A Novel Machine Learning Classifier Based on a Qualia Modeling Agent (QMA)
Abstract
This research develops a computational agent, which overcomes this problem. The Qualia Modeling Agent (QMA) is modeled after two cognitive theories: Stanovich's tripartite framework, which proposes learning results from interactions between conscious and unconscious processes; and, the Integrated Information Theory (IIT) of Consciousness, which proposes that the fundamental structural elements of consciousness are qualia. By modeling the informational relationships of qualia, the QMA allows for retaining and reasoning-over data sets in a non-ontological, non-hierarchical qualia space (QS). This novel computational approach supports concept drift, by allowing the target variable to change ad infinitum without re-training while achieving classification accuracy comparable to or greater than benchmark classifiers. Additionally, the research produced a functioning model of Stanovich's framework, and a computationally tractable working solution for a representation of qualia, which when exposed to new examples, is able to match the causal structure and generate new inferences.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 15, 2016
- Accession Number
- AD1054269
Entities
People
- Sandra L. Vaughan
Organizations
- Air Force Institute of Technology