Adaptive mechanisms for execution of sequential decisions

Claim 1

An adaptive method of optimization of sequential decision making process in an artificial intelligence system having N rules in a list to be scanned characterized by the steps of: storing the observed frequencies of firing of different rules in a one-dimensional array FREQ, with FREQ(i), i=1,...,N, containing the number of times each rule has fired; setting the FREQ values to zero the first time said system is used; generating a one-dimensional array COST(i), i=1,...,N, containing the accumulated cost of testing if a given rule is applicable; generating a one-dimensional array ATTEMPTS(i), i=1,...,N, containing the number of times a rule has been tested for firing; generating a one-dimensional array RATIO(i), i=1,...,N, containing the value which is the estimate of the ratio of probability of firing to cost per test for each rule; maintaining said list (SCANLIST) of rules in a sequence ordered by nonincreasing RATIO values; selecting a rule for firing in order from said list (SCANLIST) so that Rule i is tested before Rule j if RATIO(i) > RATIO(j); incrementing ATTEMPTS(i) and increasing COST(i) by the cost of testing when RULE i is tested for firing; and increasing FREQ(i) by one when a test for RULE i is successful indicating that a rule will fire and updating RATIO(i).