Test and evaluation of soft/hard information fusion systems: A test environment, methodology and initial data sets

Hall, David L.; More, Loretta D.; Graham, Jake; Rimland, Jeffrey C.

doi:10.1109/icif.2010.5712080

Cited by 12 publications

(8 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The value of "typical" paleo-hydrological proxies (Baker et al, 1993;Jarrett, 1991) for evaluating century-scale changes in watersheds is not well understood. Accessing the hydrologically relevant information embedded in historical data sets -for instance information regarding construction of run-of-river dams for mill operation in the USA (Merritts et al, 2011;Walter and Merritts, 2008) -is likely to require interdisciplinary research, methods for fusing "hard" and "soft" data, and ways to address the reliability of information derived from "human sensors" (Hall et al, 2010).…”

Section: Hydrologic Reconstructionmentioning

confidence: 99%

Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene

Thompson

Sivapalan

Harman

et al. 2013

Hydrol. Earth Syst. Sci.

130

View full text Add to dashboard Cite

Abstract.Globally, many different kinds of water resources management issues call for policy-and infrastructure-based responses. Yet responsible decision-making about water resources management raises a fundamental challenge for hydrologists: making predictions about water resources on decadal-to century-long timescales. Obtaining insight into hydrologic futures over 100 yr timescales forces researchers to address internal and exogenous changes in the properties of hydrologic systems. To do this, new hydrologic research must identify, describe and model feedbacks between water and other changing, coupled environmental subsystems. These models must be constrained to yield useful insights, despite the many likely sources of uncertainty in their predictions. Chief among these uncertainties are the impacts of the increasing role of human intervention in the global water cycle -a defining challenge for hydrology in the Anthropocene. Here we present a research agenda that proposes a suite of strategies to address these challenges from the perspectives of hydrologic science research. The research agenda focuses on the development of co-evolutionary hydrologic modeling to explore coupling across systems, and to address the implications of this coupling on the long-time behavior of the coupled systems. Three research directions support the development of these models: hydrologic reconstruction, comparative hydrology and model-data learning. These strategies focus on understanding hydrologic processes and feedbacks over long timescales, across many locations, and through strategic coupling of observational and model data in specific systems. We highlight the value of use-inspired and team-based science that is motivated by real-world hydrologic problems but targets improvements in fundamental understanding to support decision-making and management. Fully realizing the potential of this approach will ultimately require detailed integration of social science and physical science understanding of water systems, and is a priority for the developing field of sociohydrology.

show abstract

Section: Hydrologic Reconstructionmentioning

confidence: 99%

Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene

Thompson

Sivapalan

Harman

et al. 2013

Hydrol. Earth Syst. Sci.

130

View full text Add to dashboard Cite

show abstract

“…A third role of the EEL is to facilitate the meta-process of evaluating the system performance (often referred to as "Level 4" in the JDL data fusion model). In a hybrid system of this nature, there are a variety of challenges related to the test and evaluation process [7]. The advanced visualization/sonification capabilities of the EEL help to overcome some of these obstacles.…”

Section: Visualization / Sonificationmentioning

confidence: 99%

“…In the sections that follow, the criticadjudicated agent-based hybrid-sensing / hybrid-cognition model will be explained in the context of this MURI research at Penn State. Additional details on the data sets being created and the specific hard and soft fusion algorithms are provided in [7] and [8]. Methods for implementing the multi-agent systems involve the use of concepts such as the team-based agent approach described by Fan et al [9].…”

Section: Introductionmentioning

confidence: 99%

A multi-agent infrastructure for hard and soft information fusion

Rimland

Hall

2011

SPIE Proceedings

View full text Add to dashboard Cite

Current needs in tactical situational awareness require a new type of infrastructure to encode, transmit, store, fuse, and display vastly heterogeneous data that may include "hard" sensor types including video, radar, multispectral, acoustic sensor array, 3D flash LIDAR, and "soft" sensor inputs such as textual reports from trained and untrained personnel, unsolicited and solicited open source web information, and hybrid "hard/soft" data such as human-annotated image or video data -which can be highly useful, but difficult to categorize and exploit.While the demand for scalability, rapid deployment, and decentralized access to data and services grows, the need for data security and integrity is as critical as ever. Methods for handling the conflicting needs between access and security are addressed. Furthermore, the evolving role of humans in data fusion systems must be addressed by the infrastructure. In addition to systems enhancing human data analysis capabilities through advanced visualization and sonification techniques, the data itself is more likely to contain information about humans -which is not always a task well suited to conventional data storage and retrieval methods.This paper describes a multi-agent approach to designing a secure, distributed, service-oriented infrastructure to support human-centric hard and soft information fusion.

show abstract

“…For example, the human mobile device user can record video footage and simultaneously link that video to their GPS location and provide annotations (thus creating real-time metadata). However, this presents the added difficulty of ascertaining accuracy, reliability, and precision of data that is derived from fused human/machine sources [31].…”

Section: Updates To Data Sources and Level 0 Pre-processingmentioning

confidence: 99%

Conserving analyst attention units: use of multi-agent software and CEP methods to assist information analysis

2013

View full text Add to dashboard Cite

Although the capability of computer-based artificial intelligence techniques for decision-making and situational awareness has seen notable improvement over the last several decades, the current state-of-the-art still falls short of creating computer systems capable of autonomously making complex decisions and judgments in many domains where data is nuanced and accountability is high. However, there is a great deal of potential for hybrid systems in which software applications augment human capabilities by focusing the analyst's attention to relevant information elements based on both a priori knowledge of the analyst's goals and the processing/correlation of a series of data streams too numerous and heterogeneous for the analyst to digest without assistance.Researchers at Penn State University are exploring ways in which an information framework influenced by Klein's (Recognition Primed Decision) RPD model, Endsley's model of situational awareness, and the Joint Directors of Laboratories (JDL) data fusion process model can be implemented through a novel combination of Complex Event Processing (CEP) and Multi-Agent Software (MAS). Though originally designed for stock market and financial applications, the high performance data-driven nature of CEP techniques provide a natural compliment to the proven capabilities of MAS systems for modeling naturalistic decision-making, performing process adjudication, and optimizing networked processing and cognition via the use of "mobile agents." This paper addresses the challenges and opportunities of such a framework for augmenting human observational capability as well as enabling the ability to perform collaborative context-aware reasoning in both human teams and hybrid human / software agent teams.

show abstract

Test and evaluation of soft/hard information fusion systems: A test environment, methodology and initial data sets

Cited by 12 publications

References 3 publications

Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene

Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene

A multi-agent infrastructure for hard and soft information fusion

Conserving analyst attention units: use of multi-agent software and CEP methods to assist information analysis

Contact Info

Product

Resources

About