In this multi-part article, I will describe the learning and behaving mechanisms of Animal, a chess learning program. Even though I have been working on it for many years, Animal is not yet ready to be released because I am still doing research on its brain. As you can guess, the brain is the tricky part. In this post, I describe the essential philosophy behind Animal's brain mechanism.
Click to EnlargeThe Tree of Knowledge
Animal is a chess learning computer program that uses a spike-driven neural network for learning and behaving. Unlike traditional computer chess programs, Animal does not conduct an extensive search of the game tree. Rather, Animal pretty much learns to play chess the way we do, through trial and error. As it plays, it uses a hierarchical tree of knowledge (TOK) to store various temporal patterns in memory. Each branch of the tree represents a specific pattern and can be recalled (activated) every time the pattern is encountered. The TOK is not just used for storing learned patterns, however. Animal also uses the TOK for making predictions about possible outcomes. This predictive ability is the basis of Animal's intelligence.
In Animal's universe, an event is a phenomenon or a change in some sensed property. Animal's learning mechanism is strictly driven by change. Animal's sensors, for example, do not detect that there is a particular chess piece at a particular square position on the board. Rather, they detect whether or not a chess piece has moved from/to a particular position. Upon detecting an event, a sensor will generate a signal or spike. A spike is a temporary marker that alerts a receiving processor (spiking neuron) in Animal's brain (spiking neural network) that an event just occurred. Animal uses many sensors that generate a constant stream of spikes.
One of the most surprising aspects of Animal's brain is that the provenance of a spike is completely irrelevant to the learning mechanism. That is to say, whether a spike was caused by the motion of a pawn or a bishop is unimportant. The temporal relationships between the spikes are all that is required for learning. In fact, Animal's learning mechanism does not care about chess, tic-tac-toe, or any other environment. Animal needs only to be presented with a stream of sensory events to discover the temporal patterns and learn to recognize them.
Animal uses both sensors and effectors to interact with its environment. As with sensors, Animal's brain does not care about the destination of its motor signals. It uses whatever effectors it is given. This universality is what gives Animal's learning mechanism its power. Once the brain is perfected, it can be used in any type of situation where intelligence is required. Just plug in an appropriate set of sensors and effectors and watch it learn and behave intelligently.
In Part II, I will go over sensors and the temporal learning mechanism in greater detail.
The Brain: Universal Invariant Recognition