Duration prediction for proactive replanning

Proactive replanning attempts to predict scheduling problems or opportunities and adapt to them throughout a schedule's execution. By continuously predicting a task's remaining duration, a proactive replanner is able to accommodate upcoming problems or opportunities before they manifest themselves. We have developed a kernel density estimation-based method for predicting a task's duration distribution as it executes, and have integrated our prediction algorithm with an existing planner based on heuristic repair. Our predictor allows the planner to anticipate problems, or opportunities, early enough to avoid, or take advantage of, them, resulting in executed schedules that score significantly higher on a number of metrics. We have evaluated a limited form of our approach in simulation, and present the results of our experiments. The addition of duration prediction resulted in a 11.1% improvement in average reward. Compared with an omniscient planner, this is 45.0% of the maximum possible improvement.

[1]  Jerome H. Friedman.  Multivariate adaptive regression splines (with discussion) , 1991 .

[2]  Fred L. Bookstein,et al.  Principal Warps: Thin-Plate Splines and the Decomposition of Deformations , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[4]  Reid Simmons,et al.  Towards Proactive Replanning for Multi-Robot Teams , 2006 .

[5]  Tara Estlin,et al.  CLARAty: an architecture for reusable robotic software , 2003, SPIE Defense + Commercial Sensing.

[6]  Rachid Alami,et al.  An Architecture for Autonomy , 1998, Int. J. Robotics Res..

[7]  Nicola Muscettola,et al.  IDEA: Planning at the Core of Autonomous Reactive Agents , 2002 .

[8]  William A. Woods,et al.  Computational Linguistics Transition Network Grammars for Natural Language Analysis , 2022 .

[9]  Rob Sherwood,et al.  Using Iterative Repair to Improve the Responsiveness of Planning and Scheduling , 2000, AIPS.

[10]  Jeremy Frank,et al.  Constraint-Based Attribute and Interval Planning , 2003, Constraints.

[11]  Reid G. Simmons,et al.  A task description language for robot control , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[12]  Thomas Lengauer,et al.  Ensemble Methods for Classification in Cheminformatics , 2004, J. Chem. Inf. Model..

[13]  Tara Estlin,et al.  Decision making in a robotic architecture for autonomy , 2001 .

[14]  Malik Ghallab,et al.  Planning with Sharable Resource Constraints , 1995, IJCAI.

[15]  Joachim Hertzberg,et al.  Learning to optimize mobile robot navigation based on HTN plans , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[16]  J. Friedman.  Multivariate adaptive regression splines , 1990 .

[17]  Huaiyu Zhu.  On Information and Sufficiency , 1997 .

[18]  Rob Sherwood,et al.  ASPEN-Automated Planning and Scheduling for Space Mission Operation , 2000 .

[19]  S. Kay.  Fundamentals of statistical signal processing: estimation theory , 1993 .

[20]  David P. Miller,et al.  Experiences with an architecture for intelligent, reactive agents , 1995, J. Exp. Theor. Artif. Intell..

[21]  Erann Gat,et al.  Integrating Planning and Reacting in a Heterogeneous Asynchronous Architecture for Controlling Real-World Mobile Robots , 1992, AAAI.