Some futures for cognitive modeling and architectures: design patterns for including better interaction with the world,
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Some futures for cognitive modeling and architectures: design patterns for including better interaction with the world, moderators, and improved model to data fits (and so can you) Frank E. Ritter1 · Farnaz Tehranchi1,4 · Christopher L. Dancy2 · Sue E. Kase3
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract We note some future areas for work with cognitive models and agents that as Colbert (I am America (and so can you!), 2007) notes, “so can you”. We present three approaches as something like design patterns, so they can be applied to other architectures and tasks. These areas are: (a) Interacting directly with the screen-as-world. It is now possible for models to interact with uninstrumented interfaces both on the machine that the model is running on as well as remote machines. Improved interaction can not only support a broader range of behavior but also make the interaction more accurately model human behavior on tasks that include interaction. Just one implication is that this will force models to have more knowledge about interaction, an area that has been little modeled but essential for all tasks. (b) Providing the cognitive architecture with more representation of the body. In our example, we provide a physiological substrate to implement behavioral moderators’ effects on cognition. Cognitive architectures can now be broader in the measurements they predict and correspond to. This approach provides a more complete and theoretically appropriate way to include new aspects of behavior including stressor effects and emotions in models. And (c) using machine learning techniques, particularly genetic algorithms (GAs), to fit models to data. Because of the model complexity, this is equivalent to performing a multi-variable non-linear stochastic multiple-output regression. Doing this by hand is completely inadequate. While there is a danger of overfitting using a GA, these fits can help provide a better understanding of the model and architecture, including how the architecture changes under moderators such stress. This paper also includes some notes on model maintenance and reporting.
Colbert (2007) provided inspiration for the title. Extended author information available on the last page of the article
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Keywords Cognitive architecture · Vision models · Interaction · User models · Emotions · Moderators · Model fit · Model optimization · ACT-R · Genetic algorithm · Pattern matching
1 Introduction In this paper, we note some future areas for work with cognitive models created within cognitive architectures that may also be useful for AI agents. These models and agents are important for cognitive science and also important for agent-based modeling and computational organizational theories because they provide broader cognitive social agents. This report is based on a plenary talk at the 2018 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SBP-
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