furukawa and D. Michie and S. Muggleton (eds.), pp. 233-267, Oxford University Press, ISBN 0-198-53860-X.

[11] Bain, M. (1994). Learning Logical Exceptions in Chess. Ph.D. Thesis, University of Strathclyde [printed as Thesis No. 7866, Department of Statistics and Modeling Science, University of Strathclyde].

[12] Bain, M. and Muggleton, S. (1994). Learning optimal chess strategies. Machine Intelligence 13, K. Furukawa and D. Michie and S. Muggleton (eds.), pp. 291-309, Oxford University Press, ISBN 0-198-53850-2.

Page 2: Bibliography - Springer978-3-322-90178-1/1.pdf · Bibliography [1] Adelson-Velsky, G ... Programming a computer to play chess. 1st Summer School on Mathematical Programming, Proceedings

244 Bibliography

[13] Barth, W. (1995). Combining knowledge and search to yield infallible endgame programs. ICCA Journal, Vol. 18, No.3, pp. 149-159.

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[16] Beal, D. F. and Smith, M. C. (1996). Multiple probes of transposition tables. ICCA Journal, Vol. 19, No.4, pp. 227-233.

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[28] Berliner, H. J. and Ebeling, C. (1990). HITECH. Computers, Chess, and Cognition, T. A. Marsland and J. Schaeffer (eds.), pp. 79-109, Springer, ISBN 0-387-97415-6/3-540-97415-6.

[29] Berliner, H. J. and Goetsch, G. and Campbell, M. S. and Ebeling, C. (1990). Mea­suring the performance potential of chess programs. Artificial Intelligence, Vol. 43, No.1, pp. 7-21.

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[30] Berliner, H. J. (1987). Some innovations introduced by HITECH. ICCA Journal, Vol. 10, No.3, pp. 111-117.

[31] Berliner, H. J. and Campbell, M. (1984). Using chunking to solve chess Pawn endgames. Artificial Intelligence, Vol. 23, No.1, pp. 97-120.

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[34] Birmingham, J. A. and Kent, P. (1980). Mate at a glance. Advances in Computer Chess 2, M. R. B. Clarke (ed.), pp. 122-130, Edinburgh University Press, ISBN 0-852-24377-4 [reprinted in Computer Chess Compendium, D. N. 1. Levy (ed.), pp. 258-265, Springer, 1989, ISBN 0-387-91331-9].

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[37] Bjornsson, Y. and Marsland, T. A. (1998). Risk management in game-tree prun­ing. Technical Report TR 98-07, Department of Computing Science, University of Alberta.

[38] Bjornsson, Y. and Marsland, T. A. and Schaeffer, J. and Junghanns, A. (1998). Searching with uncertainty cut-offs. Advances in Computer Chess 8, H. J. van den Herik and J. W. H. M. Uiterwijk (eds.), pp. 167-179, University of Maastricht, ISBN 9-062-16234-7 [reprinted in ICCA Journal, Vol. 20, No.1, pp. 29-37].

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[41] Bramer, M. A. (1980). Correct and optimal strategies in game-playing programs. Computer Journal, Vol. 24, No.4, pp. 347-352.

[42] Bramer, M. A. (1980). An optimal algorithm for King and Pawn against King using pattern knowledge. Advances in Computer Chess 2, M. R. B. Clarke (ed.), pp. 82-91, Edinburgh University Press, ISBN 0-852-24377-4.

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[43] Bramer, M. A. and Clarke, M. R. B. (1979). A model for the representation of pattern-knowledge for the endgame in chess. International Journal of Man-Machine Studies, Vol. 11, No.5, pp. 635-649.

[44] Bratko, 1. (1982). Knowledge-based problem solving in AL3. Machine Intelli­gence 10, J. E. Hayes and D. Michie and Y.-H. Pao (eds.), pp. 73-100, Ellis Hor­wood, ISBN 0-470-27323-2/0-853-12431-0.

[45] Bratko, 1. and Michie, D. (1980). A representation for pattern knowledge in chess endgames. Advances in Computer Chess 2, M. R. B. Clarke (ed.), pp. 31-56, Edin­burgh University Press, ISBN 0-852-24377-4.

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[51] Buro, M. (1997). Experiments with MULTI-PROBCUT and a new high-quality eval­uation function for Othello. Technical Report No. 96, NEC Research Institute, 1997.

