Publications

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Consolidated Nuclear Security (CNS) Pantex took the initiative to organize a Review Panel of subject matter experts to independently assess the adequacy of the Pantex Tripping Man Analysis methodology. The purpose of this report is to capture the details of the assessment including the scope, approach, results, and detailed Appendices. Along with the assessment of the analysis methodology, the panel evaluated the adequacy with which the methodology was applied as well as congruence with Department of Energy (DOE) standards 3009 and 3016. The approach included the review of relevant documentation, interactive discussion with Pantex staff, and the iterative process of evaluating critical lines of inquiry.

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.

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For applications such as force protection, an effective decision maker needs to maintain an unambiguous grasp of the environment. Opportunities exist to leverage computational mechanisms for the adaptive fusion of diverse information sources. The current research employs neural networks and Markov chains to process information from sources including sensors, weather data, and law enforcement. Furthermore, the system operator's input is used as a point of reference for the machine learning algorithms. More detailed features of the approach are provided, along with an example force protection scenario.

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Despite continuing efforts to apply existing hazard analysis methods and comply with requirements, human errors persist across the nuclear weapons complex. Due to a number of factors, current retroactive and proactive methods to understand and minimize human error are highly subjective, inconsistent in numerous dimensions, and are cumbersome to characterize as thorough. An alternative and proposed method begins with leveraging historical data to understand what the systemic issues are and where resources need to be brought to bear proactively to minimize the risk of future occurrences. An illustrative analysis was performed using existing incident databases specific to Pantex weapons operations indicating systemic issues associated with operating procedures that undergo notably less development rigor relative to other task elements such as tooling and process flow. Future recommended steps to improve the objectivity, consistency, and thoroughness of hazard analysis and mitigation were delineated.

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This project is being conducted by Sandia National Laboratories in support of the DARPA Augmented Cognition program. Work commenced in April of 2002. The objective for the DARPA program is to 'extend, by an order of magnitude or more, the information management capacity of the human-computer warfighter.' Initially, emphasis has been placed on detection of an operator's cognitive state so that systems may adapt accordingly (e.g., adjust information throughput to the operator in response to workload). Work conducted by Sandia focuses on development of technologies to infer an operator's ongoing cognitive processes, with specific emphasis on detecting discrepancies between machine state and an operator's ongoing interpretation of events.

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There have been significant efforts to develop cognitively plausible software architectures of human information processing in the last three decades. This report summarizes several architectures that continue to be developed. The specific type of cognitive models developed are known as production system architectures, which refers to the characterization of knowledge in terms of procedural (''how-to'' knowledge) condition-action relationships consisting of declarative (''what'' or factual) knowledge. To illustrate the ability for these models to instantiate human cognitive performance, a simulation using ACT-R (Adaptive Control of Thought - Rational) was implemented for a supervisory control task. Correlations between simulated and human learning of the task were measured and yielded correlations as high as 0.93.