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This paper presents Co-Arg, a new type of cognitive assistant to an intelligence analyst that enables the synergistic integration of analyst imagination and expertise, computer knowledge and critical reasoning, and crowd wisdom, to draw defensible and persuasive conclusions from masses of evidence of all types, in a world that is changing all the time. Co-Args goal is to improve the quality of the analytic results and enhance their understandability for both experts and novices. The performed analysis is based on a sound and transparent argumentation that links evidence to conclusions in a way that shows very clearly how the conclusions have been reached, what evidence was used and how, what is not known, and what assumptions have been made. The analytic results are presented in a report describes the analytic conclusion and its probability, the main favoring and disfavoring arguments, the justification of the key judgments and assumptions, and the missing information that might increase the accuracy of the solution.
In many real-life situations that involve exchanges of arguments, individuals may differ on their assessment of which supports between the arguments are in fact justified, i.e., they put forward different support-relations. When confronted with such
This paper applies t-SNE, a visualisation technique familiar from Deep Neural Network research to argumentation graphs by applying it to the output of graph embeddings generated using several different methods. It shows that such a visualisation appr
The combination of argumentation and probability paves the way to new accounts of qualitative and quantitative uncertainty, thereby offering new theoretical and applicative opportunities. Due to a variety of interests, probabilistic argumentation is
The paper develops a formal theory of the degree of justification of arguments, which relies solely on the structure of an argumentation framework, and which can be successfully interfaced with approaches to instantiated argumentation. The theory is
We analyze the recently released CoGeNT data with a focus on their time-dependent properties. Using various statistical techniques, we confirm the presence of modulation in the data, and find a significant component at high (E_{ee} > 1.5$ keVee) ener