Frank Greitzer

Recent workshops and conferences supporting the intelligence community (IC) have highlighted the need to characterize the difficulty or complexity of intelligence analysis (IA) tasks in order to facilitate assessments of the impact or effectiveness of IA tools that are being considered for introduction into the IC. Some fundamental issues are: (a) how to employ rigorous methodologies in evaluating tools, given a host of problems such as controlling for task difficulty, effects of time or learning, small-sample size limitations; (b) how to measure the difficulty/complexity of IA tasks in order to establish valid experimental/quasi-experimental designs aimed to support evaluation of tools; and (c) development of more rigorous (summative), performance-based measures of human performance during the conduct of IA tasks, beyond the more traditional reliance on formative assessments (e.g., subjective ratings). Invited discussants will be asked to comment on one or more of these issues, wit...

A workshop was held to generate ideas about future enhancements to software systems designed to aid intelligence analysts in the analysis process. Workshop participants were working analysts, working members of the Pacific Northwest National Laboratory Field Intelligence Element. An analyst from the CounterIntelligence Program also participated in the workshop. This paper summarizes the results of the workshop and discusses implications for intelligence analysis software tool development.

A diverse set of challenges faces government and industry for life-cycle maintenance of aging equipment. Advancements in sensor and computer technologies are making it feasible to install sensors and small powerful computers on complex equipment to monitor the general condition or state (i.e., health) of that equipment. Methods for analyzing system status and health and for predicting system life expectancy need to be made more powerful, insightful, reliable, and robust for data collected onboard systems in real time. This paper reports on a project that is developing robust analytic methods for predicting remaining life of mechanical systems. The project is focused specifically on the investigation of a generalized statistical method for characterizing and predicting system degradation.

The Pacific Northwest National Laboratory (PNNL) has conducted R&D for the US Army on prognostics health monitoring (PHM). The main focus of the work was to demonstrate the feasibility of developing an onboard PHM system for the gas turbine engine used on the M1 Abrams tank. Research was performed on methods for real time, onboard prognostics/engine life expectancy forecasting, and a prototype system was designed, developed, and installed on several test tanks. The purpose of this presentation is to review the approaches to PHM employed in this research, to provide an overview of the PHM prototype and results obtained to date with data collected in the field, and to describe current work aimed at developing a PHM capability for diesel engines.

The focus of this paper is on user-centered systems engineering activities supporting the acquisition, requirements gathering, and preliminary design of a major technology advancement for an operating commercial command and control system. Major activities included establishment of user-centered customer and developer organizations, knowledge acquisition, human-machine interface design and prototyping, scenario and system simulation development, and demonstration of the proposed system. Because the system was designed with substantial user input, it received a high degree of user acceptance.

The focus of this paper is to discuss paradigms for learning that are based on sound principles of human learning and cognition, and to discuss technical challenges that must be overcome in achieving this research goal through instructional system design (ISD) approaches that are cost-effective as well as conformant with today's interactive multimedia instruction standards. Fundamental concepts are to: engage learners to solve real-world problems (progress from simple to complex); relate material to previous experience; demonstrate what is to be learned using interactive, problem-centered activities rather than passive exposure to material; require learners to use their new knowledge to solve problems that demonstrate their knowledge in a relevant applied setting; and guide the learner with feedback and coaching early, then gradually withdraw this support as learning progresses. Many of these principles have been put into practice by employing interactive learning objects as re-...

ABSTRACT This paper presents the multi-layer, data-driven advanced reasoning tool (M-DART), a proof-of-principle decision support tool for improved power system operation. M-DART will cross-correlate and examine different data sources to assess anomalies, infer root causes, and anneal data into actionable information. By performing higher-level reasoning “triage” of diverse data sources, M-DART focuses on early detection of emerging power system events and identifies highest priority actions for the human decision maker. M-DART represents a significant advancement over today's grid monitoring technologies that apply offline analyses to derive model-based guidelines for online real-time operations and use isolated data processing mechanisms focusing on individual data domains. The development of the M-DART will bridge these gaps by reasoning about results obtained from multiple data sources that are enabled by the smart grid infrastructure. This hybrid approach integrates a knowledge base that is trained offline but tuned online to capture model-based relationships while revealing complex causal relationships among data from different domains.

Technology-based enhancement of information analysis requires a detailed understanding of the cognitive tasks involved in the process. The information search and report production tasks of the information analysis process were investigated through evaluation of time-stamped workstation data gathered with custom software. Model tasks simulated the search and production activities, and a sample of actual analyst data were also evaluated. Task

A method and computer-based apparatus for monitoring the degradation of, predicting the remaining service life of, and/or planning maintenance for, an operating system are disclosed. Diagnostic information on degradation of the operating system is obtained through measurement of one or more performance characteristics by one or more sensors onboard and/or proximate the operating system. Though not required, it is preferred