Autonomous learning from the environment

Abstract

Contemporary artificial intelligence systems depend heavily upon their programmers for their knowledge about the world in which they must act. Robotics is only beginning to provide the sensory and motor capabilities that they require when they must interact with the physical environment. And even when such capabilities are in place, or when the interaction with the task environment is symbolic instead of physical, AI systems still need intelligent strategies for exploring their environments to acquire information about them, and to build their internal representations of them. Dr. Shen’s admirable book addresses these fundamental problems of how learning from and about the environment can be automated. It provides both a basic framework, within which Dr. Shen examines our present understanding of these matters, and an important example of a program, LIVE, that possesses novel and important capabilities for learning about its environment autonomously. Tasks calling for intelligence fall into two broad categories. In the one case, the intelligent system knows the task situation exactly, hence need not distinguish between the real world that its actions will affect and the mental world in which it plans its actions. In the other case, the intelligent system knows the actual situation only in part, hence must be concerned with incompleteness and inaccuracies of its picture of reality; for its plans will frequently fail to reach the intended goals or have undesired side effects, and it must have means for recognizing these failures, remedying them as far as possible, and re-establishing its contact with the external reality.