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Evaluation of Deep Learning Methods for Creating Synthetic Actors
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2017 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Recent advancements in hardware, techniques and data availability have resulted in major advancements within the field of Machine Learning and specifically in a subset of modeling techniques referred to as Deep Learning. Virtual simulations are common tools of support in training and decision making within the military. These simulations can be populated with synthetic actors, often controlled through manually implemented behaviors, developed in a streamlined process by domain doctrines and programmers. This process is often time inefficient, expensive and error prone, potentially resulting in actors unrealistically superior or inferior to human players.

This thesis evaluates alternative methods of developing the behavior of synthetic actors through state-of-the-art Deep Learning methods. Through a few selected Deep Reinforcement Learning algorithms, the actors are trained in four different light weight simulations with objectives like those that could be encountered in a military simulation.

The results show that the actors trained with Deep Learning techniques can learn how to perform simple as well as more complex tasks by learning a behavior that could be difficult to manually program. The results also show the same algorithm can be used to train several totally different types of behavior, thus demonstrating the robustness of these methods.

This thesis finally concludes that Deep Learning techniques have, given the right tools, a good potential as alternative methods of training the behavior of synthetic actors, and to potentially replace the current methods in the future. 

Place, publisher, year, edition, pages
2017. , p. 89
Series
UPTEC IT, ISSN 1401-5749
Keywords [en]
artificial intelligence, machine learning, deep learning, reinforcement learning, military, simulation
Keywords [sv]
artificiell intelligens, maskininlärning, djupinlärning, militär, simulering
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-324756OAI: oai:DiVA.org:uu-324756DiVA, id: diva2:1111278
External cooperation
Totalförsvarets Forskningsinstitut, FOI
Educational program
Master of Science Programme in Information Technology Engineering
Supervisors
Examiners
Available from: 2017-06-19 Created: 2017-06-18 Last updated: 2017-06-19Bibliographically approved

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