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Issues on building software for hardware agents

Published online by Cambridge University Press:  07 July 2009

Henry Hexmoor  and
David Kortenkamp
Henry Hexmoor
Affiliation:
226 Bell Hall, Department of Computer Science, SUNY at Buffalo, Buffalo, NY 14260, USA (email: hexmore@cs. buffalo, edu)
David Kortenkamp
Affiliation:
Metrica, Inc., Robotics and Automation Group, NASA Johnson Space Center—ER4, Houston, TX 77085, USA (email: [email protected])
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Extract

James Albus states that “an architecture is a description of how a system is constructed from basic components and how those components fit together to form the whole” (Albus, 1995). A software architecture for physical agents reflects the organising principles that its designers have learned from many prior experiences in building such agents. Architectures that have been proposed for physical agents have differed greatly—from subsumption (Brooks, 1986) to Soar (Laird et al., 1987). However, a surprising consensus about architectures is beginning to emerge within the small community of researchers applying artificial intelligence to robotics. The consensus is that a multi- layer, hierarchical architecture is necessary. In particular, the community is moving towards a three-layered architecture. The lowest layer is a reactive control system inspired by subsumption (Brooks, 1986). The top layer is a traditional symbolic planning and modelling system. The middle layer is the key; it serves as a “differential” between the short-range reaction and long-range reasoning.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 10 , Issue 3 , September 1995 , pp. 301 - 304
Copyright
Copyright © Cambridge University Press 1995

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References

Agre, PE and Chapman, D, 1987. “Pengi—an implementation of a theory of activity” In: Proceedings of the Fifth National Conference on Artificial Intelligence.Google Scholar
Albus, JS, 1995. “A reference model architecture for intelligent systems” In: Working Notes: AAAI 1995 Spring Symposium on Lessons Learned from Implemented Software Architectures for Physical Agents.Google Scholar
Bonasso, RP, 1991. “Integrating reaction plans and layered competences through synchronous control” In: Proceedings International Joint Conferences on Artificial Intelligence.Google Scholar
Bonasso, RP, Antonisse, HJ and Slack, MG, 1992. “A reactive robot system for find and fetch tasks in an outdoor environment” In: Proceedings of the Tenth National Conference on Artificial Intelligence.Google Scholar
Brooks, RA, 1986. “A robust layered control system for a mobile robot” IEEE Journal of Robotics and Automation 2 (1).CrossRefGoogle Scholar
Connell, JH, 1992. “A hybrid architecture applied to robot navigation” In: Proceedings IEEE international Conference on Robotics and Automation.Google Scholar
Elsaesser, C and Slack, MG. 1994. “Integrating deliberative planning in a robot architecture” In: Proceedings of the AIAA/NASA Conference on Intelligent Robots in Field, Factory, Service, and Space (CIRFFSS '94).Google Scholar
Fikes, RE and Nilsson, NJ, 1971. “Strips: A new approach to the application of theorem proving to problem solving” Artificial Intelligence 2.CrossRefGoogle Scholar
Firby, RJ, 1987. “An investigation into reactive planning in complex domains” In: Proceedings of the National Conference on Artificial Intelligence (AAAI).Google Scholar
Firby, RJ, 1992. “Building symbolic primitives with continuous control routines” In: Proceedings International Conference on AI Planning Systems.CrossRefGoogle Scholar
Firby, RJ, 1994. “Task networks for controlling continuous processes” In: Proceedings of the Second International Conference on AI Planning Systems.Google Scholar
Gat, E, 1992. “Integrating planning and reacting in a heterogeneous asynchronous architecture for controlling real-world mobile robots” In: Proceedings of the National Conference on Artificial Intelligence (AAAI).Google Scholar
Gat, E, 1995. “On the role of simulation in the study of autonomous mobile robots” In: Working Notes: AAAI 1995 Spring Symposium on Lessons Learned from Implemented Software Architectures for Physical Agents.Google Scholar
Hexmoor, H, Lammens, J and Shapiro, SC, 1993. “Behavior based Al, cognitive processes, and emergent behaviors in autonomous agents” In: Rzevski, G, Pastor, J and Adey, R (eds), Applications of AI in Engineering VIII, Vol. 2, Applications and Techniques. CMI/Elsevier.Google Scholar
Laird, JE, Newell, A and Rosenbloom, PS, 1987. “Soar: An architecture for general intelligence” Artificial Intelligence 33 (1).CrossRefGoogle Scholar
Lee, J and Durfee, EH, 1994. “Structured circuit semantics for reactive plan execution systems” In: Proceedings of the Twelth National Conference on Artificial Intelligence, 1232–1237.Google Scholar
Musliner, DJ, Hendler, JA, Agrawala, AK, Durfee, EH, Strosnider, JK and Paul, CJ, 1995. “The challenges of real-time Al” IEEE Computer 28 (1).CrossRefGoogle Scholar
Musliner, DJ, Durfee, E and Skin, K, 1993. “CIRCA: A cooperative, intelligent, real-time control architecture” IEEE Transactions on Systems, Man and Cybernetics 23 (6).Google Scholar
Newell, A, 1990. Unified Theories of cognition. Harvard University Press.Google Scholar
Nilson, NJ, 1969. “A mobile automation: An application of Al techniques” In: Proceedings of the International Joint Conference on Artificial Intelligence.CrossRefGoogle Scholar
Nilsson, NJ, 1994. “Teleo-reactive programs for agent control” Journal of Artificial Intelligence Research 1 (1).CrossRefGoogle Scholar
Pavio, A, 1986. Mental Representations: A dual coding approach, Oxford University Press.Google Scholar
Rosenschein, SJ and Kaelbling, LP, 1988. “The synthesis of digital machines with provable epistemic properties” In: Proceedings of the Conference on TheoreticalAspects of Reasoning About Knowledge. 83–98.CrossRefGoogle Scholar
Sanborn, J, Bloom, B and McGrath, D, 1989. “A situated reasoning architecture for space-based repair and replace tasks” In: Goddard Conference on Space Applications of Artificial Intelligence (NASA Publication 3033).CrossRefGoogle Scholar
Schoppers, M, 1987. “Universal plans for reactive robots in unpredictable environments” In: Proceedings of the International Joint Conferences on Artifi cial Intelligence (IJCAI).Google Scholar
Simon, H, 1983. “Why should machines learn?” In: Carbonell, J, Michalski, R and Mitchell, T (eds.), Machine Learning: An Artificial Intelligence Approach, Tioga Press.Google Scholar