Learning Human Intention for Taskable Agents
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Issue Date
2019-09-20
Language
en
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Abstract
As AI systems are continuously developed and improved, they can be used for an increasing
variety of tasks. At the same time, dependency on these systems grows, and it becomes more
important for AI systems to perform their tasks as we intend them to do. In this study,
the focus lies with agents that learn, given a task, how to perform this task as the human
intended. The use of context-dependent task constraints is studied as an approximation to
the human’s intention for how the task should be executed. A drone reconnaissance task
was built using a new multi-agent simulator, called the Man-Agent Teaming Rapid Experimentation
Simulator (MATRXS). In the pilot, a small number of participants taught an
agent how they want a task to be completed in various contexts by specifying constraints.
Machine learning models were able to effectively and efficiently learn the context-dependent
constraints (XGBoost with average F1 score of 0.95, 128 data points) for each participant
individually. Models trained without context input features scored significantly lower (average
F1 score of 0.60), showing the context-dependency of human intention for agent tasking.
Although the conclusiveness of the results is lower due to the small magnitude of the experiment,
the results show this to be a promising approach for establishing meaningful human
control over agents. Finally, lessons learned from this explorative study were summarized
into a set of recommendations which indicate promising future research and how to scale up
to an experiment of larger magnitude and complexity.
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Faculteit der Sociale Wetenschappen