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[57] Clarke, M. R. B. (1977). A quantitative study of King and Pawn against King. Advances in Computer Chess 1, M. R. B. Clarke (ed.), pp. 30-59, Edinburgh Uni­versity Press, ISBN 0-852-24292-1.

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Index

A adaptive null-move pruning 2-4, 29-40,

53, 191, 194 AEGON Man vs. Machine 2

11th (April 1996) 186, 200 12th (April 1997) 187, 200

AEGON Man vs. ~\Iachine 185-187,200 AEL pruning 4, 36, 53-60, 203-205, 238-

242 algebraic notation 8 alpha 13-19,21,22,26, 191, 196 alpha bounding 191

lazy 191 plain 191

Alpha-21064 30 150 MHz 30

Alpha-21064a 186 275 MHz 186

Alpha-21164a VIII, 30, 40, 51, 59, 61, 66, 78, 99, 112, 125, 141, 203, 204

300 MHz 30 433 MHz VIII 500 MHz VIII, 51, 78, 125, 141 600 MHz VIII, 40, 59, 61, 99, 203,

204 767 MHz VIII, 66, 112

Alpha-21264 VIII, 185, 187, 188, 202 500 MHz VIII, 185, 187,202

ALPHA-BETA 13-17 alpha-beta bounds -+ see "bound" alpha-beta pruning 14, 16, 65 alpha-beta search 1, 13-17, 22, 24, 33,

41, 66, 145, 191, 194 fail-hard 15 fail-soft -+ see "FAB"

nature of 16 ANSI-C 6, 11, 14, 19, 26, 185, 186 Artificial Intelligence 1 aspiration search 24 aspiration window 24, 191 attack 10, 189, 191, 196 attack detection 189 attack mask 189, 191

B B* 198 back rank 9 backward propagation 24 backward pruning 14, 25

259

BELLE 29, 123-126, 128, 129, 131, 132, 142, 145, 146, 158, 159, 162-164

best change 128-156 behaviour 128-156 rate 128-156

best move 5,16,22-24,123,125-156,191 best play 11 best score 12, 15-19, 22, 24 beta 13-19, 21, 22, 26 BILL 158, 175, 176 Bishop 8, 10, 196 bit number 189 bitboard 6, 185-191

a1-h8 190 a8-h1 190 diagonalized 190 flipped 189, 190 normal 189 operations on 187, 188 rotated 6, 185, 187, 189, 191 s of 187

bitboard engine 186, 187 bit board infrastructure 187 bitboard-based 185-188 bit boards 191

flipped 191 bitwise operation 187, 188 Black 4, 8, 9, 11, 193, 204 black piece 9 board 1, 6-11, 19, 23, 25, 65, 187, 189,

195-197 representation of 11, 65, 187, 197

board game 7 bound 13-17,20-22,65,68, 195-197

lower 14-17, 20, 21, 68 upper 14, 16, 21, 22, 68, 195-197

branch 3, 14 branching factor 16, 191

effective 191 brute-force paradigm 30, 31

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brute-force search 11, 29,30 BS-2830 56, 57, 185, 201, 203 BT-2630 56, 57, 185, 201, 203

C cache 1,6,22,40,51,61,141,195,202

off-chip 6, 40, 51, 61, 141, 202 on-chip 6, 40, 51, 61, 141, 202 position-addressable 22

capture 10, 18, 19, 23, 83, 89, 191-193 "bad" 19, 23 "good" 23 en-passant 83, 89 promising 18 unpromising 19

capture-only quiescence 18, 19, 191, 195 castling move 10, 89 center control 196 check 2, 9, 10, 20, 25, 29, 193, 194 check extension 20, 193, 194 checkers 2, 6, 145, 157 checkmate --+ see "mate" checks 20 chess 1-3,6-12,14,19,23,25,40,50,60,

157, 185, 186, 200 rules of 9 tree of 12

chess anomaly 2, 6, 157 chess board --+ see "board" chess engine 185, 203, 204 chess knowledge --+ see "knowledge" chess machine 29, 30 chess piece --+ see "piece" chess program VII, 1, 2, 4-6, 16, 18-20,

