Abstract
Local search has been proposed as a means of responding to changes in problem context requiring replanning. Iterative repair and iterative improvement have desirable properties of preference for plan stability (e.g., non-disruption, minimizing change), and have performed well in a number of practical applications. However, there has been little real empirical evidence to support this case. This paper focuses on the use of local search to support a continuous planning process (e.g., continuously replanning to account for problem changes) as is appropriate for autonomous spacecraft control. We describe results from ongoing empirical tests using the CASPER system to evaluate the effectiveness of local search to replanning using a number of spacecraft scenario simulations including landed operations on a comet and rover operations.
This work was performed by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
S. Chien, G. Rabideau, J. Willis, and T. Mann, “Automating Planning and Scheduling of Shuttle Payload Operations,” Artificial Intelligence Journal, 114 (1999) 239–255.
NASA Ames & JPL, Remote Agent Experiment Web Page, http://rax.arc.nasa.gov/, 1999.
S. Minton “Automatically Configuring Constraint Satisfaction Programs: A Case Study.” Constraints 1:1(7–43). 1996.
M. Zweben, B. Daun, E. Davis, and M. Deale, “Scheduling and Rescheduling with Iterative Repair,” in Intelligent Scheduling, Morgan Kaufman, San Francisco, 1994.
S. Chien, G. Rabideau, R. Knight, R. Sherwood, B. Engelhardt, D. Mutz, T. Estlin, B. Smith, F. Fisher, T. Barrett, G. Stebbins, D. Tran, “ASPEN-Automating Space Mission Operations using Automated Planning and Scheduling,” Space Operations 2000, Toulouse, France, June 2000.
S. Smith, “OPIS: An Architecture and Methodology for Reactive Scheduling,” in Intelligent Scheduling, Morgan Kaufman, 1994.
S. Chien, R. Knight, A. Stechert, R. Sherwood, and G. Rabideau, “Using Iterative Repair to Improve Responsiveness of Planning and Scheduling,” Proc. 5th International Conference on Artificial Intelligence Planning and Scheduling, Breckenridge, CO, April 2000.
G. Rabideau, R. Knight, S. Chien, A. Fukunaga, A. Govindjee, “Iterative Repair Planning for Spacecraft Operations in the ASPEN System,” Int Symp on Artificial Intelligence Robotics and Aut. in Space (ISAIRAS), Noordwijk, The Netherlands, June 1999.
Jonsson, P. Morris, N. Muscettola, K. Rajan and B. Smith, “Planning in Interplanetary Space: Theory and Practice,” Proceedings of the Fifth International Conference on Artificial Intelligence Planning Systems. Breckenridge, CO. April, 2000.
R. Volpe, J. Balaram, T. Ohm, and R. Ivlev, “Rocky 7: A Next Generation Mars Rover Prototype,” Journal of Advanced Robotics, 11(4), December 1997.
S. Hayati, and R. Arvidson, “Long Range Science Rover (Rocky 7) Mojave Desert Field Tests,” Proceedings of the 1997 International Symposium on Artificial Intelligence, Robotics and Automation in Space, Tokyo, Japan, July 1997.
M. Ginsberg and A. Parkes, “Supermodels and Robustness,” Proceedins of AAAI-98.
S. Lin and B. Kernighan, “An Effective Heuristic for the Traveling Salesman Problem,” Operations Research Vol. 21, 1973.
E. Biefeld and L. Cooper, “Bottleneck Identification Using Process Chronologies,” Proceedings of the 1991 International Joint Conference on Artificial Intelligence, Sydney, Australia, 1991.
S. Chien and G. DeJong, “Constructing Simplified Plans via Truth Criteria Approximation,” Proceedings of the Second International Conference on Artificial Intelligence Planning Systems, Chicago, IL, June 1994, pp. 19–24.
K. Hammond, “Case-based Planning: Viewing Planning as a Memory Task,” Academic Press, San Diego, 1989.
G. Sussman, “A Computational Model of Skill Acquisition,” Technical Report, MIT Artificial Intelligence Laboratory, 1973.
M. Johnston and S. Minton, “Analyzing a Heuristic Strategy for Constraint Satisfaction and Scheduling,” in Intelligent Scheduling, Morgan Kaufman, San Francisco, 1994.
Nareyek, “A Planning Model for Agents in Dynamic and Unicertain Real-Time Environments,” in Integrating Planning, Scheduling, and Execution in Dynamic and Uncertain Environments, AIPS98 Workshop, AAAI Technical Report WS-98092.
M. Cox & M. Veloso, “Goal Transformation in Continuous Pannning,” in Proceedings of the AAAI Fall Symposium on Distributed Continual Planning, 1998.
M. Veloso, M. Pollack, M. Cox, “Rationale-based monitoring for planning in dynamic environments,” Proceedings Artificial Intelligence Planning Systems Conference, Pittsburgh, PA, 1998.
K. Myers, “Towards a Framework for Continuous Planning and Execution”, in Proceedings of the AAAI Fall Symposium on Distributed Continual Planning, 1998.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chien, S., Knight, R., Rabideau, G. (2001). An Empirical Evaluation of the Effectiveness of Local Search for Replanning. In: Nareyek, A. (eds) Local Search for Planning and Scheduling. LSPS 2000. Lecture Notes in Computer Science(), vol 2148. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45612-0_5
Download citation
DOI: https://doi.org/10.1007/3-540-45612-0_5
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42898-5
Online ISBN: 978-3-540-45612-4
eBook Packages: Springer Book Archive