Patent ID: 11948101
Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
Field: Control (Instruments)
Classification: CPC G  H | IPC G  H

Claim 10:
11. A computer program product for implementing intelligent identification of predictive models by a processor, the computer program product comprising a non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising:
an executable portion that receives one or more time series of actions associated with input data, wherein each of the actions is executed by one of a plurality of decision makers;
an executable portion that receives a specification of a switching policy constraining a number of decisions able to be made by each of the plurality of decision makers, wherein only a single decision maker of the plurality of decision makers is allowed to make decisions at any given time, and wherein the single decision maker makes an unknown number of consecutive decisions until the switching policy transfers the decision making to a next decision maker of the plurality of decision makers at an unknown time instance;
an executable portion that monitors behavior of each of the plurality of decision makers as the consecutive decisions are made;
an executable portion that receives a specification of a structure of a decision model employed by at least some of the plurality of decision makers, wherein the structure of the decision model comprises a form of an equation used to compute probabilities of a next decision made by a specific one of the plurality of decision makers to which the structure represents;
an executable portion that, responsive to determining the specification of the structure of the decision model is not received for at least one of the plurality of decision makers, determines and assigns a score, to each decision observed by an unknown one of the plurality of decision makers according to an existing model structure selected from a repository of model structures, indicative of a probability that a respective decision was made by a respective one of the plurality of decision makers, wherein the score is used in estimating a number of the plurality of decision makers; and
an executable portion that executes machine learning logic to train one or more non-deterministic models using examples selected from a sequence of outcomes from decisions of each of a plurality of decision makers, wherein the one or more non-deterministic models are generated for each decision maker according to the monitored behavior to automatically predict an outcome of those of the decisions, with respect to the input data, that depend on a state of the system affected by the decisions.