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

核心动机是调查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类友好的逻辑。由于论证与可废止知识的性质相吻合，人们对论证作为符号层的研究基本上是从关注开始的。通过剥离对内在逻辑结构的假设，转而关注论点中攻击的性质，建立了一个简单的模型，该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-真假属性。因此，在这个阶段，研究的主要目的是根据与论证结构的潜在联系来探索机器学习理论。提出了一种神经网络结构，该结构根据Dung语义捕获论证的表述；攻击由边表示，可以从参数可接受性数据中学习。这一实施将扩展到更复杂的论证形式，通过允许对与论点和攻击相关的价值观以及它们各自优势的可变性表示怀疑，从而更准确地反映知识的可行性。需要进一步研究高级论证技术，特别是基于数值的论证。最后，研究将寻求将该理论发展成一个可行的应用，在一个允许与现有方法进行比较的领域。

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.