This paper analyzes the suitability of reinforcement learning (RL) for both programming and adapting situated agents. We discuss two RL algorithms: Q-learning and the Bucket Brigade. We introduce a special case of the Bucket Brigade, and analyze and compare its…

We develop a novel autonomous control synthesis strategy called Phase Space Navigator for the automatic synthesis of nonlinear control systems. The Phase Space Navigator generates global control laws by synthesizing flow shapes of dynamical systems and planning and navigating system…

We develop a qualitative method for understanding and representing phase space structures of complex systems and demonstrate the method with a program, MAPS --- Modeler and Analyzer for Phase Spaces, using deep domain knowledge of dynamical system theory. Given a…

A key problem in model-based object recognition is selection, namely, the problem of determining which regions in the image are likely to come from a single object. In this paper we present an approach that extracts and uses color region…

If we are to understand the innately human ability to solve complex problems, we must first understand the cognitive processes that allow us to combine different kinds of knowledge, to learn new things and to communicate with other people. I…

Takeshi Yoshioka & George Herman., "October 1999.", Includes bibliographical references (p. 24-25).

Mark Klein., "October 1999.", Includes bibliographical references (p. 15-16).

Stephane Bressan ... [et al.], "October, 1999.", Includes bibliographical references (p. 13-14).

The goal of the work reported here is to capture the commonsense knowledge of non-expert human contributors. Achieving this goal will enable more intelligent human-computer interfaces and pave the way for computers to reason about our world. In the domain…

by Nina Kruschwitz and George Roth., "March 1999.", Includes bibliographical references.