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IJCA/- 99, pages 584-5 89, Stockholm, Sweden, 1999.
(39) G. Goetsch and M. S. Campbell. Experiments with the null-move heuristic. In
T. A. Marsland and J. Schaeffer, editors, Computers, Chess and Cognition, pages
159-168. Springer-Verlag, New York, 1996.
(40) A. V. Goldberg and C. Harrelson. Computing the shortest path : A* search meets
graph theory. In SODA '05, 2005.
(4 1) P. Hart, N. Nilsson, and B. Raphael. A formai basis for the heuristic determination
of minimum cost paths. IEEE Tra ns. Syst. Sei. Cy bernet. , 4(2) : 100-107, 1968.
(42) W. D. Harvey and M. L. Ginsberg. Limited discrepancy search. In Chris S. Mellish,
editor, IJCAI-95, pages 607-6 15, Montréal, Québec, Canada, August 20-25 1995.
Morgan Kaufmann.
(43) J. Hopcroft, J. Schwartz, and M. Sharir. On the complexity of motion planning
for multiple independent objects : Pspace-hardness of the warehouseman's problem.
International Journal of Robotics Research, 3( 4 ), 1984.
(44) S. Huang, R. Coulom, and S. Lin. Monte-Carlo simulation balancing in practice. In
Computers and Garnes, 20 10.
(45) T. Ishida. Real-time search for autonomous agents and multiagent systems. Autonomous Agents and Mu lti-Agent Systems, 1(2) : 139-167, 1998.
(46) T. Ishida and R. E. Korf. Moving target search. In IJCAI, pages 204-211, 1991.
(47) A. Junghanns and J. Schaeffer. Search versus knowledge in game-playing programs
revisited. In IJCAI, pages 692-697, 1997.
(48) G. Kendall, A. Parkes, and K. Spoerer. A survey of NP-complete puzzles. ICGA
Jo urnal, 31(1) :13-34, 2008.
(49) A. Kishimoto and M. Müller. Df-pn in Go : an application to the one-eye problem.
In Advances in computer games JO, pages 125-141, 2003.
(50) D.E. Knuth. The art of computer programming. Vo lume 3 : sorting and searching.
Addison-Wesley, Reading MA, USA, 1973.
(5 1) D.E. Knuth and R.W. Moore. An analysis of alpha-beta pruning. Artificial Intelligence, 6(4) :293-326, 1975.
[52) R. E. Korf. Depth-first iterative-deepening : an optimal admissible tree search. Artificial Intelligence, 27( 1) :97-109, 1985.
(53) R. E. Korf. Macro-operators : A weak method for learning. Artif. Intel!. , 26( 1) :3577, 1985.
[54) R. E. Korf. Real-time heuristic search. Artif. Intel!. , 42(2-3) :189-211, 1990.
(55) R. E. Korf. Linear-space best-first search. Artif. lntell., 62( 1) :41-78, 1993.
(56) R. E. Korf. Finding optimal solutions to rubik's cube using pattern databases. In
AAAI-97, pages 700-705, 1997.
[57) M. Lanctot, K. Waugh, M. Zinkevich, and M. Bowling. Monte carlo sampling for
regret minimization in extensive games. In Advances in Neural Information Processing Systems 22 (NIPS), pages 1078-1086, 2009.
237
(38) M. L. Ginsberg. GIB : Steps toward an expert-level bridge-playing program. In
IJCA/- 99, pages 584-5 89, Stockholm, Sweden, 1999.
(39) G. Goetsch and M. S. Campbell. Experiments with the null-move heuristic. In
T. A. Marsland and J. Schaeffer, editors, Computers, Chess and Cognition, pages
159-168. Springer-Verlag, New York, 1996.
(40) A. V. Goldberg and C. Harrelson. Computing the shortest path : A* search meets
graph theory. In SODA '05, 2005.
(4 1) P. Hart, N. Nilsson, and B. Raphael. A formai basis for the heuristic determination
of minimum cost paths. IEEE Tra ns. Syst. Sei. Cy bernet. , 4(2) : 100-107, 1968.
(42) W. D. Harvey and M. L. Ginsberg. Limited discrepancy search. In Chris S. Mellish,
editor, IJCAI-95, pages 607-6 15, Montréal, Québec, Canada, August 20-25 1995.
Morgan Kaufmann.
(43) J. Hopcroft, J. Schwartz, and M. Sharir. On the complexity of motion planning
for multiple independent objects : Pspace-hardness of the warehouseman's problem.
International Journal of Robotics Research, 3( 4 ), 1984.
(44) S. Huang, R. Coulom, and S. Lin. Monte-Carlo simulation balancing in practice. In
Computers and Garnes, 20 10.
(45) T. Ishida. Real-time search for autonomous agents and multiagent systems. Autonomous Agents and Mu lti-Agent Systems, 1(2) : 139-167, 1998.
(46) T. Ishida and R. E. Korf. Moving target search. In IJCAI, pages 204-211, 1991.
(47) A. Junghanns and J. Schaeffer. Search versus knowledge in game-playing programs
revisited. In IJCAI, pages 692-697, 1997.
(48) G. Kendall, A. Parkes, and K. Spoerer. A survey of NP-complete puzzles. ICGA
Jo urnal, 31(1) :13-34, 2008.
(49) A. Kishimoto and M. Müller. Df-pn in Go : an application to the one-eye problem.
In Advances in computer games JO, pages 125-141, 2003.
(50) D.E. Knuth. The art of computer programming. Vo lume 3 : sorting and searching.
Addison-Wesley, Reading MA, USA, 1973.
(5 1) D.E. Knuth and R.W. Moore. An analysis of alpha-beta pruning. Artificial Intelligence, 6(4) :293-326, 1975.
[52) R. E. Korf. Depth-first iterative-deepening : an optimal admissible tree search. Artificial Intelligence, 27( 1) :97-109, 1985.
(53) R. E. Korf. Macro-operators : A weak method for learning. Artif. Intel!. , 26( 1) :3577, 1985.
[54) R. E. Korf. Real-time heuristic search. Artif. Intel!. , 42(2-3) :189-211, 1990.
(55) R. E. Korf. Linear-space best-first search. Artif. lntell., 62( 1) :41-78, 1993.
(56) R. E. Korf. Finding optimal solutions to rubik's cube using pattern databases. In
AAAI-97, pages 700-705, 1997.
[57) M. Lanctot, K. Waugh, M. Zinkevich, and M. Bowling. Monte carlo sampling for
regret minimization in extensive games. In Advances in Neural Information Processing Systems 22 (NIPS), pages 1078-1086, 2009.