"Chunks have long been proposed as a basic organizational unit for human memory. More recently chunks have been used to model human learning on simple perceptual-motor skills. In this paper we describe recent progress in extending chunking to be a general learning mechanism by implementing it within a general problem solver. Using the Soar problem-solving architecture, we take significant steps toward a general problem solver that can learn about all aspects of its behavior. We demonstrate chunking in Soar on three tasks: the Eight Puzzle, Tic-Tat-Toe, and a part of the RI computer-configuration task. Not only is there improvement with practice, but chunking also produces significant transfer of learned behavior, and strategy acquisition."Proceedings of the AAAi-84 National Conference. AAAI, University of Texas at Austin, TX, August, 1984.

Active compliance enables robots to carry out tasks in the presence of significant sensing and control errors. Compliant motions are quite difficult for humans to specify, however. Furthermore, robot programs are quite sensitive to details of geometry and to error characteristics and must, therefore, be constructed anew for each task. These factors motivate the search for automatic synthesis tools for robot program ming, especially for compliant motion. This paper describes a formal approach to the synthesis of compliant-motion strategies from geometric descriptions of assembly operations and explicit estimates of errors in sensing and control.

Science 224(4655):1299-1305

Artificial Intelligence, 23, 29–58.

Artificial Intelligence, 24 (1-3), 411-36.

Artificial Intelligence 23(2):123-154

Artificial Intelligence, 23 (3), 269-94.

Artificial Intelligence 24:1-5

Artificial Intelligence, 23 (3), 249-68.

J. Logic Programming, 1, 267–284.