Planning under Uncertainty by Spreading Activation through an Adaptive Probabilistic Network

As robotics and automation are applied to unstructured environments such as those in the service sector, researchers have to increasingly deal with the uncertainty about the e ects of performing actions in these environments. Two broad approaches have been devised for acting under uncertainty. The rst approach enhances traditional AI planning systems by incorporating probabilities about the e ects of actions that are conditioned on the state of the environment. This makes it possible to select plans that have the highest probability of success. The other approach, adopted by reactive systems, employs a set of prede ned situation-response rules that make it possible to move towards the goal from any situation, whether expected or unexpected. This dissertation describes a planner that combines the two approaches by having a proactive component that generates plans with the most reliable actions in a given situation, as well as a reactive component that alters the action selection based on the unexpected situations that tend to arise in an uncertain environment. Action selection occurs through a spreading activation mechanism through a probabilistic network that encodes the domain knowledge. Another aspect of this research is to learn the probabilistic domain knowledge from the observation of an expert and experience. This is relevant for environments with uncertainty, where the reliability of actions taken by the agent cannot be known a priori and may change with time. This research has also developed a learning by experimentation method that attempts to remove \misconceptions" learned as a result of observing action execution examples that are part of plan sequences and therefore, not in random order. Acknowledgments This dissertation and the research behind it could not have been accomplished without the support of many people and the access to various resources. My rst thanks go to my advisor, Professor Kazuhiko Kawamura, whose advice, trust, and support I shall cherish forever. He gave me the freedom to choose my research topic and then stood by my decision through the ups and downs of my research. I am also deeply indebted to Professor Gautam Biswas for his contributions to the work described in this dissertation. The long hours of discussions at his o ce always lled me with a sense of direction and optimism. The Intelligent Robotics Laboratory has been a great place to work in. The diverse viewpoints from di erent cultures, countries, age-groups, backgrounds, as well as technical interests contributed to an enriching experience. I thank all the past and present students at the IRL for their cooperation. I had fun working with them and hope they felt the same way (at least sometimes). Special thanks go to Ashraf Saad for his comments, criticisms, and appreciation of my research. He proof-read my writing and made sure it was understandable and grammatically correct. During the course of my stay at Vanderbilt, I was supported by research grants from Nippon Steel Corporation and Bridgestone Corporation. I am grateful to them for their sponsorship. I am also grateful to the Electrical and Computer Engineering department and Vanderbilt University as a whole for providing the necessary facilities and atmosphere for academic research. Finally, behind all this, has been the continued encouragement of my parents. I can never thank them enough. ii