Ontology and Cognitive Outcomes
This work addresses the need for reliable human-machine analytic strategies in the U.S. intelligence community, but it appears incremental as it builds on existing ontology and learning methods without claiming major breakthroughs.
The paper tackles the problem of enhancing intelligence analysis for national security by proposing an outcomes-based learning approach grounded in a cognitive process ontology, focusing on 'Representation that is Warranted' to ensure trustworthy knowledge.
Here we understand 'intelligence' as referring to items of knowledge collected for the sake of assessing and maintaining national security. The intelligence community (IC) of the United States (US) is a community of organizations that collaborate in collecting and processing intelligence for the US. The IC relies on human-machine-based analytic strategies that 1) access and integrate vast amounts of information from disparate sources, 2) continuously process this information, so that, 3) a maximally comprehensive understanding of world actors and their behaviors can be developed and updated. Herein we describe an approach to utilizing outcomes-based learning (OBL) to support these efforts that is based on an ontology of the cognitive processes performed by intelligence analysts. Of particular importance to the Cognitive Process Ontology is the class Representation that is Warranted. Such a representation is descriptive in nature and deserving of trust in its veridicality. The latter is because a Representation that is Warranted is always produced by a process that was vetted (or successfully designed) to reliably produce veridical representations. As such, Representations that are Warranted are what in other contexts we might refer to as 'items of knowledge'.