Coordinating agile systems through the model-based execution of temporal plans

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2005, Pages 114-120

  Léauté, Thomas ^a   Williams, Brian C ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARK PERFORMANCE; NEAR-OPTIMAL CONTROL SEQUENCE; UNMANNED AERIAL VEHICLES (UAVS);

MATHEMATICAL MODELS; OPTIMAL CONTROL SYSTEMS; PERFORMANCE; PROBLEM SOLVING; ROBUSTNESS (CONTROL SYSTEMS); UNMANNED VEHICLES;

ARTIFICIAL INTELLIGENCE;

EID: 29344473582     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Ambros-Ingerson, J. A., and Steel, S. 1988, Integrating planning, execution and monitoring. In Proceedings of the Seventh National Conference on Artificial Intelligence. AAAI Press.
2. Balas, E. 1979, Disjunctive programming. Annals of Discrete Mathematics 5:3-51.
3. Bellingham, J.; Richards, A.; and How, J. 2002. Receding horizon control of autonomous aerial vehicles. In Proceedings of the American Control Conference.
4. Chien, S.; Knight, R.; Stechert, A.; Sherwood, R.; and Rabideau, G. 2000. Using iterative repair to improve responsiveness of planning and scheduling. In Proceedings of the Fifth International Conference on Artificial Intelligence Planning and Scheduling.
5. Dechter, R.; Meiri, I.; and Pearl, J. 1991, Temporal constraint networks. Artificial Intelligence Journal.
6. Hofbaur, M. W., and Williams, B. C. 2004. Hybrid estimation of complex systems. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics.
7. Krishnan, R. 2004. Solving hybrid decision-control problems through conflict-directed branch and bound. Master's thesis, MIT.
8. Kuwata, Y. 2003. Real-time trajectory design for unmanned aerial vehicles using receding horizon control. Master's thesis, MIT.
9. Li, H., and Williams, B. C. 2005. Efficiently solving hybrid logic/optimization problems through generalized conflict learning. ICAPS Workshop 'Plan Execution: A Reality Check', http://mers.csail.mit.edu/mers- publications.htm.
10. Morris, P.; Muscettola, N.; and Tsamardinos, I. 1998. Reformulating temporal plans for efficient execution. In Proceedings of the International Conference on Principles of Knowledge Representation and Reasoning, 444-452.
11. Propoi, A. 1963. Use of linear programming methods for synthesizing sampled-data automatic systems. Automation and Remote Control 24(7):837-844.
12. Richalet, J.; Rault, A.; Testud, J.; and Papon, J. 1976. Algorithmic control of industrial processes. In Proceedings of the 4th IFAC Symposium on Identification and System Parameter Estimation, 1119-1167.
13. Tiwari, A. 2003. Approximate reachability for linear systems. In Proceedings of HSCC, 514-525.
14. Tsamardinos, I.; Pollack, M. E.; and Ramakrishnan, S. 2003. Assessing the probability of legal execution of plans with temporal uncertainty. In Proc. ICAPS.
15. Vidal, T., and Ghallab, M. 1996. Dealing with uncertain durations in temporal constraint networks dedicated to planning. In Proc. ECAI, 48-52.
16. Wilkins, D. E., and Myers, K. L. 1995. A common knowledge representation for plan generation and reactive execution. Journal of Logic and Computation 5(6):731-761.
17. Williams, B. C. 2003. Model-based programming of intelligent embedded systems and robotic space explorers. In Proc. IEEE: Modeling and Design of Embedded Software.