重新思考可撤销推理：一种可扩展的方法
Rethinking Defeasible Reasoning: A Scalable Approach
摘要：
最近的技术进步导致产生了前所未有的数据量，这些数据来自网络，传感器网络和社交媒体。在可废止推理方面的分析-例如决策-可以提供更丰富的基础领域的知识。传统上，可废止的推理集中在小到中等数量的数据的复杂知识结构，但最近的研究工作试图并行化的推理过程中的理论与大量的事实。这些工作表明，传统的可废止逻辑带来了限制可扩展性的开销。在这项工作中，我们设计了一个新的逻辑可废止推理，从而确保可扩展性的设计。我们建立了几个属性的逻辑，包括它的关系，现有的可废止逻辑。我们的实验结果表明，我们的方法确实是可扩展的，可撤销推理可以应用于数十亿的事实。
英文摘要：
Recent technological advances have led to unprecedented amounts of generated data that originate from the Web, sensor networks and social media. Analytics in terms of defeasible reasoning - for example for decision making - could provide richer knowledge of the underlying domain. Traditionally, defeasible reasoning has focused on complex knowledge structures over small to medium amounts of data, but recent research efforts have attempted to parallelize the reasoning process over theories with large numbers of facts. Such work has shown that traditional defeasible logics come with overheads that limit scalability. In this work, we design a new logic for defeasible reasoning, thus ensuring scalability by design. We establish several properties of the logic, including its relation to existing defeasible logics. Our experimental results indicate that our approach is indeed scalable and defeasible reasoning can be applied to billions of facts.
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