In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

[Q]: Heuristic search research often deals with finding algorithms for offline planning which aim to minimize the number of expanded nodes or planning time. In online planning, algorithms for real-time search or deadline-aware search have been considered before. However, in this paper, we are interested in the problem of {\em situated temporal planning} in which an agent's plan can depend on exogenous events in the external world, and thus it becomes important to take the passage of time into account during the planning process.
Previous work on situated temporal planning has proposed simple pruning strategies, as well as complex schemes for a simplified version of the associated metareasoning problem.
In this paper, we propose a simple metareasoning technique, called the crude greedy scheme, which can be applied in a situated temporal planner. Our empirical evaluation shows that the crude greedy scheme outperforms standard heuristic search based on cost-to-go estimates.
[A]: Metareasoning in a Situated Temporal Planner

[Q]: 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.
[A]: Handling Uncertainty in Visual Perception for Plan Recognition

[Q]: Analysis methods which enable us to better understand the
representations and functioning of neural models of language are
increasingly needed as deep learning becomes the dominant approach
in NLP. Here we present two methods based on Representational
Similarity Analysis (RSA) and Tree Kernels (TK) which allow us to
directly quantify how strongly the information encoded in neural
activation patterns corresponds to information represented by
symbolic structures such as syntax trees. We first validate our
methods on the case of a simple synthetic language for arithmetic
expressions with clearly defined syntax and semantics, and show that
they exhibit the expected pattern of results. We then apply our methods to
correlate neural representations of English sentences with their
constituency parse trees.
[A]:
Two methods based on Representational Similarity Analysis (RSA) and Tree Kernels (TK) which directly quantify how strongly information encoded in neural activation patterns corresponds to information represented by symbolic structures.