Dissertation/Thesis Abstract

Learning-Assisted Market-Based Optimization for Truck Task Scheduling
by Danna, Russell J., M.S., University of Louisiana at Lafayette, 2014, 56; 1557547
Abstract (Summary)

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.

Indexing (document details)
Advisor: Maida, Anthony
Commitee: Golconda, Suresh, Loganantharaj, Rasiah
School: University of Louisiana at Lafayette
Department: Computer Science
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
Publication Number: 1557547
ISBN: 9781303950872
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