What am I doing?: automatic construction of an agent's state-transition diagram through introspection
Pages 189 - 196
Abstract
Infrastructures for implementing agent architectures are currently unaware of what tasks the implemented agent is performing. Such knowledge would allow the infrastructure to improve the agent's autonomy and reliability. For example, the infrastructure could detect abnormal system states, predict likely faults and take preventive measures ahead of time, or balance system load based on predicted computational needs. In this paper we introduce a learning algorithm to automatically discover a state-transition model of the agent's behavior. The algorithm monitors the communication between architectural components, in the form of function calls, and finds the frequencies at which various functions are polled. It then determines the states according to what polling frequencies are active at any time. The two main novel features of the algorithm are that it is completely unsupervised (it requires no human input) and task-agnostic (it can be applied to any new task or architecture with minimal effort).
References
[1]
E. Charniak. Statistical Language Learning. Language, Speech, and Communication. MIT Press, 1996.
[2]
F. Delmotte and M. Egerstedt. Reconstruction of low-complexity control programs from data. In 43rd IEEE Conference on Decision and Control, volume 2, pages 1460--1465, 2004.
[3]
M. Fox, M. Ghallab, G. Infantes, and D. Long. Robot Introspection through Learned Hidden Markov Models. Artificial Intelligence, 170(2):59--113, 2006.
[4]
D. Goldberg and M. J. Mataric. Augmented Markov Models, 1999.
[5]
A. Guillory, H. Nguyen, T. Balch, and C. L. Isbell. Learning executable agent behaviors from observation. In Proc. of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems, 2006.
[6]
H. Jhuang, E. Garrote, X. Yu, V. Khilnani, T. Poggio, A. D. Steele, and T. Serre. Automated home-cage behavioural phenotyping of mice. Nature communications, 1(6):68, 2010.
[7]
J. Kramer and M. Scheutz. Robotic development environments for autonomous mobile robots: A survey. Autonomous Robots, 22(2):101--132, 2007.
[8]
D. Park. Concurrency and automata on infinite sequences. In P. Deussen, editor, Theoretical Computer Science, volume 104 of Lecture Notes in Computer Science, pages 167--183. Springer Berlin/Heidelberg, 1981. 10.1007/BFb0017309.
[9]
L. Rabiner. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257--286, feb 1989.
[10]
M. Scheutz. ADE - Steps towards a distributed development and runtime environment for complex robotic agent architectures. Applied Artificial Intelligence, 20(4--5), 2006.
[11]
D. Stronger and P. Stone. Towards autonomous sensor and actuator model induction on a mobile robot. Connection Science, 18(2):97--119, June 2006.
[12]
S. A. Wallace. S-assess: a library for behavioral self-assessment. In Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems, AAMAS '05, pages 256--263, New York, NY, USA, 2005. ACM.
[13]
J. Weng, J. McClelland, A. Pentland, O. Sporns, I. Stockman, M. Sur, and E. Thelen. Artificial intelligence. Autonomous mental development by robots and animals. Science, 291(5504):599--600, January 2001.
Index Terms
- What am I doing?: automatic construction of an agent's state-transition diagram through introspection
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Information
Published In
June 2012
592 pages
ISBN:0981738117
- General Chairs:
- Wiebe van der Hoek,
- Lin Padgham,
- Program Chairs:
- Vincent Conitzer,
- Michael Winikoff
Sponsors
- The International Foundation for Autonomous Agents and Multiagent Systems: The International Foundation for Autonomous Agents and Multiagent Systems
In-Cooperation
Publisher
International Foundation for Autonomous Agents and Multiagent Systems
Richland, SC
Publication History
Published: 04 June 2012
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AAMAS 12
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- The International Foundation for Autonomous Agents and Multiagent Systems
AAMAS 12: International Conference on Autonomous Agents and Multiagent Systems
June 4 - 8, 2012
Valencia, Spain
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Overall Acceptance Rate 1,155 of 5,036 submissions, 23%
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