The Emergence of Artificial Intelligence: Learning to Learn

The classical approach to the acquisition of knowledge and reason in artificial intelligence is to program the facts and rules into the machine. Unfortunately, the amount of time required to program the equivalent of human intelligence is prohibitively large. An alternative approach allows an automaton to learn to solve problems through iterative trial-and-error interaction with its environment, much as humans do. To solve a problem posed by the environment, the automaton generates a sequence or collection of responses based on its experience. The environment evaluates the effectiveness of this collection, and reports its evaluation to the automaton. The automaton modifies its strategy accordingly, and then generates a new collection of responses. This process is repeated until the automaton converges to the correct collection of responses. The principles underlying this paradigm, known as collective learning systems theory, are explained and applied to a simple game, demonstrating robust learning and dynamic adaptivity.