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Dimensions of complexity in learning from interactive instructionRobot 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.
Document ID
19930045114
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Huffman, Scott B.
(NASA Ames Research Center Moffett Field, CA, United States)
Laird, John E.
(Michigan Univ. Ann Arbor, United States)
Date Acquired
August 16, 2013
Publication Date
January 1, 1992
Publication Information
Publication: In: Cooperative intelligent robotics in space III; Proceedings of the Meeting, Boston, MA, Nov. 16-18, 1992 (A93-29101 10-54)
Publisher: Society of Photo-Optical Instrumentation Engineers
Subject Category
Man/System Technology And Life Support
Accession Number
93A29111
Funding Number(s)
CONTRACT_GRANT: NCC2-517
Distribution Limits
Public
Copyright
Other

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