We propose a framework that can incrementally expand the explanatory temporal logic rule set to explain the occurrence of temporal events. Leveraging the temporal point process modeling and learning framework, the rule content and weights will be gradually optimized until the likelihood of the observational event sequences is optimal. The proposed algorithm alternates between a master problem, where the current rule set weights are updated, and a subproblem, where a new rule is searched and included to best increase the likelihood. The formulated master problem is convex and relatively easy to solve using continuous optimization, whereas the subproblem requires searching the huge combinatorial rule predicate and relationship space. To tackle this challenge, we propose a neural search policy to learn to generate the new rule content as a sequence of actions. The policy parameters will be trained end-to-end using the reinforcement learning framework, where the reward signals can be efficiently queried by evaluating the subproblem objective. The trained policy can be used to generate new rules in a controllable way. We evaluate our methods on both synthetic and real healthcare datasets, obtaining promising results.

翻译：我们提出了一种框架，能够逐步扩展解释性时序逻辑规则集，以解释时序事件的发生。利用时序点过程建模与学习框架，规则内容及其权重将被逐步优化，直至观测事件序列的似然达到最优。所提算法在主问题（更新当前规则集权重）与子问题（搜索并纳入能最大化似然提升的新规则）之间交替迭代。主问题为凸优化问题，可通过连续优化方法轻松求解；而子问题需在庞大的规则谓词与关系组合空间中进行搜索。为应对这一挑战，我们提出了一种神经搜索策略，学习将新规则内容作为动作序列生成。该策略参数通过强化学习框架进行端到端训练，其中奖励信号可通过评估子问题目标高效获取。训练后的策略能以可控方式生成新规则。我们在合成数据集和真实医疗数据集上评估了所提方法，取得了令人满意的结果。