Learning Situation-Dependent Rules: Improving Task Planning for an Incompletely Modelled Domain

Most real world environments are hard to model completely and correctly, especially to model the dynamics of the environment. In this paper we present our work to improve a domain model through learning from execution, thereby improving a task planner’s performance. Our system collects execution traces from the robot, and automatically extracts relevant information to improve the domain model. We introduce the concept of situation-dependent rules, where situational features are used to identify the conditions that affect action achievability. The system then converts this execution knowledge into a symbolic representation that the planner can use to generate plans appropriate for given situations.

[1]  Karen Zita Haigh,et al.  Interleaving Planning and Robot Execution for Asynchronous User Requests , 1998, Auton. Robots.

[2]  Douglas J. Pearson.  Learning Procedural Planning Knowledge in Complex Environments , 1996, AAAI/IAAI, Vol. 2.

[3]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[4]  K. Haigh,et al.  A Layered Architecture for Ooce Delivery Robots , 1997 .

[5]  Andrew W. Moore,et al.  Generalization in Reinforcement Learning: Safely Approximating the Value Function , 1994, NIPS.

[6]  Eugene Fink,et al.  Integrating planning and learning: the PRODIGY architecture , 1995, J. Exp. Theor. Artif. Intell..

[7]  Karen Zita Haigh,et al.  A Layered Architecture for O ce Delivery Robots , 1997 .

[8]  Karen Zita Haigh,et al.  Situation-dependent learning for interleaved planning and robot execution , 1998 .

[9]  Karen Zita Haigh,et al.  A layered architecture for office delivery robots , 1997, AGENTS '97.

[10]  Andrew McCallum,et al.  Reinforcement learning with selective perception and hidden state , 1996 .

[11]  Karen Zita Haigh,et al.  Learning situation-dependent costs: improving planning from probabilistic robot execution , 1998, AGENTS '98.

[12]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[13]  Xuemei Wang,et al.  Learning Planning Operators by Observation and Practice , 1994, AIPS.

[14]  Wei-Min Shen,et al.  Autonomous learning from the environment , 1994 .

[15]  Cristina Baroglio,et al.  Learning Controllers for Industrial Robots , 2005, Machine Learning.

[16]  Ming Tan,et al.  Cost-sensitive robot learning , 1991 .

[17]  Dean A. Pomerleau,et al.  Neural Network Perception for Mobile Robot Guidance , 1993 .

[18]  R. R. Murphy,et al.  Learning the expected utility of sensors and algorithms , 1994, Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems.

[19]  Leemon C. Baird,et al.  Residual Algorithms: Reinforcement Learning with Function Approximation , 1995, ICML.