Abstract
Specifying a reactive behavioral configuration for use by a multiagent team requires both a careful choice of the behavior set and the creation of a temporal chain of behaviors which executes the mission. This difficult task is simplified by applying an object-oriented approach to the design of the mission using a construction called an assemblage and a methodology called temporal sequencing. The assemblage construct allows building high level primitives which provide abstractions for the designer. Assemblages consist of groups of basic behaviors and coordination mechanisms that allow the group to be treated as a new coherent behavior. Upon instantiation, the assemblage is parameterized based on the specific mission requirements. Assemblages can be re-parameterized and used in other states within a mission or archived as high level primitives for use in subsequent projects. Temporal sequencing partitions the mission into discrete operating states with perceptual triggers causing transitions between those states. Several smaller independent configurations (assemblages) can then be created which each implement one state. The Societal Agent theory is presented as a basis for constructions of this form. The Configuration Description Language (CDL) is developed to capture the recursive composition of configurations in an architecture- and robot-independent fashion. The MissionLab system, an implementation based on CDL, supports the graphical construction of configurations using a visual editor. Various multiagent missions are demonstrated in simulation and on our Denning robots using these tools.
Similar content being viewed by others
References
Arbib, M.A., Kfoury, A.J., and Moll, R.N. 1981. A Basis for Theoretical Computer Science, Springer-Verlag: NY.
Arkin, R.C. 1987. Towards cosmopolitan robots: Intelligent navigation of a mobile robot in extended manmade environments. Ph.D. dissertation, University of Massachusetts, Department of computer and Information Science, COINS TR 87–80.
Arkin, R.C. 1989. Moto schema-based mobile robot navigation. The International Journal of Robotics Research, 8(4):92–112.
Arkin, R.C. and MacKenzie, D.C. 1994. Temporal coordination of perceptual algorithms for mobile robot navigation. IEEE Transactions on Robotics and Automation, 10(3):276–286.
Balch, T., Boone, G., Collins, T., Forbes, H., MacKenzie, D., and Santamaría, J. 1995. Io, ganymede and callisto—A multiagent robot trash-collecting team. AI Magazine, 16(2):39–51.
Balch, T. and Arkin, R.C. 1995. Motor-schema based formation control for multiagent robotic teams. In Proc. 1995 International Conference on Multiagent Systems, San Francisco, CA, pp. 10–16.
Bobrow, D.G. et al. 1990. Common lisp object system. In Common Lisp: The Language, edited by Guy L. Steele Jr. (Ed.), Digital Press: Chap. 28, pp. 770–864.
Brooks, R.A. 1986. A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation, 2(1):14–23.
Brooks, R.A. 1989. A robot that walks: Emergent behaviors from a carefully evolved network. Neural Computation, 1(2):253–262. Also MIT AI Memo 1091.
Brooks, R.A. 1990. The behavior language: User's guide. AI Memo 1227, MIT.
Cameron, J.M. and MacKenzie, D.C. 1996. MissionLab User Manual. College of Computing, Georgia Institute of Technology, Available via http://www.cc.gatech.edu/ai/robot-lab/research/MissionLab/mlab_manual.ps.gz, Version 1.0 edition.
Connell, J. 1989. A colony architecture for an artificial creature. AI Tech. Report 1151, MIT.
Firby, J. 1989. Adaptive execution in complex dynamic worlds. Computer Science Tech. Report YALEU/CSD/RR 672, Yale.
Georgeff, M.P. and Lansky, A.L. 1987. Reactive reasoning and planning. In Proc. AAAI Conference, pp. 677–682.
Gertz, M.W., Maxion, R.A., and Khosla, P.K. 1995. Visual programming and hypermedia implementation within a distributed laboratory environment. Intelligent Automation and Soft Computing, 1(1):43–62.
Gowdy, J. 1991. SAUSAGES Users Manual. Robotics Institute, Carnegie Mellon, version 1.0 edition. SAUSAGES: A framework for plan specification, execution, and monitoring.
Gowdy, J. 1994. Sausages: Between planning and action. Technical Report Draft, Robotics Institute, Carnegie Mellon.
Henderson, T.C. 1990. Logical behaviors. Journal of Robotic Systems, 7(3):309–336.
Henderson, T.C. and Shilcrat, E. 1984. Logical sensor systems. Journal of Robotic Systems, 1(2):169–193.
Hopcroft, J.E. and Ullman, J.D. 1979. Introduction to Automata Theory, Languages, and Computation, Addison-Wesley, p. 79.
Huber, M.J., Lee, J., Kenny, P., and Durfee, E.H. 1993. UM-PRS V1.0 Programmer and user guide. Artificial Intelligence Laboratory, The University of Michigan.
Kaelbling, L.P. 1986. Rex programmer's manual. Technical Note 381, SRI International.
Kaelbling, L.P. 1988. Goals as parallel program specifications. In Proc AAAI Conference, St. Paul, MN, vol. 1, pp. 60–65.
