Recognizing Plans by Learning Embeddings from Observed Action Distributions
Plan recognition aims to look for target plans to best explain the observed actions based on plan libraries and/or domain models. Despite the success of previous approaches on plan recognition, they mostly rely on correct action observations. Recent advances in visual activity recognition have the potential of enabling applications such as automated video surveillance. Effective approaches for such problems would require the ability to recognize the plans of agents from video information. Traditional plan recognition algorithms rely on access to detailed planning domain models. One recent promising direction involves learning approximate (or shallow) domain models directly from the observed activity sequences. Such plan recognition approaches expect observed action sequences as inputs. However, visual inference results are often noisy and uncertain, typically represented as a distribution over possible actions. In this work, we develop a visual plan recognition framework that recognizes plans with an approximate domain model learned from uncertain visual data.
The paper presents an approach to unsupervised plan recognition from video, which can take into account the uncertainty of actions that have been recognized automatically from video, in this case the 50 salads database. This probabilistic approach seems plausible, but I would honestly be surprised if something like this has not been attempted before. The paper contains very little discussion of the relevance of the results to the state of the art and about other related techniques, making it hard for someone not from the field to judge the contribution of this work. The proposed approach seems straightforward and reasonable. I am not sure if the 50 salads database is not maybe considered a toy task, and how the results presented here compare to other work - this should definitely be addressed before publication. Finally, the paper is not well formatted, and most images have very small captions (font size), making it hard to read.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Recognizing Plans by Learning Embeddings from Observed Action Distributions
Plan recognition aims to look for target plans to best explain the observed actions based on plan libraries and/or domain models. Despite the success of previous approaches on plan recognition, they mostly rely on correct action observations. Recent advances in visual activity recognition have the potential of enabling applications such as automated video surveillance. Effective approaches for such problems would require the ability to recognize the plans of agents from video information. Traditional plan recognition algorithms rely on access to detailed planning domain models. One recent promising direction involves learning approximate (or shallow) domain models directly from the observed activity sequences. Such plan recognition approaches expect observed action sequences as inputs. However, visual inference results are often noisy and uncertain, typically represented as a distribution over possible actions. In this work, we develop a visual plan recognition framework that recognizes plans with an approximate domain model learned from uncertain visual data.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The paper presents an approach to unsupervised plan recognition from video, which can take into account the uncertainty of actions that have been recognized automatically from video, in this case the 50 salads database. This probabilistic approach seems plausible, but I would honestly be surprised if something like this has not been attempted before. The paper contains very little discussion of the relevance of the results to the state of the art and about other related techniques, making it hard for someone not from the field to judge the contribution of this work. The proposed approach seems straightforward and reasonable. I am not sure if the 50 salads database is not maybe considered a toy task, and how the results presented here compare to other work - this should definitely be addressed before publication. Finally, the paper is not well formatted, and most images have very small captions (font size), making it hard to read.
assistant
The paper presents an approach to unsupervised plan recognition from video, which can take into account the uncertainty of actions that have been recognized automatically from video, in this case the 50 salads database. This probabilistic approach seems plausible, but I would honestly be surprised if something like this has not been attempted before. The paper contains very little discussion of the relevance of the results to the state of the art and about other related techniques, making it hard for someone not from the field to judge the contribution of this work. The proposed approach seems straightforward and reasonable. I am not sure if the 50 salads database is not maybe considered a toy task, and how the results presented here compare to other work - this should definitely be addressed before publication. Finally, the paper is not well formatted, and most images have very small captions (font size), making it hard to read.