Browsing by Author "Preece, A."

Now showing 1 - 5 of 5

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Agilely assigning sensing assets to mission tasks in a coalition context
(IEEE, 2013) Preece, A.; Norman, T.; de Mel, G.; Pizzocaro, D.; Şensoy, Murat; Pham, T.; Computer Science; ŞENSOY, Murat
When managing intelligence, surveillance, and reconnaissance (ISR) operations in a coalition context, assigning available sensing assets to mission tasks can be challenging. The authors' approach to ISR asset assignment uses ontologies, allocation algorithms, and a service-oriented architecture.
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Interpretability of deep learning models: a survey of results
(IEEE, 2018-06-26) Chakraborty, S.; Tomsett, R.; Raghavendra, R.; Harborne, D.; Alzantot, M.; Cerutti, F.; Srivastava, M.; Preece, A.; Julier, S.; Rao, R. M.; Kelley, T. D.; Braines, D.; Şensoy, Murat; Willis, C. J.; Gurram, P.; Computer Science; ŞENSOY, Murat
Deep neural networks have achieved near-human accuracy levels in various types of classification and prediction tasks including images, text, speech, and video data. However, the networks continue to be treated mostly as black-box function approximators, mapping a given input to a classification output. The next step in this human-machine evolutionary process - incorporating these networks into mission critical processes such as medical diagnosis, planning and control - requires a level of trust association with the machine output. Typically, statistical metrics are used to quantify the uncertainty of an output. However, the notion of trust also depends on the visibility that a human has into the working of the machine. In other words, the neural network should provide human-understandable justifications for its output leading to insights about the inner workings. We call such models as interpretable deep networks. Interpretability is not a monolithic notion. In fact, the subjectivity of an interpretation, due to different levels of human understanding, implies that there must be a multitude of dimensions that together constitute interpretability. In addition, the interpretation itself can be provided either in terms of the low-level network parameters, or in terms of input features used by the model. In this paper, we outline some of the dimensions that are useful for model interpretability, and categorize prior work along those dimensions. In the process, we perform a gap analysis of what needs to be done to improve model interpretability.
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Learning and reasoning in complex coalition information environments: a critical analysis
(IEEE, 2018-09-05) Cerutti, F.; Alzantot, M.; Xing, T.; Harborne, D.; Bakdash, J. Z.; Braines, D.; Chakraborty, S.; Kaplan, L.; Kimmig, A.; Preece, A.; Raghavendra, R.; Şensoy, Murat; Srivastava, M.; Computer Science; ŞENSOY, Murat
In this paper we provide a critical analysis with metrics that will inform guidelines for designing distributed systems for Collective Situational Understanding (CSU). CSU requires both collective insight - i.e., accurate and deep understanding of a situation derived from uncertain and often sparse data and collective foresight - i.e., the ability to predict what will happen in the future. When it comes to complex scenarios, the need for a distributed CSU naturally emerges, as a single monolithic approach not only is unfeasible: it is also undesirable. We therefore propose a principled, critical analysis of AI techniques that can support specific tasks for CSU to derive guidelines for designing distributed systems for CSU.
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Subjective bayesian networks and human-in-the-loop situational understanding
(Springer, 2018-03-21) Braines, D.; Thomas, A.; Kaplan, L.; Şensoy, Murat; Bakdash, J. Z.; Ivanovska, M.; Preece, A.; Cerutti, F.; Computer Science; ŞENSOY, Murat
In this paper we present a methodology to exploit human-machine coalitions for situational understanding. Situational understanding refers to the ability to relate relevant information and form logical conclusions, as well as identify gaps in information. This process for comprehension of the meaning information requires the ability to reason inductively, for which we will exploit the machines’ ability to ‘learn’ from data. However, important phenomena are often rare in occurrence with high degrees of uncertainty, thus severely limiting the availability of instance data for training, and hence the applicability of many machine learning approaches. Therefore, we present the benefits of Subjective Bayesian Networks—i.e., Bayesian Networks with imprecise probabilities—for situational understanding, and the role of conversational interfaces for supporting decision makers in the evolution of situational understanding.
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Uncertainty-aware situational understanding
(SPIE, 2019) Tomsett, R.; Kaplan, L.; Cerutti, F.; Sullivan, P.; Vente, D.; Vilamala, M. R.; Kimmig, A.; Preece, A.; Şensoy, Murat; Computer Science; Pham, T.; ŞENSOY, Murat
Situational understanding is impossible without causal reasoning and reasoning under and about uncertainty, i.e. prob-abilistic reasoning and reasoning about the confidence in the uncertainty assessment. We therefore consider the case of subjective (uncertain) Bayesian networks. In previous work we notice that when observations are out of the ordinary, confidence decreases because the relevant training data-effective instantiations-to determine the probabilities for unobserved variables-on the basis of the observed variables-is significantly smaller than the size of the training data-the total number of instantiations. It is therefore of primary importance for the ultimate goal of situational understanding to be able to efficiently determine the reasoning paths that lead to low confidence whenever and wherever it occurs: this can guide specific data collection exercises to reduce such an uncertainty. We propose three methods to this end, and we evaluate them on the basis of a case-study developed in collaboration with professional intelligence analysts.