1. A method of selecting a value of at least one of an actuator means in response to an extant value of at least one of a sensor means in a sensing and action period, said selected value being determined by a record of a historical probability of each of an actual value of said actuator means occurring with each of said extant value of said sensor means in each of said sensing and action period, said method comprising: a. determining a beginning and an end of said sensing and action period, and b. identifying said extant value of each said sensor means near a beginning of said sensing and action period, and c. examining said record of said historical probability belonging to each said extant value of said sensor means in said sensing and action period, and d. selecting said selected value of each said actuator means, said selected value having a highest of said historical probability of occurring with said extant value of each said sensor means in said sensing and action period, and e. making an attempt to produce said selected value of each said actuator means within said sensing and action period, and f. restraining said actuator means except when said attempt to produce said selected value is made, and g. making a measurement of an actual value of each said actuator means after a predetermined delay from said attempt to produce said selected value of said actuator means, said measurement being made within said sensing and action period, and h. establishing said record of said historical probability, said record being made according to each said actual value of said actuator means occurring with each said extant value of said sensor means during each of said sensing and action period, whereby i. said method of selecting a value of said actuator means in response to said value of said sensor means and said record of said historical probability provides a simple self-learning machine.