22-25, 29, 30, 36, 53, 59, 65, 66, 83, 99, 123, 145, 185-188, 192-195, 203, 205

bitboard-based 185, 186, 188 CHEssMACHINE SCHRODER 30 CHESS MASTER 59,203-238 CHESS 4.x 29, 41, 186 CHINOOK IX, 66, 115, 116, 145, 147, 155,

158,159,174,175 CILKCHESS 30 colour 8, 10 column-major mapping 189 COMET 59, 203-238 computer checkers 2, 5, 157

Index

computer chess VII, 1-3,5,7, 19,22,24, 25, 29, 31, 41, 53, 157, 186, 187, 192, 198

computer game-playing 7 computer Othello 2, 5, 157 confident conclusion 2, 6, 157 CPU VIII, 6, 40, 51, 61, 99, 141, 186,

188, 189, 198, 202, 203 32-bit 186 64-bit 186

CRAFTY 59, 84, 98, 123-125, 129, 131-140, 142-150, 154-156, 186, 187, 203-238

CRAY BLITZ 30, 186 critical tree 16, 22, 191 current position 196 cutoff 2,3,14-19,22-26,65,198

"stand pat" 3, 18, 19 fail-high 14-17,19,22 null-move 26 rate of 15, 16, 22 selective 2, 25

cutoff rate 15, 16, 22

D DARKTHOUGHT VII-IX, 1-6, 29, 33-41,

44-46, 49-51, 53, 54, 56-61, 65-67, 72-74, 76-79, 81, 83, 95, 99, 112, 113, 115, 123-125, 129-142, 145-147, 150-156, 185-199,202-242

database 1,4,5,33,67,77,83-120, 191, 192, 203, 204

5-piece 67, 77, 83 Edward's 83-98 Edwards' 1, 5, 83, 99 encoding of 1,4, 5, 33,99-120, 191 Huffman-encoded 83 indexing of 5, 83-96 knowledgeable 1,5,33,99-120,191 Nalimov's 83, 85, 86, 89, 98 Thompson's 67,83-98

DEEP BLUE 30, 31, 96, 115 DEEP THOUGHT 29, 30, 115 deep-search extension 192-194 defence 204 depth limit 14, 20

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depth reduction 2, 3, 20, 25, 29, 33, 34, 38, 39, 194

adaptive 3, 29, 38, 39 factor of 2, 3, 25, 29, 33, 34, 38, 39,

194 fixed 2, 33, 34, 194

depth-first search 1, 7 depth-limited search 14, 15 depth-reduction factor 2, 3, 25, 29, 33,

34, 38, 39, 194 adaptive 3, 29, 38, 39 fixed 2, 33, 34, 194

diagonal 6, 189, 190 a1-h8 190 a8-h1 190

diagonal bits 190 diagonalized bitboard 190 diminishing returns 2,5,6,126,157-179 distance-to-conversion 67,83 distance-to-mate 67, 84, 99 distance-to-root 14, 23 domain-dependent 3, 5, 99-120 domain-independent 2, 23, 25 draft 21,22 draw 4, 9, 11, 204 drosophila 1 dynamic forward pruning 2, 25

E

ECM ECO edge effort

ELO

1, 3, 4, 33, 40, 50, 53, 57, 60, 201 57, 59, 204-242 12,14

14, 16, 22, 24, 25, 29, 31, 41, 53, 157, 191

200 empirical evidence 2-5, 53, 123, 157

confident 2, 5, 157 en-passant capture 83, 89 Encyclopedia of Chess Middlegames --+

see "ECM" endgame 5, 10, 25, 66, 83, 99, 123, 185,

187, 191, 194, 196, 204 endgame database --+ see "database" evaluation 3, 11, 14, 18, 19,23, 25, 65-67,

185-187, 191-193, 195-197 fail-high 19 game-theoretical 11, 14 infrastructure of 195, 196

King / Pawn 196 material-balance 196 Pawn-race 196 Pawn-structure 196 piece-placement 196, 197 positional 187, 196 score of 195, 196

261

static 18, 19, 25, 66, 67, 191, 192 static exchange --+ see "SEE"

evaluation engine 186, 187, 195, 197 evaluation function 3, 19, 185-187, 195-

197 bitboard-based 186 programmable 185, 187, 195 state of 195

evaluation infrastructure 195-197 evaluation machine 197 execution unit 6 experiment IX, 1-6, 29, 33, 34, 36, 38,

