CURATING UNCERTAINTY AND RELIABLE EXPLOITATION
Abstract
Machine Reasoning and Intelligence is usually done in a vacuum or without consultation of the ultimate decision-maker. The late consideration of the human cognitive process creates some major problems in the use of automated systems to provide reliable and actionable information that users can trust and depend on to make the best Course-of-Action (COA). On the other hand, if automated systems are created exclusively based on human cognition, then there is a danger of developing systems that don’t push the barrier of technology and are mainly done for the comfort level of the selected Subject-Matter-Expert (SME). Our proposal of Combining Human And Machine Processes (CHAMP) is based on the notion of formulating a Mathematical Model to provide an optimal strategy on where, when, what and how Human Intelligence should be injected within a Machine Reasoning and Intelligence process. This insertion of human interaction in the process should be based on the criteria of improving the quality of the output of the automated process while maintaining the required computational efficiency for a COA to be actuated in timely fashion. CUBRC’s proposed work will address the following research areas that will go beyond the benefits to DoD/IC and include potential gains to healthcare informatics, law enforcement and multiple other domains: • Consistency with a mission: Injection of Human Reasoning and Intelligence within the reliability and temporal needs of a mission to attain Situational Awareness, Impact Assessment and a Course-of-Action. • Support the use of data that is uncertain, incomplete, imprecise and contradictory (UIIC): Develop a mathematical model to suggest the insertion of a cognitive process within a machine reasoning and intelligent system so as to minimize UIIC concerns. • Develop systems that include humans in the loop whose performance can be analyzed and understood to provide feedback to the sensors (hard or soft): Human intelligence is going to be a critical part of CHAMP within the process refinement aspects so as to re-task/feedback sensors and develop the most meaningful collection requirements to maximize information gain.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jun 02, 2016
- Source ID
- N002441610022
Entities
People
- Moises Sudit
Organizations
- Research Foundation for the State University of New York
- United States Department of Defense