Electrical computers and digital processing systems: multicomput – Distributed data processing – Processing agent
Reexamination Certificate
2001-12-04
2004-08-03
Coulter, Kenneth R. (Department: 2141)
Electrical computers and digital processing systems: multicomput
Distributed data processing
Processing agent
C706S011000
Reexamination Certificate
active
06772190
ABSTRACT:
REFERENCE TO COMPUTER PROGRAM LISTING AND TABLE APPENDICES
Computer program listings and Table appendices comprising duplicate copies of a compact disc, named “DEJI 1000-5”, accompany this application and are incorporated by reference. The appendices include the following files:
APPENDIX I.txt 59 Kbytes created Sep. 19, 2002
APPENDIX II.txt 7 Kbytes created Sep. 19, 2002
APPENDIX III.txt 3 Kbytes created Sep. 19, 2002
APPENDIX IV.txt 24 Kbytes created Sep. 19, 2002
COPYRIGHT DISCLAIMER
A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure as it appears in the U.S. Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever.
BACKGROUND
1. Field of the Invention
The invention relates to user-machine interfaces, and more particularly, to software methods and techniques for implementing an agent-oriented architecture which is useful for user-machine interfaces.
2. References
The following documents are all incorporated by reference herein.
T. Kuhme, Adaptive Action Prompting—A complementary aid to support task-oriented interaction in explorative user interfaces. Report #GIT-GVU-93-19, Georgia Institute of Technology, Dept. of Computer Science, Graphics, Visualization, and Usability Center, 1993.
L. Balint, Adaptive Dynamic Menu System. Poster Abstracts HCI International '89, Boston, September 18-22, 1989.
A. Cypher. Eager: Programming Repetitive Tasks By Example. Proc. CHI'91, pp. 33-39, 1991.
R. Beale, A. Wood, Agent-based interaction, Proceedings of HCI'94 Glasgow, 1995, pp. 239-245.
A. Wood, “Desktop Agents”, School of Computer Science, University of Birmingham, B.Sc. Dissertation, 1991.
Clarke, Smyth, “A Cooperative Computer Based on the Principles of Human Cooperation”, International Journal of Man-Machine Studies 38, pp.3-22, 1993.
N. Eisenger, N. Elshiewy, MADMAN—Multi-Agent Diary Manager, ESRC-92-7i (Economic & Social Resource Council) Internal Report, 1992.
T. Oren, G. Salomon, K. Kreitman, A. Don, “Guides: Characterizing the Interface”, in The Art of Human-Computer Interface Design, Brenda Laurel (ed.), 1990 (pp.367-381).
F. Menczer, R. K. Belew, Adaptive Information Agents in Distributed Textual Environments, Proceedings of the Second International Conference on Autonomous Agents (Agents '98), Minneapolis, Minn., May 1998.
P. Brazdil, M. Gams, S. Sian, L. Torgo, W. van de Velde, Learning in Distributed Systems and Multi-Agent Environments, http://www.ncc.up.pt/~ltorgo/Papers/LDSME/LDSME-Contents.html (visited 1998).
B. Hodjat, M. Amamiya, The Self-organizing symbiotic agent, hhttp://www_al.is.kyushu-u.ac.jp/~bobby/1stpaper.htm, 1998.
P. R. Cohen, A. Cheyer, M. Wang, S. C. Baeg, OAA: An Open Agent Architecture, AAAI Spring Symposium, 1994, http://www.ai.sri.com/~cheyer/papers/aaai/adam-agent.html (visited 1998).
S. Franklin, A. Graesser, Is it an Agent or just a Program? A Taxonomy for Autonomous Agents, in: Proceedings of the Third International Workshop on Agents Theories, Architectures, and Languages, Springer-Verlag,1996, http://www.msci.memphis.edu/~Franklin/AgentProg.html (visited 1998).
B. Hayes-Roth, K. Pfleger, P. Lalanda, P. Morignot, M. Balabanovic, A domain-specific Software Architecture for adaptive intelligent systems, IEEE Transactions on Software Engineering, April 1995, pp. 288-301.
Y. Shoham, Agent-oriented Programming, Artificial Intelligence, Vol. 60, No. 1, pages 51-92, 1993.
M. R Genesereth, S. P. Ketchpel, Software Agents, Communications of the ACM, Vol. 37, No. 7, July 1994, pp. 48-53, 147.
A. Cheyer, L. Julia, Multimodal Maps: An Agent-based Approach, http:/www.ai.sri.com/~cheyer/papers/mmap/mmap.html, 1996.
T. Khedro, M. Genesereth, The federation architecture for interoperable agent-based concurrent engineering systems. In International Journal on Concurrent Engineering, Research and Applications, Vol. 2, pages 125-131, 1994.
P. Brazdil and S. Muggleton: “Learning to Relate Terms in Multiple Agent Environment”, Proceedings of Machine Learning—EWSL-91, pp. 424-439, Springer-Verlag, 1991.
S. Cranefield, M. Purvis, An agent-based architecture for software tool coordination, in Proceedings of the Workshop on Theoretical and Practical Foundations of Intelligent Agents, Springer, 1996.
T. Finin, J. Weber, G. Wiederhold, M. Genesereth, R. Fritzson, D. McKay, J. McGuire, S. Shapiro, C. Beck, Specification of the KQML Agent-Communication Language, 1993 (hereinafter “KQML 1993”), http://www.cs.umbc.edu/kqml/kqmlspec/spec.html (visited 1998).
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3. Description of Related Art
Most human-machine interfaces in use today are relatively complicated and difficult to use. Frequently this is a consequence of the growing number of features to which the interface is expected to provide easy access.
Users usually have the following problems with current interfaces:
Prior to selecting an action, users have to consider whether the machine provides an appropriate action at all. It would therefore be desirable if the interface could provide feedback to the user.
It is difficult to access the actions users already know about. It would therefore be desirable if the user could freely express his or her needs without being bound to a limited set of conventions preset by the interface.
Users have to imagine what would be an appropriate action to proceed with in order to perform a certain task of the machine domain. It would therefore be desirable if the interface could guide users through the many options they may have at any stage of the interaction.
User interfaces that adapt their characteristics to those of the user are referred to as adaptive interfaces. These interactive software systems improve their ability to interact with a user based on partial experience with that user. The user's decisions offer a ready source of training data to support learning. Every time the interface suggests some choice, the human either accepts that recommendation or rejects it, whether this feedback is explicit or simply reflected in the user's behavior.
The following general features may be desirable in a user interface:
Natural Expression: The user should be able to express his or her intentions as freely and naturally as possible.
Optimum Interaction: Interaction should be limited to the situations in which the user is in doubt as to what she/he can do next or how she/he can do it, or the system is in doubt as to what the user intends to do next. Note here that lack of interaction or feedback from the system is not necessarily desirable. Interaction is considered optimum if it occurs where it is required, no more often and no less often.
Adaptability: Adaptability could be about the changing context of interaction or application, but more importantly, the system should be able to adapt to the user's way of expressing her/his intentions. Two main issues that are taken into account in this regard are generalization and contradiction recovery.
Generalization: An adaptable system in its simplest form will learn only the instance that it has been taught (implicitly or explicitly). Generalization occurs when the system uses what it has learned to resolve problems it deems similar. The success and degree of generalization, therefore, depend on the precision of the similarity function and the threshold the system uses to distinguish between similar and dissimilar situations.
Amamiya Makoto
Hodjat Babak
Savoie Christopher J.
Coulter Kenneth R.
Dejima, Inc.
Haynes Beffel & Wolfeld LLP
Wolfeld Warren S.
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