39, 41, 45, 46, 49, 50, 53, 54, 57, 123-179, 194

"go deep" 2, 5, 123-156 behavioural 5, 123-156 quantitative 1, 29, 33, 34, 36, 38, 39,

41,45,46,49,50,53,54,57 self-play 2, 5, 157-179

experimental result 5, 123 extended futility pruning 2-4,25,33,41-

50, 53, 191 extension 1, 20-22,29, 191-194,198

check 20, 193, 194 deep-search 192-194 fractional 198 layering 193, 194 mate-threat 20, 193 Pawn 20,193 recapture 20, 193 single-reply 20, 192, 193 singular 20 trigger of 193

extension trigger 193 extrapolation 147, 155, 156

F FAB 15,194 fail-hard 15, 17 fail-high 14-17,19,22,24-26,191 fail-high evaluation 19

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fail-low 14-16,22,24,25 fail-soft 15-17 FEr; 78, 202 FERRET 30 fianchetto 196 file 8, 189, 196

open 196 file bits 189 find-bit 188, 189 fixed depth 2-5, 33, 36, 41, 53, 123, 239,

241 flipped bitboard flipped bit boards forward pruning

189 191 1-3, 24, 25, 29-31, 41,

53 dynamic 2, 25 static 3, 25, 29, 41 successful 1, 25, 29, 31, 53

FRENCHESS 30 FRITZ 30, 58, 59, 97, 203-238 frontier node 3, 41 full-width node 2, 18, 25, 26, 193

nominal 25, 26 full-width search 1, 18-20, 24-26, 29, 30,

191 nominal 25

futility condition 19 futility idea 3, 25, 41 futility pruning 2-4, 19, 25, 33, 41-50,

G

53,191-193,195-197 extended 2-4, 25, 33, 41-50, 53, 191 idea of 3, 25, 41 in evaluation 196, 197 in quiescence 19, 25 normal 3,4,25,33,41-44, 191 principle of 3

game stage 10 game theory 11 game tree 12, 13, 16, 22, 29 game-playing program IX, 1, 5, 18, 20,

22, 24, 157 game-theoretical evaluation 11-15, 17,

18, 65 game-theoretical knowledge 1, 4 game-tree search -+ see "search" GENIUS 59,203-238

GM 200

H

half move 9, 14 hash code 22

Index

hash table 1,4,40,51,61, 141, 195, 196, 202-204

entries of 40,51,61, 141,202 hashed move 22, 23, 192 HITECH 30,124,127,158,159,166-171,

193, 195 HIARCS 59, 203-238 hierarchical memory 6 high-performance 1,4, 6, 66 high-speed 1, 65, 67 history heuristic 23, 33, 191 history move 23, 192 history table 23, 195 horizon effect 145 horizon node 3, 14, 16-18,20 Huffman-encoded database 83

I I/O delay 1,5 ICCA VII, IX, 2, 29, 30, 41, 65, 83, 99,

123, 185 [GGA Journal VII, IX, 2, 29,41,65,83,

99, 123, 185 11\'1 200 indexing 5, 83-96 inexact knowledge 4 Institute for Program Structures and Data

Organization -+ see "IPD" integer 6, 187, 188

64-bit 6, 187 unsigned 6, 187

interior node 2, 4, 5, 33, 65, 66, 191, 192 interior-node recognition 2, 4, 5, 33, 65-

81,191, 192 International Computer Chess Association

-+ see "ICCA" interpolation 145-156 IPD IX, 185, 186 iteration 23-25, 123, 191

depth of 23-25 iteration controller 23, 24 iteration depth 23-25

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iterative deepening 23, 24, 33, 145, 191, 192

internal 33, 191, 192

J

JUNIOR 30, 59, 203-238

K

KAISSA 186 Karlsruher UniSchau 200

1st (June 1997) 200 KEYANO IX, 158, 176-178 killer heuristic 23, 33, 191 killer move 23, 192 King 8-10,19,40,51,61,141,196,202 King safety 19, 196 King / Pawn structure 196 Knight 8, 10 knowledge 1,4,5,10,29,65-81,99-120,

157, 185, 195 a-priori 5 chunk of 4 domain-dependent 5 game-theoretical 1, 4 inexact 4 perfect 4

knowledgeable database 1,5,33,99-120, 191

knowledgeable encoding 5, 99-120 knowledgeable probing 5, 105-107 knowledgeable querying 5, 110-111 knowledgeable scoring 5, 106-110

