The goal of this research is to develop techniques that will permit a computer or robot to learn from examples to carry out multipart tasks specified in natural language on behalf of a user. It will study each of these components in isolation, but a significant focus will be on integrating them into a coherent system. The project will also leverage this technology to provide an entry point to educate non- or pre-computer science students about the capabilities and utility of computers as tools.
Our approach uses three main subcomponents, each of which requires innovative research to solve its portion of the overall problem. In addition, the integrated architecture is a novel contribution of this work. The three components are (1) recognizing intention from observed behavior using extensions of inverse reinforcement learning, (2) translating instructions to task specifications using novel techniques in the area of natural language processing, and (3) creating generalized task specifications to match user intentions using probabilistic methods for creating and managing abstractions.
The goal of the work is develop technology for an improved ability for human users to interact with intelligent agents, the incorporation of novel AI research insights and activities into education and outreach activities, and the development of resources for the AI educator community. In addition to permitting intelligent agents to be developed and trained in the future for a broad range of complex application domains, the interactive agents that we will develop will be used for outreach and student learning.