Date of Thesis
Spring 2019
Description
Cognitive architectures such as ACT-R provide a system for simulating the mind and human behavior. On their own they model decision making of an isolated agent. However, applying a cognitive architecture to a complex environment yields more interesting results about how people make decisions in more realistic scenarios. Furthermore, cognitive architectures enable researchers to study human behavior in dangerous tasks which cannot be tested because they would harm participants. Nonetheless, these architectures aren’t commonly applied to such environments as they don’t come with one. It is left to the researcher to develop a task environment for their model. The difficulty in creating one prevents cognitive architectures from being utilized in more advanced studies. This project aims to address that issue by building a bridge between ACT-R and Project Malmo, an artificial general intelligence test suite. The bridge facilitates easy integration of new missions by allowing researchers to specify how to create the world and update it without worrying about the overhead of Malmo. Furthermore, this study analyses how well ACT-R’s utility learning system will adapt in a complex environment. The Adaptive Gain Theory was implemented to improve how the system adapts by using task engagement, derived from measures of utility, to dynamically modify noise. The system was tested using a modified Symbolic Maze task. Tests revealed the parameters of the Adaptive Gain mechanism need to be refined to have a greater impact on model performance. Nonetheless, the bridge provides an interface for ACT-R to be used to study decision making in a complex environment. Improving the bridge will enable more advanced experiments to be conducted whilst improving the Adaptive Gain Theory implementation will move us one step closer to understanding everyday intelligent behavior.
Keywords
ACT-R, cognitive architecture, Project Malmo, exploration exploitation trade-off, Adaptive Gain Theory, reinforcement learning
Access Type
Honors Thesis
Degree Type
Bachelor of Science in Computer Science and Engineering
Major
Computer Science & Engineering
Minor, Emphasis, or Concentration
Mathematics
First Advisor
Christopher L. Dancy
Second Advisor
L. Felipe Perrone
Recommended Citation
Schwartz, David M., "Bridging ACT-R and Project Malmo, developing models of behavior in complex environments" (2019). Honors Theses. 505.
https://digitalcommons.bucknell.edu/honors_theses/505
Included in
Artificial Intelligence and Robotics Commons, Cognitive Psychology Commons, Software Engineering Commons