Exploring the connections between symbolic and subsymbolic reasoning in producing data-driven decision making.

核心动机是研究混合推理机的潜在优势，这种推理机可以利用机器学习技术的力量，同时仍然受益于相对人性化的逻辑。主要的研究已经开始关注非常集中在论证作为符号层，由于其雅阁的属性，可废止的知识。通过剥离关于内在逻辑结构的假设，而不是专注于论点中攻击的性质，建立了一个简单的模型，可以与复杂的算法协调，而不会失去知识的最基本属性-真与假。因此，在这个阶段，研究的主要目的是探索机器学习理论与论证结构的潜在联系。已经提出了一种神经网络架构，该架构根据Dung语义捕获论证的制定;攻击由边缘表示，并且可以从论证可接受性数据中学习。这种实现将扩展到更复杂的论证形式，更准确地反映了可废止的性质的知识，允许怀疑，以表示与参数和攻击以及在其各自的优势变化相关的价值。需要进一步研究先进的论证技术，特别注意基于数值的论证。最后，研究将寻求发展的理论到一个可行的应用领域，允许与现有的方法进行比较。

The core motive is investigation of the potential advantages of a hybrid reasoner that can draw on the power of machine learning techniques whilst still benefiting from relatively human-friendly logic. Principally the research has begun with attention very much focused on argumentation as the symbolic layer due to its accord with the properties of defeasible knowledge. By stripping away assumptions about inherent logical structures and instead focusing on the nature of attacks within arguments a simple model is established that can harmonise with complex algorithms without losing the most fundamental property of knowledge - that of truth and falsehood.At this stage the research is thus primarily aimed at exploring machine learning theory in light of potential connections with argumentation structures. A neural network architecture has been proposed that captures the formulation of argumentation as per Dung semantics; attacks are represented by edges and can be learned from argument acceptability data. This implementation will be extended to more complex forms of argumentation that more accurately reflect the defeasible nature of knowledge by permitting doubt to be expressed with respect to the values associated with arguments and attacks as well as variability in their individual strengths. Further research on advanced argumentation techniques is required with particular attention given to numerical based argumentation.Finally the research will look to develop the theory into a viable application in a domain that permits comparison with extant methods.