Abstract:
Traditional classification approaches are straightforward: collect data, apply classification algorithms, then generate classification results. However, such approaches depend on data being amply available, which is not always the case.
This paper describes an approach to maximize the utility of collected data through intelligent guidance of the data collection process. We present the development and evaluation of a knowledge-based decision-support system: the Logical Reasoner (LR), which guides data collection by unmanned ground and air assets to improve behavior classification. The LR is a component of a Human Directed and Controlled AI system (or “Human-AI” system) aimed at semi-autonomous classification of potential threat and non-threat individuals in a complex urban setting.
The setting provides little to no pre-existing data; thus, the system collects, analyzes, and evaluates real-time human behavior data to determine whether the observed behavior is indicative of threat intent. The LR’s purpose is to produce contextual knowledge to help make productive decisions about where, when, and how to guide the vehicles in the data collection process. It builds a situational-awareness picture from the observed spatial relationships, activities, and interim classifications, then uses heuristics to generate new information-gathering goals, as well as to recommend which actions the vehicles should take to better achieve these goals.
The system uses these recommendations to collaboratively help the operator direct the autonomous assets to individuals or places in the environment to maximize the effectiveness of evidence collection. LR is based on the Soar Cognitive Architecture which excels in supporting Human-AI collaboration. The described DoD-sponsored system has been developed and extensively tested for over three years, in simulation and in the field (with role-players). Results of these experiments have demonstrated that the LR decision support contributes to automated data collection and overall classification accuracy by the Human-AI team.
This paper describes the development and evaluation of the LR based on multiple test events. The research reported in this document was performed under Defense Advanced Research Projects Agency (DARPA) contract #HR001120C0180, Urban Reconnaissance through Supervised Autonomy (URSA). The views, opinions, and/or findings expressed are those of the author(s) and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon. Many thanks to Robert Marinier and Kris Kearns for their assistance in the preparation of this manuscript, as well as the entire ISOLATE R&D team. Distribution Statement “A” (Approved for Public Release, Distribution Unlimited)
Authors: Randolph Jones, Ph.D., Robert Bixler, Robert Marinier, III, Ph.D., and Lilia Moshkina, Ph.D.