[FRC Seminar] Learning-enhanced Market-based Task Allocation for Disaster Response

Speaker:
E. Gil Jones
Ph.D. Candidate
Robotics Institute

Abstract:
This talk will introduce a learning-enhanced market-based task allocation system for disaster response domains. I model the disaster response domain as a team of robots cooperating to extinguish a series of fires that arise due to a disaster. Each fire is associated with a time-decreasing reward for successful mitigation, with the value of the initial reward corresponding to task importance, and the speed of decay of the reward determining the urgency of the task. Deadlines are also associated with each fire, and penalties are assessed if fires are not extinguished by their deadlines. The team of robots aims to maximize summed reward over all emergency tasks, resulting in the lowest overall damage from the series of fires.

In this talk I will first describe my implementation of a baseline market-based approach to task allocation for disaster response. In the baseline approach the allocation respects fire importance and urgency, but agents do a poor job of anticipating future emergencies and are assessed a high number of penalties. I will then describe two regression-based approaches to learning-enhanced task allocation. The first approach, task-based learning, seeks to improve agents' valuations for individual tasks. The second method, schedule-based learning, tries to quantify the
tradeoff between performing a given task or not performing the task and retaining the flexibility to better perform future tasks. Finally, I will compare the performance of the two learning methods and the baseline approach over a variety of parameterizations of the disaster response domain.

Speaker Bio:
Gil is a second year Ph.D. student at the Robotics Institute, and is co-advised by Bernardine Dias and Tony Stentz. His primary interest is market-based multi-robot coordination. He received his BA in Computer Science from Swarthmore College in 2001, and spent two years as a software engineer at Bluefin Robotics - manufacturer of autonomous underwater vehicles - in Cambridge, Mass.