Research and Advances

An expert system for a resource allocation problem

We describe an expert system for resource allocation in a particular military domain. The system incorporates variants of several important techniques of artificial intelligence and makes the first use of the Merit system for question selection. This technique enables the system to direct the acquisition of information by finding questions with a high ratio of probable importance to difficulty. In the current application, each resource is a military weapon, each task to which such a resource can be allocated is firing at a military target, and the objective function is the expected reduction in value of targets. The coefficients that relate a particular resource to a particular task are not provided explicitly. Instead, in the first phase of the allocation process, the system uses a computation network to determine the effectiveness of each individual weapon against each prospective target. The network, built by a domain expert in advance, allows reasoning with logical, Bayesian, and expert-defined operators. After the calculation of individual effectiveness values, portions of an allocation tree are constructed to determine good allocation plans for the set of weapons. The individual effectiveness values are used to direct the traversal and pruning of the allocation tree.


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Research and Advances

Experiment with an automatic theorem-prover having partial ordering inference rules

Automatic theorem-provers need to be made much more efficient. With this in mind, Slagle has shown how the axioms for partial ordering can be replaced by built-in inference rules when using a particular theorem-proving algorithm based upon hyper-resolution and paramodulation. The new rules embody the transitivity of partial orderings and the close relationship between the ⊂ and ⊆ predicates. A program has been developed using a modified version of these rules. This new theorem-prover has been found to be very powerful for solving problems involving partial orderings. This paper presents a detailed description of the program and a comprehensive account of the experiments that have been performed with it.
Research and Advances

Application of game tree searching techniques to sequential pattern recognition

A sequential pattern recognition (SPR) procedure does not test all the features of a pattern at once. Instead, it selects a feature to be tested. After receiving the result of that test, the procedure either classifies the unknown pattern or selects another feature to be tested, etc. Medical diagnosis is an example of SPR. In this paper the authors suggest that SPR be viewed as a one-person game played against nature (chance). Virtually all the powerful techniques developed for searching two-person, strictly competitive game trees can easily be incorporated either directly or by analogy into SPR procedures. In particular, one can incorporate the “miniaverage backing-up procedure” and the “gamma procedure,” which are the analogues of the “minimax backing-up procedure” and the “alpha-beta procedure,” respectively. Some computer simulated experiments in character recognition are presented. The results indicate that the approach is promising.
Research and Advances

Experiments in automatic learning for a multipurpose hueristic program

An automatic learning capability has been developed and implemented for use with the MULTIPLE (MULTIpurpose Program that LEarns) heuristic tree-searching program, which is presently being applied to resolution theorem-proving in predicate calculus. MULTIPLE's proving program (PP) uses two evaluation functions to guide its search for a proof of whether or not a particular goal is achievable. Thirteen general features of predicate calculus clauses were created for use in the automatic learning of better evaluation functions for PP. A multiple regression program was used to produce optimal coefficients for linear polynomial functions in terms of the features. Also, automatic data-handling routines were written for passing data between the learning program and the proving program, and for analyzing and summarizing results. Data was generally collected for learning (regression analysis) from the experience of PP. A number of experiments were performed to test the effectiveness and generality of the learning program. Results showed that the learning produced dramatic improvements in the solutions to problems which were in the same domain as those used for collecting learning data. Learning was also shown to generalize successfully to domains other than those used for data collection. Another experiment demonstrated that the learning program could simultaneously improve performance on problems in a specific domain and on problems in a variety of domains. Some variations of the learning program were also tested.
Research and Advances

Experiments with the M & N tree-searching program

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.

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