Action selection for an autonomous agent was studied within the confines of truck task scheduling. An experimental setup was established to compare a naive selection approach, a simple market-based optimization approach, and a learning-assisted market-based optimization over a series of scenarios with varying complexity. For sufficiently complex scenarios, the results showed that learning was able to improve the performance of the truck by delaying delivery to a given site until it was the most protable action available. This research adds to the existing autonomous planning research by demonstrating a novel approach for planning under resource constraints. This approach improves upon an existing market-based optimization technique through the use of on-line reinforcement learning for market adjustment.
|Commitee:||Golconda, Suresh, Loganantharaj, Rasiah|
|School:||University of Louisiana at Lafayette|
|School Location:||United States -- Louisiana|
|Source:||MAI 53/01M(E), Masters Abstracts International|
|Subjects:||Artificial intelligence, Computer science|
|Keywords:||Machine learning, Market-based optimization, Task scheduling|
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