Abstract
Operant conditioning is one of the fundamental mechanisms of animal learning, which suggests that the behavior of all animals, from protists to humans, is guided by its consequences. We present a new stochastic learning automaton called a Skinner automaton that is a psychological model for formalizing the theory of operant conditioning. We identify animal operant learning with a thermodynamic process, and derive a so-called Skinner algorithm from Monte Carlo method as well as Metropolis algorithm and simulated annealing. Under certain conditions, we prove that the Skinner automaton is expedient, ɛ-optimal, optimal, and that the operant probabilities converge to the set of stable roots with probability of 1. The Skinner automaton enables machines to autonomously learn in an animal-like way.
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