L

LeT-II 56,57,185,201,203 leaf node 185, 187, 191, 195 learning 198, 204 least significant I-bit 188, 189 limited razoring 3, 4, 25, 41, 47-50, 53 LOTECH 127,158,159,166-171 load-store architecture 6 long-range plan 10 lookup table 188 loss 4, 11

M main memory 6, 99

major piece 10, 191 mate 9 mate-threat extension 20, 193 material balance 19, 193, 196 material gain 19 material signature 4, 65, 72-77 material value 10 MAX 11 maximin behaviour 11, 13 MCHESS 59, 98, 203-238 MEISTERSCHACH 59, 204-238 memory access 189

263

microcomputer VIII, IX, 2, 4, 29, 30, 36, 45, 66, 84, 95, 112, 186, 187, 194, 199, 204

microprocessor 186 64-bit 186

middlegame I, 10,29,40,50,60,66, 185, 187, 191, 196

MIN 11 MINIMAX 11-14,16 minimal tree 16, 22, 191 minimal window 16, 17, 26

search with ---+ see "MWS" minimax semantics 11 minor piece 10, 191 mobility 196 modeling 145-156 most significant I-bit 189 most valuable victim / least valuable

aggressor ---+ see "MVV / LV A" move 5, 9-26, 29, 65, 89, 123, 186, 187,

191-193, 195, 204 best 5, 16, 22-24, 123, 191 capture 10, 18, 19, 23, 191, 192 castling 10, 89 checking 195 execution of 11, 22, 65, 195 forcing 20 generation of 11, 14, 19,29, 186, 187 half 9, 14 hashed 22, 23, 192 history 23, 192 irreversible 10 killer 23, 192 legal 9, 20, 25 list of 192, 195 maximal-benefit 11

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minimal-benefit 11 notational 9, 10 possible 9, 11, 12, 23 promotion 19 reversible 9 selection of 23

move execution 11, 22, 65, 195 move generation 11, 14, 19, 29, 186, 187

bit board-based 187 plausible 29

move list 192, 195 move numbering 9 move ordering 1, 16, 17, 22-24, 33, 191,

192 dynamic 1, 22-24, 33, 191 heuristic 22-24, 33, 191 perfect 16, 22 quality of 22 role of 22 static 22, 23

move-ordering heuristic 22-24,33, 191 dynamic 22-24, 33, 191 history 23 killer 23 static 22, 23

mover 6 diagonal 6 straight-line 6

MTD(F) 198 MULTI-PROBCUT 32 MVV / LVA 19, 23, 191, 192 MWS 16,17

N NEGAMAX 11-14,16,43 NEGASCOUT 33, 191, 194 nested minimax 65 NIMZO 59, 204-238 node 2,3, 11-26, 33,41,66,99, 185, 187,

191-193, 195, 196 current 14, 15, 21, 22, 26 expansion of 192, 193, 195 frontier 3, 41 full-width 2, 18, 25, 26, 193 horizon 3, 14, 16-18, 20 interior 2, 4, 5, 33, 65, 66, 99, 191,

192 leaf 185, 187, 191, 195

pre-frontier 3, 41, 193 pre-pre-frontier 3 quiescence 18, 19, 193

Index

root 12, 14, 17,23,24,99, 193, 195, 196

terminal 14 node expansion 192, 193, 195 nodes per second -+ see "nps" nominal depth 20, 22 normal futility pruning 3,4, 25, 33,41-

44, 191 nps 57,66,78,99,112,185-187,201,202 null move 2-4,25,26,29-40,53,191-194

assumption of 25 cutoff 26 idea of 2, 25 pruning 2-4, 25, 26, 29-40, 53, 191,

193, 194 search 25, 192, 194

null-move pruning 2-4, 25, 26, 29-40, 53, 191, 193, 194

adaptive 2-4, 29-40, 53, 191, 194 standard 3,4,26,29-33

null-move search 25, 192, 194 Nunn position 59, 204-242

o obligation-to-move 9, 25 opening 10, 198, 204 opening book 198, 204 oracle 195-197 Othello 2, 6, 157 outpost 196

