Reinforcement Logic Rule Learning for Temporal Point Processes
Chao Yang Lu Wang Kun Gao Shuang Li
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.