The ETURWG encourages a community experience within the areas of focus. This facilitates relationships between members to develop through sharing of knowledge, collaborative working, and creation of data repositories for use by the group as test beds. Discussions, archives, data, and other resources can be accessed via the ETURWG website. The rules governing the activities of the group are as follows:

• Members may leave the group at any time, and new members may be added at any time subject to unanimous consent of the current members of the group. Visitors are allowed at ETURWG meetings, at the discretion of the group.
• The group will host at least one special session each year, as part of the ISIF International Conference on Information Fusion (FUSION) and/or another conference of interest.
• The working group chairs will rotate among members. The tenure of a chairs starts immediately following a working group meeting, and concludes with the following working group meeting.
• The group will report on a yearly basis to the ISIF BoD, either by a short presentation at an ISIF BoD meeting, or by a short written document.
• ISIF and the ETUR retain the right to sever their ties at any time.

The goal of the ETUR is bring together experts, researchers, and practitioners from the Fusion community to leverage the advances and developments in the area of evaluation of uncertainty representation to address the problem of evaluating uncertainty representation and reasoning approaches for High Level Information Fusion (HLIF) systems.
The group will provide a forum for its members to collectively address a common need for the ISIF community, coordinate with researchers in the area, and and evaluate techniques for assessing, managing, and reducing uncertainty. The main aspects involved are

1. to establish features required for any quantitative uncertainty representation to support the exchange of soft and hard information in a net-centric environment;
2. to develop a set of use cases involving information exchange and fusion requiring reasoning and inference under uncertainty; and
3. to define evaluation criteria supporting unbiased comparisons among different approaches applied to the use cases.

One of the main goals of information fusion is uncertainty reduction. Quantification of uncertainty reduction depends on how uncertainty is represented. Uncertainty representation differs across the various Levels of Information Fusion (as defined by the JDL/DFIG models). For Level 1 Fusion, standard measures of uncertainty reduction are widely accepted in the community. For tracking, uncertainty reduction corresponds to reducing spatial (distance) and temporal (time) errors; for identification, the goal is to increase the probability of detection and reduce the probability of false alarms. However, for levels 2 and above (hereafter called High-Level Information Fusion – HFIL), the requirements and success criteria are not well standardized for the information fusion community.
High-level information fusion of hard and soft information from diverse sensor types still depends heavily on human cognition. This results in a scalability conundrum that current technologies are incapable of solving. Although there is widespread acknowledgement that an HLIF framework must support automated knowledge representation and reasoning with uncertainty, there is no consensus on the requirements an HLIF framework must meet, on the most appropriate technologies to satisfy these requirements, and on how to evaluate how well they are being met. A clearly defined, scientifically rigorous evaluation framework and metrics are needed to help information fusion researchers assess the suitability of various approaches and tools to their applications. The ETUR working group is devoted to bring this evaluation framework to fruition.

If fully successful, the WG will provide the ISIF community with standardized methods, metrics, tools, and supporting data for evaluation of uncertainty representation in HLIF systems. Even if the WG doesn’t achieve a complete consensus on the evaluation framework, the techniques discussed and lessons learned in the process would be important for the researchers in the ISIF community. In both cases, the bigger impact would be for the customers of information fusion systems to determine how measure, evaluate, and approve systems that assess the situation beyond Level 1 fusion. The customers of information fusion products would have guidelines to draft requirements documentation, the gain of fusion systems over current techniques, as well as issues that are important in information fusion systems design.

Tasks at higher levels of the information fusion framework [e.g. the FPMFWG (Fusion Process Models and Frameworks Working Group)], such as predicting threat behavior, require reasoning about complex situations in which entities of different types are related to each other in diverse ways. This is particularly true in asymmetric warfare where the threats are elusive, secretive, and decentralized entities that often appear unconnected and exhibit stealthy behaviour that is very difficult to predict. Automated methods for reasoning about such complex situations require expressive languages that can represent and reason with uncertainty. Recent years have seen rapid advances in the expressive languages that support systems in providing the ability to represent and reason about complex situations with uncertainty.
As these technologies have become more mature, interest grew in many disciplines in which uncertainty plays a major role. Starting in the eighties in the field of artificial intelligence (AI), discussion of the suitability of each technique to address the diverse aspects in each potential application fuelled various initiatives to promote clarification on the issue. Although there is no shortage of comparisons between major approaches for representing and reasoning with uncertainty, from the standpoint of the HLIF community there are enough questions left unanswered to make it difficult for a researcher or practitioner to choose an approach best suited to his/her problem. This situation not only causes a waste of resources in various steps of the research and development processes but also fosters misconceptions that can jeopardize the advancement of the community as a whole.
A credible, unbiased framework is needed for evaluation of uncertainty management methods in information fusion. The framework must span all levels of the JDL fusion hierarchy, and must be applicable to a broad range of application areas for fusion technology. It must support fully automated as well as human-in-the-loop systems; and distributed as well as stand-alone systems. The framework must leverage the existing body of research on technologies for uncertainty representation and reasoning to produce a comprehensive, unbiased evaluation framework. A successful outcome of the ETUR WG will not only provide important insights into uncertainty management for HLIF, but will also produce a valuable tool for the community to support future comparisons.
Therefore, in order to achieve its goals, the ETUR WG will foster discussions and deliberations with the following outcomes:

1. Perform an in-depth analysis of the major requirements for representing and reasoning with uncertainty from the HLIF perspective;
2. Develop a set of use cases with enough complexity to cover the identified requirements;
3. Define a comprehensive set of criteria to evaluate how well a given methodology addresses the representational and reasoning needs of each use case; and
4. Conduct an evaluation of major uncertainty management approaches that could be used to address the use cases.

A key methodological component of this process is the design of use cases that exemplify complex situations involving difficult aspects of uncertainty representation and reasoning, especially those that any HLIF system must be capable of handling. The use cases must be derived from, and support identification of, requirements that any HLIF system ought to address. The evaluation plan must also include development of metrics to evaluate performance of the uncertainty management methods on the use case scenarios. In the schedule proposed below, the WG will follow a spiral systems engineering process to achieve its goals. Each cycle of the spiral will include all four of the above steps, which should increase in detail and complexity as the group leverages the lessons learned from previous cycles.

Proposed Schedule - 2011/2012