P P.CoNNERS 30 parallelization 195, 198 passed Pawn 20, 193, 196 path 20

length of 20 Pawn 8,10,19,20,24,40,51,61,67,83,

141, 187, 191, 193, 194, 196, 197, 202

passed 20, 193, 196 weak 196

Pawn extension 20, 193 Pawn race 196

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Pawn shelter 196 Pawn structure 19, 196 perfect information 11 perfect knowledge 4 performance 1, 3, 4, 14-17, 22-25, 29, 31,

33, 34, 38, 39, 41, 46, 49, 50, 53, 57, 77, 186, 188, 191, 194, 204

PHOENIX IX, 124, 158, 159, 172, 173 piece 1,5,6,8-10, 19, 25, 186, 187, 189,

191, 194-197 black 9, 187 captured 10 capturing 10 major 10, 191 minor 10, 191 movement of 10 placement of 196, 197 sliding 187, 189 value of 10 white 9

piece interaction 19 piece list 186 piece movement 10

rules of 10 piece value 10 piece-attack detection 187 piece-on-square table -+ see "PST" pipelined execution 6 plan 10

long-range 10 short-range 10

player 8-11, 13 playing strength 5,123,157-179 ply 2-5, 9, 14, 20, 23, 25, 29, 33, 41, 53,

66, 123, 191-194, 198 population-count 188, 189

iterative 188 non-iterative 188

position 1-5, 8-26, 33, 40, 50, 59, 60, 65, 66, 99, 187, 191, 195-197, 204-242

current 11, 12, 15-19, 21, 22, 191, 196

decidabilityof 11-15,17,18,65 endgame 5, 66, 99 forced 20 Nunn 59, 204-242 quiet 18, 19

265

representation of 11, 65, 187, 197 root 12, 16, 17, 24, 195, 196 score of 11, 14, 16-19 starting 8-12

positional evaluation 187, 196 pre-frontier node 3, 41, 193 pre-pre-frontier node 3 principal-variation search -+ see "PVS" PROBCUT 32 processor 1, 6, 186

micro 186 Prof. Salvatore Award 2 promotion 19, 193 pruning 1-4, 14, 16, 19, 22, 24-26, 29-

50, 53-60, 65, 191-197, 203-205, 238-242

AEL 4,36,53-60,203-205,238-242 alpha-beta 14, 16, 24, 65 backward 14,25 efficiency of 16, 22, 24 forward 1-3,24,25,29-31,41,53 futility 2-4, 19, 25, 33, 41-50, 53,

191-193, 195-197 null-move 2-4,25,26, 29-40, 53, 191,

193, 194 selective 1, 3, 4, 29-31,41

PST 195,196 PVS 16-19,21,22,26,33,191,194

Q quantitative experiment 1 Queen 8,10 quiescence node 18, 19, 193 quiescence search 17-19, 24, 25, 30, 191,

192, 194, 195

R

capture-only 18, 19, 191, 195 selectivity of 18

RAM VIII, 1, 5, 40, 51, 61, 99, 141, 185, 202, 203

rank 8, 9, 189, 193 back 9 bottom 9 top 9

rank bits 189 ray bits 189

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razoring 3, 4, 25, 41, 47-50, 53 flavours of 25 limited 3, 4, 25, 41, 47-50, 53

re-search 16, 17, 24 REBEL 59, 204-238 recapture 20, 29, 193 recapture extension 20, 193 recognizer 2, 4, 5, 33, 65-81, 191, 192 recognizer function 76-78 recognizer score 69-71 recursion tree 12 reduced material 1, 123, 196 register 6 remaining search depth 25 RETRO 89 right-to-move 2, 9, 25 Rook 8, 10, 196

connected 196 root node 12, 14, 17,23,24,99, 193, 195,

196 root position root window

12, 16, 17, 24, 195, 196 14

root-node oracle -+ see "oracle" rotated bitboard 185, 187, 191 row 8 row-major mapping 189

S scalability 1-6, 29, 38, 41, 53, 99

of the search 1-5,29,38,41,53,99 technological 1, 6

score 11-26,65-81,191,195,196 best 12, 15-19, 22, 24 bound on 14, 16, 21, 22, 65 cached 21, 22, 195, 196 exact 14, 16, 21, 22 partial 196 recognizer 69-71

scoring function 14, 17-19, 66, 195-197 dynamic 18 special 196 static 18, 19

search VIII, 1-7, 11-26, 29-31, 33, 34, 36-39, 41, 43, 46, 48-50, 53, 55, 57, 65, 66, 77, 83, 99, 123, 145, 157, 185-188, 191-195,201, 202, 239, 241

