To see all Research Highlights of the Navy Center for Applied Research in Artificial Intelligence, select "Research Highlights" in the tab menu above.

Participant uses NRL's experimental multimodal, multitask testbed. 3D Audio-Cued Operator Performance Modeling

This project, known as the "Auditory Principles" project, is basic research seeking to identify and model attentional principles for integrated uses of virtual audio information in Navy watchstanding operations. Navy watchstanding operations increasingly involve environments in which operators must attend to more than one critical task display at a time. In response, the Navy is pursuing a model-based understanding of human performance in multitask settings.

A Tactical Action Officer (TAO) interacts with computer displays and audio inputs. Chat Attention Management for Enhanced Situational Awareness

This applied research project focuses on novel techniques and learning algorithms for (1) classifying chat messages, 2) summarizing chat room situations, and (3) 3D audio and visual cueing techniques for intelligently cueing watchstanders in a Navy Combat Information Center (CIC).

Decision tree graphically depicting decision processes in a law enforcement scenario. Cognitively Inspired Decision Making for Visualization

The major objective of this project is to investigate the potential advantages of using a cognitively-based approach to autonomous decision making at multiple levels in a command structure. The intent of this approach is to facilitate the presentation of autonomous reasoning to human decision makers in ways that allow them to rely on and/or revise decisions that have been made at lower levels. This research will promote rapid situational understanding of the battle space and facilitate the decision maker’s ability to intervene and override the system as needed.

Octavia the robot assisting in extinguishing a fire. Damage Control for the 21st Century

The DC-21 project is developing advanced technologies for shipboard damage control. The project includes the development of advanced autonomous systems to assist in damage control and discovery and control of incipient fires. Our focus is specifically on the human-robot interaction technology that will allow a Navy firefighter to interact peer-to-peer, shoulder-to-shoulder with a humanoid robotic firefighter.

Sonobuoys are used to make an impulsive broadband signal (essentially an explosion), and its reflections (echoes) are evaluated to determine if potentially dangerous underwater objects of interest (subs, mines, etc.) are in the vicinity. Human Mimetic Active Sonar Classification

Exploratory development in this research involves the development of algorithms, based on human perception of sound, for improved and automated active sonar classification. Single-ping algorithms transform sonar sounds into aural features for a new classifier that classify sounds.The project uses archived data sets and human subject research results to develop human-mimetic transforms to generate set-theoretic features to describe sounds. The project seeks to develop classifiers based on these types of features.

Phonological Analysis of Speech on Hand-held Smartphones produces langugage/dialect identification and associated confidence levels. Machine Classification of Spoken Language

Machine Classification of Spoken Language is basic research involving the classification and identification of the speech patterns of native and non-native speakers of English using phonetic and phonological analyses. The initial phase of this research involved the hand-coding of phonetic and phonological rules to determine speakers' native accents from passages spoken in English. The research progresses into the Automatic Dialect Identification project that provides machine-driven phonological analyses with confidence levels to identify the native language/dialect of both native and non-native speakers of English. Machine learning techniques will be used to train the phonological analysis engine in order to automate the procedure.

Change points or naturally occurring breaks in accelerated speech affect comprehension. Naturally Occurring Change Points in Navy Radio Communications

The project addresses the problem of monitoring multiple radio communication channels. By acceleration and serialization of radio traffic from concurrently active channels. Using these techniques, Navy watch standers have an increased comprehension of the communication channels, and miss less communications. Building on related work being conducted at NRL, a series of experiments are designed to determine to what extent the skill of attending to and understanding accelerated speech can be trained, and whether or not individual differences in memory and executive factors contribute to task performance in complex auditory domains.

Unifying Inference through Attention Unifying Inference through Attention

This research program has as its goal the development of a cognitive system that acquires strategies for controlling inference. Much like humans can learn to solve mathematical equations, prove logical theorems, analyze filmic metaphor, and construct legal arguments, a broadly intelligent system must be able to develop new forms of reasoning about the world. We claim that the manipulation of attention, directing it toward specific thoughts and perceptions, provides a way to carry out different kinds of reasoning. Moreover, learning attention strategies involves attending to trains of thought that make sense of the world.