title: Coordinated machine learning and decision support for situation awareness.
abstract: Domains such as force protection require an effective decision maker to maintain a high level of situation awareness. A system that combines humans with neural networks is a desirable approach. Furthermore, it is advantageous for the calculation engine to operate in three learning modes: supervised for initial training and known updating, reinforcement for online operational improvement, and unsupervised in the absence of all external signaling. An Adaptive Resonance Theory based architecture capable of seamlessly switching among the three types of learning is discussed that can be used to help optimize the decision making of a human operator in such a scenario. This is followed by a situation assessment module.
authors.affiliation: Reliability Assessment and Human Systems Integration Department, Sandia National Laboratories, Albuquerque, NM 87185, USA.
authors.forename: N G
authors.initials: NG
authors.lastname: Brannon
authors.forename: J E
authors.initials: JE
authors.lastname: Seiffertt
authors.forename: T J
authors.initials: TJ
authors.lastname: Draelos
authors.forename: D C
authors.initials: DC
authors.lastname: Wunsch
mesh_terms: D000223:Adaptation, Psychological / Q000502:physiology; D001185:Artificial Intelligence; D001364:Awareness / Q000502:physiology*; D003658:Decision Making, Computer-Assisted; D003661:Decision Support Techniques; D007858:Learning / Q000502:physiology; D016571:Neural Networks, Computer; D012054:Reinforcement, Psychology; D014584:User-Computer Interface