Index

alpha-beta 1, 13-17,22,24,33,41, 66, 145, 191, 194

aspiration 24 behaviour of 1, 2, 5 brute-force 11, 29, 30 depth of 2-5, 14, 19, 20, 22-25, 29,

33, 36, 41, 53, 66, 123, 145, 157, 191-195, 239, 241

depth-first 1, 7 depth-limited 14, 15 effort of 3-5, 14, 16, 22, 24, 25, 29,

31, 41, 53, 157, 191 full-width 1, 18-20, 24-26, 29, 30,

191 horizon of 14 iterative deepening 23, 24, 145 lookahead of 14, 20, 24, 29 minimal-window -+ see "MvVS" minimax 11-14, 16 negamax 11-14, 16, 43 negascout 33, 191, 194 non-terminating 20 null-move 25, 192, 194 parameterization of 185, 194 performance of 1,3,4,14-17,22-25,

29, 31, 33, 34, 38, 39, 41, 46, 49, 50, 53, 57, 77, 186, 188, 191, 194

principal-variation -+ see "PVS" quiescence 17-19, 24, 25, 30, 191,

192, 194, 195 recursive 11,13-17,19-22,26,192 result of 11-19,21,22,24,25,191 scalability of 1-5,29,38,41,53,99 selective 1, 2, 18, 19, 25, 26, 29, 30,

33, 37, 41, 48, 55, 191, 193 speed of 1, 5, 30, 66, 77, 99, 157,

185-187,201,202 techniques of 7, 11-26 top-level 24, 191 unconstrained 11, 12, 14 uniform-depth 20 variable-depth 20, 191 window of 13-17, 22, 24, 26

search depth 2-5, 14, 19, 20, 22-25, 29, 33, 36, 41, 53, 66, 123, 145, 157, 191-195,239,241

fixed 2-5, 33, 36, 41, 53, 123, 239, 241

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high 5, 24, 25, 41, 123, 145, 191 nominal 20, 22 progressing 3, 4, 23, 29 remaining 3, 14, 20, 22, 23, 25, 41,

192 uniform 20 variable 20, 191

search effort 3-5, 14, 16, 22, 24, 25, 29, 31, 41, 53, 157, 191

search engine 186, 187 search extension 33, 191 search extension -+ see "extension" search horizon 14 search result 22 search speed 1, 5, 30, 66, 77, 99, 157,

185-187,201,202 search tree 1,3, 12, 14, 16,22,29,41,99,

191-194 alpha-beta 16 branch of 3, 14, 41 critical 16, 22, 191 minimal 16, 22, 191 pruning of -+ see "pruning" shape of 14 size of 1, 3, 14, 16, 41, 191, 193

search window 13-17, 22, 24, 26 SEE 19,23 selective risk 3, 25, 31 self-play IX, 1,2,4-6,36, 53, 58, 157-179,

238-242 direct 5,157-179 game 4, 36, 58, 238-242 round-robin 5, 157, 160, 163, 164,

166-171, 178 self-play experiment 2, 5 short-range plan 10 SHREDDER 30, 59, 97, 204-238 side-to-move 2,9-12, 19,25, 197 single-reply extension 20, 192, 193 singular extension 20 SOCRATES 194 solution time 56, 57, 201 space consumption 5, 99 SPECint95 204 SPECTOR 84 square 6,8, 10, 187, 189-191,195, 197

attacked 10, 187 black 8

rays of 189 reachable 10 white 8

stalemate 9 standard error 33

267

standard null-move pruning 3, 4, 26, 29-33

standing pat 9 STARSOCRATES 30, 194 starting position 8-12 static evaluation 18, 19, 25, 66, 67, 191,

192 static exchange evaluation -+ see "SEE" static forward pruning 3, 25, 29, 41 statistical analysis 5, 157-162 superscalar 6

T table lookup 189 tablebase 1,5,83-120,203

Edward's 83-98 Edwards' 1,5,83,99 Nalimov's 83, 85, 86, 89, 98

tactical performance 3, 4, 29, 31, 33, 41, 53, 191

TECH 29,41 TECHMATE 126, 158, 164-166 test game 1, 4, 36, 53, 58, 203-242 test suite 1, 3, 4, 31, 40, 50, 56, 57, 60,

