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Full-Text Articles in Physical Sciences and Mathematics
Reliable Decision-Making With Imprecise Models, Sandhya Saisubramanian
Reliable Decision-Making With Imprecise Models, Sandhya Saisubramanian
Doctoral Dissertations
The rapid growth in the deployment of autonomous systems across various sectors has generated considerable interest in how these systems can operate reliably in large, stochastic, and unstructured environments. Despite recent advances in artificial intelligence and machine learning, it is challenging to assure that autonomous systems will operate reliably in the open world. One of the causes of unreliable behavior is the impreciseness of the model used for decision-making. Due to the practical challenges in data collection and precise model specification, autonomous systems often operate based on models that do not represent all the details in the environment. Even if …
Integrating Recognition And Decision Making To Close The Interaction Loop For Autonomous Systems, Richard Freedman
Integrating Recognition And Decision Making To Close The Interaction Loop For Autonomous Systems, Richard Freedman
Doctoral Dissertations
Intelligent systems are becoming increasingly ubiquitous in daily life. Mobile devices are providing machine-generated support to users, robots are "coming out of their cages" in manufacturing to interact with co-workers, and cars with various degrees of self-driving capabilities operate amongst pedestrians and the driver. However, these interactive intelligent systems' effectiveness depends on their understanding and recognition of human activities and goals, as well as their responses to people in a timely manner. The average person does not follow instructions step-by-step or act in a formulaic manner, but instead varies the order of actions and timing when performing a given task. …
Abstractions In Reasoning For Long-Term Autonomy, Kyle Hollins Wray
Abstractions In Reasoning For Long-Term Autonomy, Kyle Hollins Wray
Doctoral Dissertations
The path to building adaptive, robust, intelligent agents has led researchers to develop a suite of powerful models and algorithms for agents with a single objective. However, in recent years, attempts to use this monolithic approach to solve an ever-expanding set of complex real-world problems, which increasingly include long-term autonomous deployments, have illuminated challenges in its ability to scale. Consequently, a fragmented collection of hierarchical and multi-objective models were developed. This trend continues into the algorithms as well, as each approximates an optimal solution in a different manner for scalability. These models and algorithms represent an attempt to solve pieces …