The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Teleo-Reactive Programs for Agent Control
Open accessAbstract
A formalism is presented for computing and organizing actions for autonomous agents in dynamic environments. We introduce the notion of teleo-reactive (T-R) programs whose execution entails the construction of circuitry for the continuous computation of the parameters and conditions on which agent action is based. In addition to continuous feedback, T-R programs support parameter binding and recursion. A primary difference between T-R programs and many other circuit-based systems is that the circuitry of T-R programs is more compact; it is constructed at run time and thus does not have to anticipate all the contingencies that might arise over all possible runs. In addition, T-R programs are intuitive and easy to write and are written in a form that is compatible with automatic planning and learning methods. We briefly describe some experimental applications of T-R programs in the control of simulated and actual mobile robots.
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Cites
A robust layered control system for a mobile robot
Brooks, R. (1986) · IEEE Journal on Robotics and Automation · doi:10.1109/jra.1986.1087032 · Cited by 7,799
Principles of Artificial Intelligence
Nilsson, N. J. (1982) · doi:10.1007/978-3-662-09438-9 · Cited by 3,457 · cited 1×
“The conditions are therefore regressions (Nilsson, 1980) of higher conditions through the actions that achieve those higher conditions.”
BackgroundSelf-citationThe control of discrete event systems
Ramadge, P. J. G., & Wonham, W. M. (1989) · Proceedings of the IEEE · doi:10.1109/5.21072 · Cited by 2,937
The Esterel synchronous programming language: design, semantics, implementation
Berry, G., & Gonthier, G. (1992) · Science of Computer Programming · doi:10.1016/0167-6423(92)90005-v · Cited by 1,739
Explanation-Based Generalization: A Unifying View
Mitchell, T. M., Keller, R. M., & Kedar-Cabelli, S. T. (1986) · Machine Learning · doi:10.1023/a:1022691120807 · Cited by 1,382
Learning and executing generalized robot plans
Fikes, R. E., Hart, P. E., & Nilsson, N. J. (1972) · Artificial Intelligence · doi:10.1016/0004-3702(72)90051-3 · Cited by 1,019
Self-citationReactive reasoning and planning
Georgeff, M., & Lansky, A. (1987) · National Conference on Artificial Intelligence · Cited by 888
Rivest, R. L. (1987) · Machine Learning · doi:10.1007/bf00058680 · Cited by 886
MAES, P. (1989) · Connection Science · doi:10.1080/09540098908915643 · Cited by 486
Universal plans for reactive robots in unpredictable environments
Schoppers, M. (1987) · Cited by 476
Cited by
Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
Sutton, R. S., Precup, D., & Singh, S. (1999) · Artificial Intelligence · doi:10.1016/s0004-3702(99)00052-1 · Cited by 3,119
Artificial Intelligence: A New Synthesis
(1998) · Elsevier eBooks · doi:10.1016/c2009-0-27773-7 · Cited by 1,163
Metadata
- Authors
- Nilsson, N.
- Venue
- Journal of Artificial Intelligence Research
- Publisher
- AI Access Foundation
- Published
- 1994-01-01
- Volume
- 1
- Pages
- 139-158
- ISSN
- 1076-9757 (ISSN-L)
- OpenAlex
- W1699699942
- Cited by
- 366
- References
- 26
- FWCI
- 19.86
- Type
- journal article
- Language
- en
- Open access
- diamond
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