185, 186, 191, 201, 203, 204 THE TURK IX, 124, 127, 158, 173 THE KING 203 Thompson's database 67,83 threefold repetition 9 time control 4, 36, 204, 238

tournament 4, 36, 204, 238 time management 23 top-level search 24, 191 transposition table 21-24,26,33,40,51,

61, 66, 68-70, 141, 191, 192, 194, 196,202

entries of 22, 40, 51, 61, 141, 202 probe of 21, 22, 26, 192, 194 result from 21

tree search -+ see "search"

U uniform depth 20

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268

V variable depth 20, 191

W

VVAC 1, 3, 4, 33, 40, 50, 53, 57, 60, 201 VVCCC VIII, IX, 29, 30, 36, 65, 67, 84,

186, 194, 199 7th (June 1992) 30 8th (May 1995) IX, 30, 65, 67, 84,

186, 194, 199 9th (June 1999) VIII, 30, 36, 199

VVCS 1, 3, 4, 33, 40, 50, 53, 57, 60, 201 weak Pawn 196 VVhite 4, 8, 9, 11, 193, 204 white piece 9 win 4,9, 11 Win at Chess -+ see "VVAC" Winning Chess Sacrifices -+ see "VVCS" winning percentage 5, 157, 204 VVMCC VIII, IX, 2, 4, 36, 45, 66, 84, 95,

112, 186, 187, 199,204,205 13th (October 1995) VIII, 84, 186,

199 14th (October 1996) VIII, IX, 187,

199, 204 15th (October 1997) VIII, 45, 66, 95,

112, 199 16th (June 1999) VIII, 2, 199

world championship VIII, IX, 2, 4, 30, 36, 45, 65-67, 84, 95, 112, 185-187, 194, 198, 199, 204

VVorld Computer-Chess Championship -+ see "VVCCC"

VVorld Microcompt.-Chess Championship -+ see "VVMCC"

worst-to-best order 16

z zero window -+ see "minimal window" zero-sum condition 11 zero-sum game 11 ZUGZWANG 30, 124, 127, 158, 171, 172 Zugzwang 9, 25, 191

Index

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The Efficiency of Theorem Proving Strategies

The Efficiency of Theorem Proving Strategies

A Comparative and Asymptotic Analysis

by David A. Plaisted and Yunshan Zhu

2. Ed. 1999. viii, 170 pp. (Computational Intelligence) Softc. OM 58,00 ISBN 3-528-15574-4

aI vleweg

Abraham·lIncoln-Stra8e 46 65189 Wiesbaden Fax 0180.57878·80 www.vleweg.de

Asymptotic bounds on the sizes of the search spaces generated by many common theorem proving strategies - gaining a theoretical understanding of the efficiencies of many different theorem proving methods - a comparative study of theorem proving strategies

This book is unique in that it gives asymptotic bounds on the sizes of the search spaces generated by many common theorem proving strategies. Thus it permits one to gain a theoretical unterstanding of the efficiencies of many different theorem proving methods. This is a fundamental new tool in the compa­rative study of theorem proving strategies.

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Multiobjective Heuristic Search

, .

Multlobjectlve Heuristic Search

An introduction to intelli­gent search methods for multicriteria optimization

Pallab Dasgupta, P. P. Chakrabarti, S. C. DeSarkar

1999. x, 134 pp. with 25 figs., (Computational Intelligence) Softc. DM 98,00 ISBN 3-528-05708-4

The Multiobjective Search Model -Multiobjective State Space Search -Multiobjective Problem Reduction

Search - Multiobjective Game Tree Search - Applications of Multiobjec­tive Search

This text describes the multiobjec­tive search model and develops the theoretical foundations of the sub­ject, including complexity results. The fundamental algorithms for three major problem formulation schemes, namely state-space formu­lations, problem-reduction formula­tions, and game-tree formulations are developed with the support of illustrative examples. Applications of multiobjective search techniques to synthesis problems in VLSI, and operations research are considered. This text provides a complete pic­ture on contemporary research on multiobjective search, most of which is the contribution of the authors.

November 1999 Anderungen vorllehal ten. Erhiilllich belm Buchhandel oder belm Verlag.