Kaelbling, L.P. and Rosenschein, S.J. 1990. Action and planning in embedded agents. Robotics and Autonomous Systems, 6:35–48. Also in Designing Autonomous Agents: Theory and Practice from Biology to Engineering and Back, P. Maes (Ed.), MIT Press.
Lee, B. and Hurson, A.R. 1994. Dataflow architectures and multithreading. IEEE Computer, pp. 27–39.
Lee, J., Huber, M.J., Durfee, E.H., and Kenny, P.G. 1994. UM-PRS: An implementation of the procedure reasoning system for multirobot applications. In Proc. AIAA/NASA Conference on Intelligent Robots in Field, Factory, Service, and Space (CIRFFSS'94).
Lim, W. 1992. Sal—A language for developing an agent-based architecture for mobile robots. In Proc. SPIE Conference on Mobile Robots VII, Boston, MA, pp. 285–296.
Lyons, D.M. 1993. Representing and analyzing action plans as networks of concurrent processes. IEEE Transactions on Robotics and Automation, 9(3):241–256.
Lyons, D.M. and Arbib, M.A. 1989. A formal model of computation for sensory-based robotics. IEEE Journal of Robotics and Automation, 5(3):280–293.
Maes, P. 1989. The dynamics of action selection. In Proc. Eleventh International Joint Conference on Artificial Intelligence, IJCAII-89, vol. 2, pp. 991–997.
Maes, P. 1990. Situated agents can have goals. Robotics and Autonomous Systems, 6:49–70. Also in Designing Autonomous Agents: Theory and Practice from Biology to Engineering and Back, P. Maes (Ed.), MIT Press.
Mataric, M.J., 1992a. Designing emergent behaviors: From local interactions to collective intelligence. In Proc. from Animals to Animats, Second International Conference on Simulation of Adaptive Behavior (SAB92). MIT Press.
Mataric, M.J. 1992b. Minimizing complexity in controlling a mobile robot population. In Proc. 1992 IEEE International Conference on Robotics and Automation, Nice, France.
Miller, D.J. and Lennox, R.C. 1990. An object-oriented environment for robot system architectures. In Proc. IEEE International Conference on Robotics and Automation, vol. 1, pp. 352–361, Cincinati, OH.
Minsky, M. 1986. The Society of Mind. Simon and Schuster: New York.
Parker, L.E. 1992a. Adaptive action selection for cooperative agent teams. In Proc. of 2nd International Conference on Simulation of Adaptive Behavior, in SAB, Honolulu, HA.
Parker, L.E. 1992b. Local versus global control laws for cooperative agent teams. Technical Report AI Memo No. 1357, MIT.
Parker, L.E. 1992c. A performance-based architecture for heterogeneous, situated agent cooperation. In AAAI-1992 Workshop on Cooperation Among Heterogeneous Intelligent Systems, San Jose, CA.
Parker, L.E. 1994. Heterogeneous multi-robot cooperation. Ph.D. dissertation, MIT, Department of Electrical Engineering and Computer Science.
Ramadge, P.J. and Wonham, W.M. 1987. Supervisory control of a class of discrete event process. SIAM J. Control Optimization, 25(1):206–230.
Ramadge, P.J. and Wonham, W.M. 1989. The control of discrete event systems. Proc. of the IEEE, 77–1(1):81–97.
Rosenblatt, J.K. and Payton, D.W. 1989. A fine-grained alternative to the subsumption architecture for mobile robot control. In IEEE INNS International Joint Conference on Neural Networks, vol. 2, pp. 317–323.
Rosenblatt, S.J. and Kaelbling, L.P. 1987. The synthesis of digital machines with provable epistemic properties. Technical Note 412, SRI International, Menlo Park, California.
Saffiotti, A., Konolige, K., and Ruspini, E. 1993. A multivalued logic approach to integrating planning and control. Technical Report 533, SRI Artificial Intelligence Center, Menlo Park, California.
Schneider, S.A., Chen, V.W., and Pardo-Castellote, G. 1995. The controlshell component-based real-time programming system. In Proc. IEEE International Conference on Robotics and Automation, pp. 2381–2388.
Spector, L. 1992. Supervenience in dynamic-world planning. Ph.D. dissertation, University of Maryland, Department of Computer Science. Also Tech Report CS-TR-2899 or UMIACS-TR-92–55.
Stewart, D.B. and Khosla, P.K. 1995. Rapid development of robotic applications using component-based real-time software. In Proc. Intelligent Robotics and Systems (IROS 95), vol. 1, pp. 465–470, IEEE/RSJ, IEEE Press.
Tinbergen, N. 1969. The Study of Instinct. Oxford University Press: London, second edition.
University of New Mexico. Khoros: Visual Programming System and Software Development Environment for Data Processing and Visualization.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
MacKenzie, D.C., Arkin, R. & Cameron, J.M. Multiagent Mission Specification and Execution. Autonomous Robots 4, 29–52 (1997). https://doi.org/10.1023/A:1008807102993
Issue Date:
DOI: https://doi.org/10.1023/A:1008807102993