An Application of Context-Learning in a Goal-Seeking Neural Network

An important function of many organisms is the ability to use contextual information in order to increase the probability of achieving goals. For example, a street address has a particular meaning only in the context of the city it is in. In this paper, predisposing conditions that influence future outcomes are learned by a goal-seeking neural network called Mona. A maze problem is used as a context-learning exercise. At the beginning of the maze, an initial door choice forms a context that must be remembered until the end of the maze, where the same door must be chosen again in order to reach a goal. Mona must learn these door associations and the intervening path through the maze. Movement is accomplished by expressing responses to the environment. The goalseeking effectiveness of the neural network in a variety of maze complexities is measured.