Learning to Understand by Evolving Theories
This addresses the challenge of command understanding for autonomous systems, but appears incremental as it builds on existing methods for semantic inference.
The paper tackles the problem of enabling autonomous systems to infer the semantics of commands by inducing theories from observation sequences, resulting in a semantic description of actions based on minimal background knowledge.
In this paper, we describe an approach that enables an autonomous system to infer the semantics of a command (i.e. a symbol sequence representing an action) in terms of the relations between changes in the observations and the action instances. We present a method of how to induce a theory (i.e. a semantic description) of the meaning of a command in terms of a minimal set of background knowledge. The only thing we have is a sequence of observations from which we extract what kinds of effects were caused by performing the command. This way, we yield a description of the semantics of the action and, hence, a definition.