Multi-agent active perception is a task where a team of agents cooperatively gathers observations to compute a joint estimate of a hidden variable.

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Multi-agent active perception is a task where a team of agents cooperatively gathers observations to compute a joint estimate of a hidden variable.

Weaknesses: The paper is well written and easy to follow. The problem is active perception is also interesting. There are a few areas where more clarification is needed as pointed below: -- The authors have highlighted a number of previous models for the problem of active perception such as Dec-\rhoPOMDP, POMDP-IR etc. Given the focus on converting this problem to a decentralized framework, it is not clearly conveyed why decentralizing the problem is significant? There are hints available in the paper such as less communication overhead, but there is no empirical evidence presented towards justifying decentralized approaches over such previous approaches (e.g., how much communication overhead is reduced) -- The technical approach presented by the authors is elegant and simple, but it is essentially a heuristic approach. The bound provided in theorem 1 would seem to be loose in the worst case (and its values in experiments is not shown).

This paper addresses the problem of multiagent active perception, a somewhat nascent area, and proposes a new reformulation of Dec-rho-POMDPs into a DEC-POMDP though the addition of a final-stage "predictive action." The reviewers appreciated the novelty of this contribution as well as the theoretical analysis/loss bounds. The original reviews raised a number of questions however, and the author response addressed many of these. However, there remain some issues that undercut the significance of the contribution, including: the somewhat incremental combination/adaptation of existing techniques; the fact that the claimed scalability is not demonstrated very convincingly in the experiments; among others. On my reading of the paper, I largely concur and do not reiterate the positive contributions in the other reviews, but point out some concerns about importance/impact: 1.

Multi-agent active perception is a task where a team of agents cooperatively gathers observations to compute a joint estimate of a hidden variable. The task is decentralized and the joint estimate can only be computed after the task ends by fusing observations of all agents. The objective is to maximize the accuracy of the estimate. The accuracy is quantified by a centralized prediction reward determined by a centralized decision-maker who perceives the observations gathered by all agents after the task ends. In this paper, we model multi-agent active perception as a decentralized partially observable Markov decision process (Dec-POMDP) with a convex centralized prediction reward.