Abstract
We describe a computational cognitive model intended to be a generalizable classifier that can provide context-based feedback to semantic perception in robotic applications. Many classifiers (including cognitive models of categorization) perform well at the task of associating features with objects. Underlying their performance is an effective selection of the features used during classification. This feature selection (FS) process is usually performed outside the boundaries of the models that learn and perform classification tasks, often by human experts. In contrast, the cognitive model we describe simultaneously learns which features to use, as it learns the associations between features and classes. This integration of FS and class learning in one model makes it complementary to other machine-learning techniques that generate feature-based representations (e.g., deep learning methods). But their integration in a cognitive architecture also provides a means for creating a dynamic context that includes disparate sources of information (e.g., environmental observations, task knowledge, commands from humans). This richer context, in turn, provides a means for making semantic perception goal-directed. We demonstrate automated FS, integrated with an instance-based learning approach to classification, in an ACT-R model of categorization by labeling facial expressions of emotion (e.g., happy, sad), and then generalizing the model to the classification of indoor public spaces (e.g., cafes, classrooms).
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Notes
After disappointing results from initial runs of the Canonical model, we began initializing DM with prototypes for each emotion. The 7 class prototypes are represented as 7 configuration chunks with slots for each of the 39 features filled with the modal value of each AU for each emotion, plus the emotion class slot and its value. Thus the Canonical model performs poorly even though DM was seeded with standard prototypes. All refinements also were based on models that included seeding DM with standard prototypes.
The generation of SHOG links and node attributes are unresolved issues in our SHOG development efforts. The use of hard thresholds to generate links, raw positioning data as attributes and a superordinate room node is only a proof of concept. This makes interpretations of network metrics tentative at best.
Filter approaches to FS pre-process data independent of classification learning algorithms to reduce dimensionality. Wrapper approaches include the learning algorithms as part of the evaluation in the search for feature subsets. A variety of intuitive notions of relevance is used to evaluate feature-set goodness in many FS algorithms.
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Martin, M., Lebiere, C., Fields, M. et al. Learning features while learning to classify: a cognitive model for autonomous systems. Comput Math Organ Theory 26, 23–54 (2020). https://doi.org/10.1007/s10588-018-9279-3
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DOI: https://doi.org/10.1007/s10588-018-9279-3