Cumulative Learning with Causal-Relational Models

摘要

In the quest for artificial general intelligence (AGI), questions remain about what kinds of representations are needed for the kind of flexibility called for by complex environments like the physical world. A capacity for continued learning of many domains has yet to be realized, and proposals for how to achieve general performance improvement through continuous cumulative learning-while seemingly a necessary feature of any AGI-remain scarce. In this paper we describe a cumulative learning mechanism that produces causal-relational models of its environment, to predict events and achieve goals. We show how such models, coupled with an appropriate modeling process, result in knowledge whose accuracy increases over time and can run continuously throughout the lifetime of an agent. The methods have been implemented, demonstrating learning of complex tasks and situated grammatically-correct natural language by observation. Here we focus on key theoretical principles of the modeling method and explain how effective cumulative learning is achieved.