RAMBOT (Restructuring Associative Memory Based on Training): A Connectionist Expert System That Learns by Example

摘要

Expert systems seem to be quite the rage in artificial intelligence, but getting expert knowledge into these systems is a difficult problem. One solution would be to endow the systems with powerful learning procedures which could discover appropriate behaviors by observing an expert in action. A promising source of such learning procedures can be found in recent work on connectionist networks, that is, massively parallel networks of simple processing elements. This paper discusses a connectionist expert system that learns to play a simple video game by observing a human player. The game, Robots, is played on a two-dimensional board containing the player and a number of computer-controlled robots. The object of the game is for the player to move around the board in a manner that will force all of the robots to collide with one another before any robot is able to catch the player. The connectionist system learns to associate observed situations on the board with observed moves. It is capable not only of the human player, but of learning generalizations that apply to novel situations. Keywords: Parallel distributed processing.