Venue: ICAPS 2021 Workshop XAIP

Reviews:

final_score: rating: 9: Top 15% of accepted papers, strong accept
review_content: Overview: This paper presents a method for identifying user's epistemic knowledge of a model without requiring a priori specification (i.e. it's adaptive). This approach is in the realm of 'model reconciliation', but aims at providing a more practical approach to identifying/using the user's knowledge of the domain to provide targeted explanations.

Comments:

- Motivation for this work is clear, expecially w.r.t. the drawbacks of many model reconciliation approaches in practice when there are going to be varied users.

- The clustering approach to identifying user types is interesting, however in practice it would be limited to planning domains for which, in practice, there are clear and distinct user 'types'. It is mentioned as a future direction to look beyond fixed types.

- Good to see both quantitative evaluation of algorithms along with user study. Again, since only one domain was tested, it's hard to know how this approach would extrapolate to other settings, but still useful to see. 

Overall nice ideas and starting point for practical personalised explanations.


review_title: Paper Review

final_score: rating: 7: Good paper, accept
review_content: The paper is original and mostly clear (some suggestions below). The connection to active sensing and POMDPs is specially interesting and appreciated.

One issue that I had was in envisioning how such a system would work in practice (in "the real world"). For example, how do the authors envision this system being sustainably trained (to gather data from users and cluster their "types")? I imagine the types depend heavily on the domain, but many systems will have a single or few users, e.g. a single operator --- so even if the operator could follow one of several types of "types" or "personalities" we probably do not have access to them unless we test the system with many other users that are not the single user. It might be easier to just do personalization for the operator we have than to gather so much data from people that might not be good models of real users.

Even if a system has several users (as in the examples provided in the paper), this approach still requires finding many other users for training (costly) or gathering a lot of data from the existing users (which might be enough to train them and change their user "type" by the end).

So it would be interesting to read the authors' vision of how this would work in practice. Please try to discuss it in more depth in the paper.

Other suggestions:

1) The motivation is very clear in the second part of the second paragraph ("they generally look at the problem...") but not in the abstract, perhaps you could condense this into the abstract to improve it
2) In Figure 1 add some more text to the table (B) to make it self-contained. For example: "actions and transitions that user associates with r=-1" or "actions and transitions that user believes harmful"
3) [0,1]^|E| seems to be a continuous interval, I believe {0,1}^|E| would be more appropriate
4) In the S_epsilon equation it should be 2^|E| instead of 2^E
5) Discuss the consequences of the clustering not working on the setting with 4 user types (what's the impact? what could wrong? how could this be potentially avoided?)
6) Please elaborate what is meant by "ask specific questions about user's knowledge which may not be available to the system". Perhaps one or two examples would clarify what is meant here, but right now I cannot understand whether you mean some systems could not have interfaces to ask questions, or that some users might not know the answer to some questions, etc.
7) In the conclusion it is said that "we argue that while one size fits all explanations are easy to generate they can easily overwhelm the user", however, this claim is not actually supported by the paper, nor is this question pursued. For this paper I would remove this statement, but for future work it would be interesting to actually try to see how much users are overwhelmed.

Minor suggestions:
- in the abstract remove "undoubtedly" --- I don't think we have evidence of this lack of doubt (I have some doubt)
- rephrase "As in the specifics of the user model are completely ignored" to integrate better with the previous sentence
- rephrase "end-to-end" as this is misleading given the way the term is now heavily used in deep learning
- "a manner that a human observer would find explainable" ---> I believe you mean "explicable" here, or "self-explainable"
- "types of user that exists" ---> "types of users that exist"
- rephrase "quite close" in the results section to make it impartial (e.g. % closer)

Questions:

1) What is lost when you use simulations instead of real users to train user types?
2) In the Results section it is stated that "the 5 user types of the domain were assigned randomly". This does not mean that each (real) user was assigned a user type, right? I thought that the user type should be estimated from the user's behavior? Please clarify what was done and why.
review_title: Interesting and original, not clear how it would work in practice

Meta-review: metareview: Thanks very much for submitting your paper!

Summary: This paper proposes an end-to-end adaptive explanation generation system that learns the different types of users that the agent could interact with and adjust its explanations accordingly.

Strengths: 
- Clear motivation and interesting perspective
- Broad evaluation with both quantitative evaluation and user study

Limitations:
- Costly approach - i.e., requires gathering a lot of data from the existing users
- Missing link with real-world applications

We hope that you find the reviewers' comments to be informative, please take them into account when revising your papers. We look forward to your presentation!

confidence: 5: The area chair is absolutely certain
Decision: Accept