Navigational Map Learning by Context Chaining and Abstraction

Efficient navigation of one’s environment is a fundamental requirement of a successful mobile robot. Ideally an agent’s interactions with an unmarked environment should build reliable spatial relationship information without the aid of foreknowledge. Problems arise when different parts of the environment “look ” similar to the agent, confusing the agent as to its true position. This problem has become known as ‘perceptual aliasing’. In this research project this problem was approached by introducing and investigating the chaining of dynamic virtual landmark identification in the agent’s environment. The learning method introduced, called “context chaining ” is an extension of the Interactivist-Expectancy Theory of Learning (IETAL). Experiments with software agent simulations showed the success of this approach, measured by the number of steps required to reach the drive satisfier, cumulative memory size, and the number of surprises encountered