AN AUTOMATED ACTION-SELECTION SYSTEM AND METHOD, AND APPLICATION THEREOF TO TRAINING PREDICTION MACHINES AND DRIVING THE DEVELOPMENT OF SELF-DEVELOPING DEVICES

摘要

In order to promote efficient learning of relationships inherent in a system or setup S described by system-state and context parameters, the next action to take, affecting the setup, is determined based on the knowledge gain expected to result from this action. Knowledge-gain is assessed 'locally' by comparing the value of a knowledge-indicator parameter after the action with the value of this indicator on one or more previous occasions when the system-state/context parameter(s) and action variable(s) had similar values to the current ones. Preferably the 'level of knowledge' is assessed based on the accuracy of predictions made by a prediction module. This technique can be applied to train a prediction machine by causing it to participate in the selection of a sequence of actions. This technique can also be applied for managing development of a self-developing device or system, the self-developing device or system performing a sequence of actions selected according to the action-selection technique.