Venue: ICLR 2018 Workshop

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: This paper introduces a method which combines Policy Gradient (or other model-free techniques such as A2C) with model-based reasoning powered by MCTS. Since the RL dynamics are known and deterministic MCTS naturally improves performance (much like in e.g. AlphaGo). The idea of using MCTS + deep RL is far from new, so the originality of this work is, in my opinion, quite weak. The only novel idea is how to deal with cycles in the knowledge graph traversal, and the fix is quite trivial. 

The application and good empirical results on tough KB reasoning datasets is quite good however. 

One important note: I disagree with the authors that this is a "partially observable" MDP, just because you don't base your decision on the current node doesn't mean the other nodes are unobserved -- they are observed just not included in your state vector. 
review_title: Cool application of PG + MCTS to KB reasoning, potentially not novel enough

final_score: rating: 6: Marginally above acceptance threshold
review_content: This paper uses MCTS + RL for answering queries on a knowledge base graph. The contribution of this paper is to do planning using MCTS instead of policy gradient. This indeed is important in the sparse reward setting and leads to better peformance. 
1. Given that this approach is so close to Minerva, please explicitly contrast against it at the beginning of the paper. The basic explanation is hurried and not very clear. 
2. In the line, "we use the following formula to calculate the score for each unique candidate n. .." Please justify this. Isn't there a more principled way to address this issue?


review_title: Uses MCTS + RL for knowledge base completion

Meta-review: Congratulations, your paper was accepted to the ICLR workshop.
Decision: Accept