Engineering topics

Laird, John E.

Publications and source records attributed to Laird, John E..

4 records

NASA NTRS1994

Learning procedures from interactive natural language instructions

Despite its ubiquity in human learning, very little work has been done in artificial intelligence on agents that learn from interactive natural language instructions. In this paper, the problem of learning procedures from interactive, situated instruction is examined in which the student is attempting to perform tasks within the instructional domain, and asks for instruction when it is needed. Presented is Instructo-Soar, a system that behaves and learns in response to interactive natural language instructions. Instructo-Soar learns completely new procedures from sequences of instruction, and also learns how to extend its knowledge of previously known procedures to new situations. These learning tasks require both inductive and analytic learning. Instructo-Soar exhibits a multiple execution learning process in which initial learning has a rote, episodic flavor, and later executions allow the initially learned knowledge to be generalized properly.

Huffman, Scott B.↗

NASA NTRS1992

Dimensions of complexity in learning from interactive instruction

Robot systems deployed in space must exhibit flexibility. In particular, an intelligent robotic agent should not have to be reprogrammed for each of the various tasks it may face during the course of its lifetime. However, pre-programming knowledge for all of the possible tasks that may be needed is extremely difficult. Therefore, a powerful notion is that of an instructible agent, one which is able to receive task-level instructions and advice from a human advisor. An agent must do more than simply memorize the instructions it is given (this would amount to programming). Rather, after mapping instructions into task constructs that it can reason with, it must determine each instruction's proper scope of applicability. In this paper, we will examine the characteristics of instruction, and the characteristics of agents, that affect learning from instruction. We find that in addition to a myriad of linguistic concerns, both the situatedness of the instructions (their placement within the ongoing execution of tasks) and the prior domain knowledge of the agent have an impact on what can be learned.

Huffman, Scott B.↗

NASA NTRS1989

Learning in tele-autonomous systems using Soar

Robo-Soar is a high-level robot arm control system implemented in Soar. Robo-Soar learns to perform simple block manipulation tasks using advice from a human. Following learning, the system is able to perform similar tasks without external guidance. It can also learn to correct its knowledge, using its own problem solving in addition to outside guidance. Robo-Soar corrects its knowledge by accepting advice about relevance of features in its domain, using a unique integration of analytic and empirical learning techniques.

Laird, John E.↗

NASA NTRS1989

Learning from external environments using Soar

Soar, like the previous PRODIGY and Theo, is a problem-solving architecture that attempts to learn from experience; unlike them, it takes a more uniform approach, using a single forward-chaining architecture for planning and execution. Its single learning mechanism, designated 'chunking', is domain-independent. Two developmental approaches have been employed with Soar: the first of these allows the architecture to attempt a problem on its own, while the second involves a degree of external guidance. This learning through guidance is integrated with general problem-solving and autonomous learning, leading to an avoidance of human interaction for simple problems that Soar can solve on its own.

Laird, John E.↗