Physical Sciences and Mathematics Commons^™

The Intelligence Game: Assessing Delphi Groups And Structured Question Formats, Bonnie Wintle, Steven Mascaro, Fiona Fidler, Marissa Mcbride, Mark Burgman, Louisa Flander, Geoff Saw, Charles Twardy, Aidan Lyon, Brian Manning

Australian Security and Intelligence Conference

In 2010, the US Intelligence Advanced Research Projects Activity (IARPA) announced a 4-year forecasting “tournament”. Five collaborative research teams are attempting to outperform a baseline opinion pool in predicting hundreds of geopolitical, economic and military events. We are contributing to one of these teams by eliciting forecasts from Delphi-style groups in the US and Australia. We elicit probabilities of outcomes for 3-5 monthly questions, such as: Will Australia formally transfer uranium to India by 1 June 2012? Participants submit probabilities in a 3-step interval format, view those of others in their group, share, rate and discuss information, and then make …

Temporal Data Mining Of Uncertain Water Reservoir Data, Abhinaya Mohan, Peter Revesz

CSE Conference and Workshop Papers

This paper describes the challenges of data mining uncertain water reservoir data based on past human operations in order to learn from them reservoir policies that can be automated for the future operation of the water reservoirs. Records of human operations of water reservoirs often contain uncertain data. For example, the recorded amounts of water released and retained in the water reservoirs are typically uncertain, i.e., they are bounded by some minimum and maximum values. Moreover, the time of release is also uncertain, i.e., typically only monthly or weekly amounts are recorded. To increase the effectiveness of data mining of …

Uncertainty-Aware Video Visual Analytics Of Tracked Moving Objects, Markus Höferlin, Benjamin Höferlin, Daniel Weiskopf, Gunther Heidemann

Journal of Spatial Information Science

Vast amounts of video data render manual video analysis useless while recent automatic video analytics techniques suffer from insufficient performance. To alleviate these issues we present a scalable and reliable approach exploiting the visual analytics methodology. This involves the user in the iterative process of exploration hypotheses generation and their verification. Scalability is achieved by interactive filter definitions on trajectory features extracted by the automatic computer vision stage. We establish the interface between user and machine adopting the VideoPerpetuoGram (VPG) for visualization and enable users to provide filter-based relevance feedback. Additionally users are supported in deriving hypotheses by context-sensitive statistical …

Scalable Content Authentication In H.264/Svc Videos Using Perceptual Hashing Based On Dempster-Shafer Theory, Dengpan Ye, Zhuo Wei, Xuhua Ding, Robert H. Deng

Research Collection School Of Computing and Information Systems

The content authenticity of the multimedia delivery is important issue with rapid development and widely used of multimedia technology. Till now many authentication solutions had been proposed, such as cryptology and watermarking based methods. However, in latest heterogeneous network the video stream transmission has b een coded in scalable way such as H.264/SVC, there is still no good authentication solution. In this paper, we firstly summarized related works and p roposed a scalable content authentication scheme using a ratio of different energy (RDE) based perceptual hashing in Q/S dimension, which is used Dempster-Shafer theory and combined with the latest scalable …

How To Divide Students Into Groups So As To Optimize Learning: Towards A Solution To A Pedagogy-Related Optimization Problem, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

To enhance learning, it is desirable to also let students learn from each other, e.g., by working in groups. It is known that such groupwork can improve learning, but the effect strongly depends on how we divide students into groups. In this paper, based on a first approximation model of student interaction, we describe how to optimally divide students into groups so as to optimize the resulting learning. We hope that, by taking into account other aspects of student interaction, it will be possible to transform our solution into truly optimal practical recommendations.

Prioritized Shaping Of Models For Solving Dec-Pomdps, Pradeep Reddy Varakantham, William Yeoh, Prasanna Velagapudi, Paul Scerri

Research Collection School Of Computing and Information Systems

An interesting class of multi-agent POMDP planning problems can be solved by having agents iteratively solve individual POMDPs, find interactions with other individual plans, shape their transition and reward functions to encourage good interactions and discourage bad ones and then recompute a new plan. D-TREMOR showed that this approach can allow distributed planning for hundreds of agents. However, the quality and speed of the planning process depends on the prioritization scheme used. Lower priority agents shape their models with respect to the models of higher priority agents. In this paper, we introduce a new prioritization scheme that is guaranteed to …

Stochastic Dominance In Stochastic Dcops For Risk-Sensitive Applications, Nguyen Duc Thien, William Yeoh, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Distributed constraint optimization problems (DCOPs) are well-suited for modeling multi-agent coordination problems where the primary interactions are between local subsets of agents. However, one limitation of DCOPs is the assumption that the constraint rewards are without uncertainty. Researchers have thus extended DCOPs to Stochastic DCOPs (SDCOPs), where rewards are sampled from known probability distribution reward functions, and introduced algorithms to find solutions with the largest expected reward. Unfortunately, such a solution might be very risky, that is, very likely to result in a poor reward. Thus, in this paper, we make three contributions: (1) we propose a stricter objective for …

Partial Orders For Representing Uncertainty, Causality And Decision Making: General Properties, Operations, And Algorithms, Francisco Adolfo Zapata

Open Access Theses & Dissertations

One of the main objectives of science and engineering is to help people select the most beneficial decisions. To make these decisions, we must know people's preferences, we must have the information about different possible consequences of different decisions. Since information is never absolutely accurate and precise, we must also have information about the degree of certainty of different parts on information. All these types of information naturally lead to partial orders:

- For preferences, a <= b means that b is preferable to a. This relation is used in decision theory.

- For events, a <= b means that a can influence b. This causality relation is one of the fundamental notions of physics, especially of physics of space-time.

* For uncertain statements, a <= b means that a is less certain than b. This relation is used in logics describing uncertainty, such as fuzzy logic.

In each of these areas, there is abundant research about studying the corresponding partial orders. …

Robust Distributed Scheduling Via Time Period Aggregation, Shih-Fen Cheng, John Tajan, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

In this paper, we evaluate whether the robustness of a market mechanism that allocates complementary resources could be improved through the aggregation of time periods in which resources are consumed. In particular, we study a multi-round combinatorial auction that is built on a general equilibrium framework. We adopt the general equilibrium framework and the particular combinatorial auction design from the literature, and we investigate the benefits and the limitation of time-period aggregation when demand-side uncertainties are introduced. By using simulation experiments on a real-life resource allocation problem from a container port, we show that, under stochastic conditions, the performance variation …