Deep learning for decision making in cyber-physical systems

dc.contributor.advisor Sarkar, Soumik
dc.contributor.advisor Krishnamurthy, Adarsh
dc.contributor.advisor Ganapathysubramanian, Baskar
dc.contributor.advisor Bhattacharya, Sourabh
dc.contributor.advisor Sharma, Anuj
dc.contributor.author Tan, Kai Liang
dc.contributor.department Mechanical Engineering en_US
dc.date.accessioned 2023-01-10T20:05:38Z
dc.date.available 2023-01-10T20:05:38Z
dc.date.embargo 2025-01-10T00:00:00Z
dc.date.issued 2022-12
dc.date.updated 2023-01-10T20:05:38Z
dc.description.abstract Ever since the inception of the first computer, computer systems have helped enable the essential building blocks of the current modern society. Simple systems like a calculator to sophisticated systems like banking, transportation, and orbital launch systems. However, complex systems still have limitations to perform intelligent actions, such as exhaustively enumerating rule-based algorithms to cover every corner case. Recent progress of machine learning and deep learning has been accelerated with breakthroughs in solving traditionally time-consuming solutions with efficient and parallel computing architectures. This has enabled the possibility towards the fourth industrial revolution by transforming traditional manufacturing pipelines into cyber-physical systems. While deep learning has found success in the computer vision research community and commercial adoption, the potential impact for cyber-physical systems is likely to be monumental. This thesis highlights several possible pathways for decision-making cyber-physical systems, along with potential challenges that impede widespread adoption. These can be summarized as the following ideas: i) reliability, ii) generalization, and iii) reproducibility. In conventional engineering systems, these are important aspects required to be tested before allowing full-scale deployment to the public. While there are many years of development and experience in the engineering industry to develop reliable systems for widespread adoption, enabling cyber-physical systems for decision-making is still in its infancy stage, requiring addressing the challenges mentioned above to develop standards for the industry. To address these challenges, this thesis report investigates the reliability of a deep reinforcement learning system. Formally, the investigation focuses specifically on deep reinforcement learning case studies and their reliability. The first study dives into the robustness of deep reinforcement learning system used as a traffic controller for an intersection. The second study explores the reliability of a deep reinforcement learning-based controller by developing potential adversaries. Finally, the third study investigates robustifying deep reinforcement learning systems with the adversary model.
dc.format.mimetype PDF
dc.identifier.orcid 0000-0003-2559-1559
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/0zEyJO5z
dc.language.iso en
dc.language.rfc3066 en
dc.subject.disciplines Artificial intelligence en_US
dc.subject.keywords adversarial attack en_US
dc.subject.keywords agriculture en_US
dc.subject.keywords biotic stress en_US
dc.subject.keywords deep learning en_US
dc.subject.keywords reinforcement learning en_US
dc.subject.keywords traffic control en_US
dc.title Deep learning for decision making in cyber-physical systems
dc.type article en_US
dc.type.genre dissertation en_US
dspace.entity.type Publication
thesis.degree.discipline Artificial intelligence en_US
thesis.degree.grantor Iowa State University en_US
thesis.degree.level dissertation $
thesis.degree.name Doctor of Philosophy en_US
File
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
0 B
Format:
Item-specific license agreed upon to submission
Description: