Publications

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Natural world interaction (NWI), the pursuit of arbitrary goals in unstructured physical environments, is an excellent motivating problem for the reintegration of artificial intelligence. It is the problem set that humans struggle to solve. At a minimum it entails perception, learning, planning, and control, and can also involve language and social behavior. An agent's fitness in NWI is achieved by being able to perform a wide variety of tasks, rather than being able to excel at one. In an attempt to address NWI, a brain-emulating cognition and control architecture (BECCA) was developed. It uses a combination of feature creation and model-based reinforcement learning to capture structure in tne environment in order to maximize reward. BECCA avoids making common assumptions about its world, such as stationarity, determinism, and the Markov assumption. BECCA has been demonstrated performing a set of tasks which is non-trivially broad, including a vision-based robotics task. Current development activity is focused on applying BECCA to the problem of general Search and Retrieve, a representative natural world interaction task. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

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Events of interest to data analysts are sometimes difficult to characterize in detail. Rather, they consist of anomalies, events that are unpredicted, unusual, or otherwise incongruent. The purpose of this LDRD was to test the hypothesis that a biologically-inspired anomaly detection algorithm could be used to detect contextual, multi-modal anomalies. There currently is no other solution to this problem, but the existence of a solution would have a great national security impact. The technical focus of this research was the application of a brain-emulating cognition and control architecture (BECCA) to the problem of anomaly detection. One aspect of BECCA in particular was discovered to be critical to improved anomaly detection capabilities: it's feature creator. During the course of this project the feature creator was developed and tested against multiple data types. Development direction was drawn from psychological and neurophysiological measurements. Major technical achievements include the creation of hierarchical feature sets created from both audio and imagery data.

The purpose of this LDRD is to develop technology allowing warfighters to provide high-level commands to their unmanned assets, freeing them to command a group of them or commit the bulk of their attention elsewhere. To this end, a brain-emulating cognition and control architecture (BECCA) was developed, incorporating novel and uniquely capable feature creation and reinforcement learning algorithms. BECCA was demonstrated on both a mobile manipulator platform and on a seven degree of freedom serial link robot arm. Existing military ground robots are almost universally teleoperated and occupy the complete attention of an operator. They may remove a soldier from harm's way, but they do not necessarily reduce manpower requirements. Current research efforts to solve the problem of autonomous operation in an unstructured, dynamic environment fall short of the desired performance. In order to increase the effectiveness of unmanned vehicle (UV) operators, we proposed to develop robots that can be 'directed' rather than remote-controlled. They are instructed and trained by human operators, rather than driven. The technical approach is modeled closely on psychological and neuroscientific models of human learning. Two Sandia-developed models are utilized in this effort: the Sandia Cognitive Framework (SCF), a cognitive psychology-based model of human processes, and BECCA, a psychophysical-based model of learning, motor control, and conceptualization. Together, these models span the functional space from perceptuo-motor abilities, to high-level motivational and attentional processes.

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Automatic feature creation is a powerful tool for identifying and reaching goals in the natural world. This paper describes in detail a biologically-inspired method of feature creation that can be applied to sensory information of any modality. The algorithm is incremental and on-line; it enforces sparseness in the features it creates; and it can form features from other features, making a hierarchical feature set. Here it demonstrates the creation of both visual and auditory features. © 2011 The authors and IOS Press. All rights reserved.

The neocortex is perhaps the highest region of the human brain, where audio and visual perception takes place along with many important cognitive functions. An important research goal is to describe the mechanisms implemented by the neocortex. There is an apparent regularity in the structure of the neocortex [Brodmann 1909, Mountcastle 1957] which may help simplify this task. The work reported here addresses the problem of how to describe the putative repeated units ('cortical circuits') in a manner that is easily understood and manipulated, with the long-term goal of developing a mathematical and algorithmic description of their function. The approach is to reduce each algorithm to an enhanced perceptron-like structure and describe its computation using difference equations. We organize this algorithmic processing into larger structures based on physiological observations, and implement key modeling concepts in software which runs on parallel computing hardware.

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Low Temperature Cofired Ceramic (LTCC) has proven to be an enabling medium for microsystem technologies, because of its desirable electrical, physical, and chemical properties coupled with its capability for rapid prototyping and scalable manufacturing of components. LTCC is viewed as an extension of hybrid microcircuits, and in that function it enables development, testing, and deployment of silicon microsystems. However, its versatility has allowed it to succeed as a microsystem medium in its own right, with applications in non-microelectronic meso-scale devices and in a range of sensor devices. Applications include silicon microfluidic ''chip-and-wire'' systems and fluid grid array (FGA)/microfluidic multichip modules using embedded channels in LTCC, and cofired electro-mechanical systems with moving parts. Both the microfluidic and mechanical system applications are enabled by sacrificial volume materials (SVM), which serve to create and maintain cavities and separation gaps during the lamination and cofiring process. SVMs consisting of thermally fugitive or partially inert materials are easily incorporated. Recognizing the premium on devices that are cofired rather than assembled, we report on functional-as-released and functional-as-fired moving parts. Additional applications for cofired transparent windows, some as small as an optical fiber, are also described. The applications described help pave the way for widespread application of LTCC to biomedical, control, analysis, characterization, and radio frequency (RF) functions for macro-meso-microsystems.

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The development of tools for complex dynamic security systems is not a straight forward engineering task but, rather, a scientific task where discovery of new scientific principles and math is necessary. For years, scientists have observed complex behavior but have had difficulty understanding it. Prominent examples include: insect colony organization, the stock market, molecular interactions, fractals, and emergent behavior. Engineering such systems will be an even greater challenge. This report explores four tools for engineered complex dynamic security systems: Partially Observable Markov Decision Process, Percolation Theory, Graph Theory, and Exergy/Entropy Theory. Additionally, enabling hardware technology for next generation security systems are described: a 100 node wireless sensor network, unmanned ground vehicle and unmanned aerial vehicle.

We developed an Augmented Musculature Device (AMD) that assists the movements of its wearer. It has direct application to aiding military and law enforcement personnel, the neurologically impaired, or those requiring any type of cybernetic assistance. The AMD consists of a collection of artificial muscles, each individually actuated, strategically placed along the surface of the human body. The actuators employed by the AMD are known as 'air muscles' and operate pneumatically. They are commercially available from several vendors and are relatively inexpensive. They have a remarkably high force-to-weight ratio--as high as 400:1 (as compared with 16:1 typical of DC motors). They are flexible and elastic, even when powered, making them ideal for interaction with humans.

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Evidence for the existence of discrete sub-movements underlying continuous human movement has motivated many attempts to "extract" them. Although they produce visually convincing results, all of the methodologies that have been employed are prone to produce spurious decompositions. Examples of potential failures are given. A branch-and-bound algorithm for submovement extraction, capable of global nonlinear minimization (and hence capable of avoiding spurious decompositions), is developed and demonstrated.

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Submovements are hypothesized building blocks of human movement, discrete ballistic movements of which more complex movements are composed. Using a novel algorithm, submovements were extracted from the point-to-point movements of 41 persons recovering from stroke. Analysis of the extracted submovements showed that, over the course of therapy, patients' submovements tended to increase in peak speed and duration. The number of submovements employed to produce a given movement decreased. The time between the peaks of adjacent submovements decreased for inpatients (those less than 1 month post-stroke), but not for outpatients (those greater than 12 months post-stroke) as a group. Submovements became more overlapped for all patients, but more markedly for inpatients. The strength and consistency with which it quantified patients' recovery indicates that analysis of submovement overlap might be a useful tool for measuring learning or other changes in motor behavior in future human movement studies.