Venue: ICAPS 2019 Workshop XAIP

Reviews:

final_score: rating: 2: Marginally below acceptance threshold
review_content: I am not sure if the paper is relevant to the XAIP Workshop. I suppose goal recognition can be seen as a way of trying to "explain" what the goal of an agent is from observations. But the authors made no attempt to make that stance. Even if that is the case, there is plenty of existing work [1] where the agent tries to make the goal or plan recognition task easier for the observer.

If there is a case to be made for operator count heuristics specifically for this purpose, I would love to hear it.

[1] Explicability? Legibility? Predictability? Transparency? Privacy? Security? The Emerging Landscape of Interpretable Robot Behavior. Tathagata Chakraborti, Anagha Kulkarni, Sarath Sreedharan, David Smith, Subbarao Kambhampati.

[2] Also this might be interesting to the authors (at the intersection of XAIP and landmarks): https://arxiv.org/abs/1903.08218
review_title: Relevance to XAIP not clear

final_score: rating: 2: Marginally below acceptance threshold
review_content: This is a nicely done work on improving goal recognition in the planning-based framework, cleverly leveraging operator-counting heuristics.

I don't have anything to criticize on that content -- other than, why was it submitted to XAIP?

My best hypothesis is, the authors wanted to submit to some ICAPS workshop, browsed the list, and deemed XAIP to be closest.

Which in itself could still be fine. But the topic match is far from obvious, and the authors do not even make an attempt to discuss it. It cannot be the task of the reader to figure out how this is relevant to XAIP. (Actually it seems the authors submitted as-is, not even taking proper care in reformatting to AAAI style; in some places Section refs are broken because there is no section numering anymore; but this is just an aside)

The best I can come up with personally is that goal recognition can be construed as related to XAI as a prerequisite for understanding another agent's behaviour. But then, this is like a non-cooperative setting of XAI, where the agent one is trying to understand is not actually trying to be understood. Maybe this is relevant in some application, I don't know, but XAI as the term is usually understood means the opposite, trying to make an AI agent understandable to its environment/its users.

In any case, making sense of this can't be the task of the reader. And with so many submissions to XAIP we can't just accept anything, we need tp focus. So I lean to reject. Perhaps the authors could be invited to a panel on viewpoints on XAIP, if such a panel is oplanned (and if they are indeed willing to argue a connection to goal recognition).
review_title: Nice paper on improved goal recognition but why XAIP?

Meta-review: While the reviewers view this paper critically, the authors are correct in that goal reasoning is part of the CFP for this workshop. Furthermore, in the spirit of making the workshop a venue for discussion and feedback, we decided to reject only those papers with strong reject votes.

Please address the review criticism as best possible for the final paper version and its presentation at the workshop. In particular, please carefully discuss the relation to/links to XAIP, and the XAIP literature. Looking forward to discuss your work at the workshop!
Decision: Accept