Journal of the ACM (JACM)
A Heuristic Program that Solves Symbolic Integration Problems in Freshman Calculus
Journal of the ACM (JACM)
Experiments With a Multipurpose, Theorem-Proving Heuristic Program
Journal of the ACM (JACM)
Experiments With Some Programs That Search Game Trees
Journal of the ACM (JACM)
Experiments with a deductive question-answering program
Communications of the ACM
Improving heuristic regression analysis
ACM-SE 6 Proceedings of the 6th annual Southeastern regional meeting of the Association for Computing Machinery and national meeting of Biomedical Computing - Volume 1
Computers and Thought
The use of theorem-proving techniques in question-answering systems
ACM '68 Proceedings of the 1968 23rd ACM national conference
An adaptive tree pruning system: A language for programming heuristic tree searches
ACM '68 Proceedings of the 1968 23rd ACM national conference
A completeness theorem and a computer program for finding theorems derivable from given axioms
A completeness theorem and a computer program for finding theorems derivable from given axioms
LISP 1.5 Programmer's Manual
Control strategies for two-player games
ACM Computing Surveys (CSUR)
Application of game tree searching techniques to sequential pattern recognition
Communications of the ACM
Empirical Exploration of the Performance of the Alpha Beta Tree-Searching Heuristic
IEEE Transactions on Computers
Comparing minimax and product in a variety of games
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 1
Comparing minimax and product in a variety of games
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 1
On optimal game tree propagation for imperfect players
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
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The M & N procedure is an improvement to the mini-max backing-up procedure widely used in computer programs for game-playing and other purposes. It is based on the principle that it is desirable to have many options when making decisions in the face of uncertainty. The mini-max procedure assigns to a MAX (MIN) node the value of the highest (lowest) valued successor to that node. The M & N procedure assigns to a MAX (MIN) node some function of the M (N) highest (lowest) valued successors. An M & N procedure was written in LISP to play the game of kalah, and it was demonstrated that the M & N procedure is significantly superior to the mini-max procedure. The statistical significance of important conclusions is given. Since information on statistical significance has often been lacking in papers on computer experiments in the artificial intelligence field, these experiments can perhaps serve as a model for future work.