The cost of gathering and labelling data is remaining constant, while computing power is becoming cheaper. Recognizing this, the notion of a lifelong learning agent has been proposed. A lifelong learning agent is a system that learns knowledge and, when thereafter learning novel tasks, can use this knowledge to increase the accuracy of what is learned or to reduce the number of examples necessary for learning. Many open problems exist: can an agent exploit multiple sources of learned knowledge, to what degree will an agent benefit from different types of learned knowledge, what types of knowledge should be made available, how should the agent adapt as a new task arrives, how might the order of task arrival impact learning, and of course, how should such an agent be built?
A lifelong learning agent offers the promise of learning from substantially fewer examples. Currently, we have shown situations were the number of examples required to learn a task have been halved. Understanding how and where a lifelong agent can be applied would allow systems to be built that efficiently learn novel tasks.
This problem area is still quite young and fertile, and so there is quite a wide diversity of approaches in the literature. In the majority of previous work on agents, as the agent ages and further new tasks arise, the agent does not improve its ability to learn those tasks. Of the wide variety of underlying mechanisms proposed for creating a lifelong long agent, few have been applied to a complete system, and not enough is understood about how the mechanisms scale as an agent ages. There have been several different types of lifelong learning agents systems proposed - including variants of reinforcement learning and of high level planning systems. Generally, these systems either make abstracting assumptions about the agent input, or are limited in scope.
I propose that an agent can be constructed which learns knowledge and exploits that knowledge to effectively improve further learning by reducing the number of examples required to learn. I am studying the transfer of learned knowledge by supervised life-long learning agents within a neural network based architecture capable of increasing capacity with the number of tasks faced. So far, preliminary work has been carried out in controlled settings, and an appropriate architecture has been outlined. This work has confirmed that learned knowledge can reduce the number of examples required to learn novel tasks and that combining previously separate mechanisms can yield a synergistic improvement on learning ability. It has shown that capacity can be expanded as new tasks arise over time. The underlying mechanisms that are being used for transfer include learning useful representations for a specific domain, and learning domain specific models for use in further learning. Our initial work has explored the benefits of these mechanisms separately, and how to extend these methods to operate concurrently, and as the agent ages.
The hypothesizes are being studied experimentally both in simulated domains, and will be verified on the robot test-bed in the Learning Robot Lab; Xavier which we designed and constructed internally within the laboratory in 1993, and Amelia, a commercial descendent of Xavier.
We are currently building upon the underlying mechanisms to generate a tool-box for building curriculums. That is, given that a lifelong learning agent exists, what is the best things to teach the agent, and the best way to teach them, so that we expect to learn novel tasks efficiently. We speculate that the order in which tasks arise can be exploited with a graded curriculum.
http://www.cs.cmu.edu/~Xavier, March 1994.