1. An adaptive mechanism for the optimization of sequential decision making in an artificial intelligence system having N rules in a list to be scanned comprising the steps of: storing the observed frequencies of firing of different rules in a one-dimensional array FREQ, with FREQ(i), i=l,. .. ,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 ATTMEPTS(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 ##EQU2## which is the estimate of the ratio of probability of firing to cost per test for each rule; maintaining a SCANLIST of rules in a sequence ordered by nonincreasing RATIO values; selecting a rule for firing in order from SCANLIST so that Rule i is tested before Rule j if RATIO(i) &gt;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).