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A Multi-Objective No-Regret Decision Making Model With Bayesian Learning For Autonomous Unmanned Systems, Matthew Howard
A Multi-Objective No-Regret Decision Making Model With Bayesian Learning For Autonomous Unmanned Systems, Matthew Howard
Electronic Theses and Dissertations
The development of a multi-objective decision making and learning model for the use in unmanned systems is the focus of this project. Starting with traditional game theory and psychological learning theories developed in the past, a new model for machine learning is developed. This model incorporates a no-regret decision making model with a Bayesian learning process which has the ability to adapt to errors found in preconceived costs associated with each objective. This learning ability is what sets this model apart from many others. By creating a model based on previously developed human learning models, hundreds of years of experience …
Autonomous Environmental Mapping In Multi-Agent Uav Systems, Linus Jan Luotsinen
Autonomous Environmental Mapping In Multi-Agent Uav Systems, Linus Jan Luotsinen
Electronic Theses and Dissertations
UAV units are by many researchers and aviation specialists considered the future and cutting edge of modern flight technology. This thesis discusses methods for efficient autonomous environmental mapping in a multi-agent domain. An algorithm that emphasizes on team work by sharing the agents local map information and exploration intentions is presented as a solution to the mapping problem. General theories on how to model and implement rational autonomous behaviour for UAV agents are presented. Three different human and tactical behaviour modeling techniques are evaluated. The author found the CxBR paradigm to be the most interesting approach. Also, in order to …