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The intent of Sandia National Laboratories' Human Interactions (HI) project is to demonstrate initial virtual human interaction modeling capability. To accomplish this, we have begun the process of simulating human behavior in a manner that produces life-like characteristics and movement, as well as creating the framework for models that are based on the most current experimental research in cognition, perception, physiology, and cognitive modeling. Currently the simulated human models can sense each other, react to each other, and move about in a simulated 3D environment. A preliminary action generation or motor-level cognition model, which transforms abstract actions generated by high-level cognition to actions that can be carried out by a simulated physical human model, has also been developed. Our work has yielded models of perceptual, spatial, and motor functioning and memory that will be embedded in upgrades to the cognitive framework.

Research by Sandia National Laboratories (SNL) is currently being conducted that seeks to embody human-like cognitive capacities in machines by transforming the human-machine interaction so that it more closely resembles a human-to-human interaction. This document reports on the initial phase of research and development by SNL in creating a capability whereby a machine-based cognitive model provides a real-time awareness of the cognitive state of an operator. In the capability referred to as “Discrepancy Detection,” the machine uses an operator's cognitive model to monitor its own state and when there is evidence of a discrepancy between the actual state of the machine and the operator's perceptions concerning the state of the machine, a discrepancy may be signaled. The current project offers successful evidence that a machine may accurately infer an operator's interpretation of situations based on an individualized cognitive model of the operator.

This Lab-Directed Research and Development (LDRD) sought to develop technology that enhances scenario construction speed, entity behavior robustness, and scalability in Live-Virtual-Constructive (LVC) simulation. We investigated issues in both simulation architecture and behavior modeling. We developed path-planning technology that improves the ability to express intent in the planning task while still permitting an efficient search algorithm. An LVC simulation demonstrated how this enables 'one-click' layout of squad tactical paths, as well as dynamic re-planning for simulated squads and for real and simulated mobile robots. We identified human response latencies that can be exploited in parallel/distributed architectures. We did an experimental study to determine where parallelization would be productive in Umbra-based force-on-force (FOF) simulations. We developed and implemented a data-driven simulation composition approach that solves entity class hierarchy issues and supports assurance of simulation fairness. Finally, we proposed a flexible framework to enable integration of multiple behavior modeling components that model working memory phenomena with different degrees of sophistication.

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.

This 3-year research and development effort focused on what we believe is a significant technical gap in existing modeling and simulation capabilities: the representation of plausible human cognition and behaviors within a dynamic, simulated environment. Specifically, the intent of the ''Simulating Human Behavior for National Security Human Interactions'' project was to demonstrate initial simulated human modeling capability that realistically represents intra- and inter-group interaction behaviors between simulated humans and human-controlled avatars as they respond to their environment. Significant process was made towards simulating human behaviors through the development of a framework that produces realistic characteristics and movement. The simulated humans were created from models designed to be psychologically plausible by being based on robust psychological research and theory. Progress was also made towards enhancing Sandia National Laboratories existing cognitive models to support culturally plausible behaviors that are important in representing group interactions. These models were implemented in the modular, interoperable, and commercially supported Umbra{reg_sign} simulation framework.

This report documents work undertaken to endow the cognitive framework currently under development at Sandia National Laboratories with a human-like memory for specific life episodes. Capabilities have been demonstrated within the context of three separate problem areas. The first year of the project developed a capability whereby simulated robots were able to utilize a record of shared experience to perform surveillance of a building to detect a source of smoke. The second year focused on simulations of social interactions providing a queriable record of interactions such that a time series of events could be constructed and reconstructed. The third year addressed tools to promote desktop productivity, creating a capability to query episodic logs in real time allowing the model of a user to build on itself based on observations of the user's behavior.

This paper presents simulation and analysis of a collective of autonomous unmanned ground-based vehicles navigating a building searching for increasing smoke concentrations. The vehicles communicate smoke concentrations to each other to determine the location of the highest concentration value. The data generated front the robots' sensors is used to activate a semantic network to generate data for further cognitive operations. Statistical analysis is employed on the data to identify schema and themes, which enable the robots to convey a story of their experiences, thus emulating human episodic memory.

The authors consider the following problem: given a robot system, find a minimal-time trajectory from a start state to a goal state, while avoiding obstacles by a safety margin and respecting bounds on velocity and generalized forces. A provably good polynomial-time approximation algorithm for this problem is one for which it is possible to (1) bound the goodness of the approximate solution it produces by an error term epsilon ; (2) polynomially bound the running time (complexity) of the algorithm; and (3) express the complexity as a polynomial function of 1/ epsilon . Using a new trajectory tracking lemma for robots with coupled dynamics bounds and a generalization of the basic algorithm of J. Canny et al. (1988), the authors describe provably good polynomial-time approximation algorithms for nonrotating robots obeying L/sub 2/ dynamics bounds and for open-chain manipulators. These algorithms only consider near-extremal accelerations, thus the out-degree complexity of the search is lower than that of the earlier provably good polynomial-time approximation algorithm of P. Jacobs et al. (1989) for open-chain manipulators. >

We discuss the integration of the SANDROS path planner into a general robot simulation and control package with the inclusion of a fast geometry engine for distance calculations. This creates a single system that allows the path to be computed, simulated, and then executed on the physical robot. The architecture and application procedures are presented. Also, we present examples of its usage in typical environments found in our organization. The resulting system is as easy to use as the general simulation system and is fast enough to be used interactively on an everyday basis.

The Geometric Search Engine is a software system for storing and searching a database of geometric models. The database maybe searched for modeled objects similar in shape to a target model supplied by the user. The database models are generally from CAD models while the target model may be either a CAD model or a model generated from range data collected from a physical object. This document describes key generation, database layout, and search of the database.

Umbra is a new Sandia-developed modeling and simulation framework. The Umbra framework allows users to quickly build models and simulations for intelligent system development, analysis, experimentation, and control and supports tradeoff analyses of complex robotic systems, device, and component concepts. Umbra links together heterogeneous collections of modeling tools. The models in Umbra include 3D geometry and physics models of robots, devices and their environments. Model components can be built with varying levels of fidelity and readily switched to allow models built with low fidelity for conceptual analysis to be gradually converted to high fidelity models for later phase detailed analysis. Within control environments, the models can be readily replaced with actual control elements. This paper describes Umbra at a functional level and describes issues that Sandia uses Umbra to address.