Classical planning formulations like the Planning Domain Definition Language
(PDDL) admit action sequences guaranteed to achieve a goal state given an
initial state if any are possible. However, reasoning problems defined in PDDL
do not capture temporal aspects of action taking, for example that two agents
in the domain can execute an action simultaneously if postconditions of each do
not interfere with preconditions of the other. A human expert can decompose a
goal into largely independent constituent parts and assign each agent to one of
these subgoals to take advantage of simultaneous actions for faster execution
of plan steps, each using only single agent planning. By contrast, large
language models (LLMs) used for directly inferring plan steps do not guarantee
execution success, but do leverage commonsense reasoning to assemble action
sequences. We combine the strengths of classical planning and LLMs by
approximating human intuitions for two-agent planning goal decomposition. We
demonstrate that LLM-based goal decomposition leads to faster planning times
than solving multi-agent PDDL problems directly while simultaneously achieving
fewer plan execution steps than a single agent plan alone and preserving
execution success. Additionally, we find that LLM-based approximations of
subgoals can achieve similar multi-agent execution steps than those specified
by human experts. Website and resources at this https URL

通过结合经典规划和大型语言模型的优势，我们实现了基于 LLMs 的目标分解，提高了计划的速度和执行成功，并且比单一代理计划少了更少的执行步骤，同时实现了与人类专家指定的多代理执行步骤类似的结果。

TwoStep：基于经典规划器和大型语言模型的多智能体任务规划

TwoStep: Multi-agent Task Planning using Classical Planners and Large  Language Models

Children learn powerful internal models of the world around them from a few
years of egocentric visual experience. Can such internal models be learned from
a child's visual experience with highly generic learning algorithms or do they
require strong inductive biases? Recent advances in collecting large-scale,
longitudinal, developmentally realistic video datasets and generic
self-supervised learning (SSL) algorithms are allowing us to begin to tackle
this nature vs. nurture question. However, existing work typically focuses on
image-based SSL algorithms and visual capabilities that can be learned from
static images (e.g. object recognition), thus ignoring temporal aspects of the
world. To close this gap, here we train self-supervised video models on
longitudinal, egocentric headcam recordings collected from a child over a two
year period in their early development (6-31 months). The resulting models are
highly effective at facilitating the learning of action concepts from a small
number of labeled examples; they have favorable data size scaling properties;
and they display emergent video interpolation capabilities. Video models also
learn more robust object representations than image-based models trained with
the exact same data. These results suggest that important temporal aspects of a
child's internal model of the world may be learnable from their visual
experience using highly generic learning algorithms and without strong
inductive biases.

从儿童的视觉经验中，可以通过高度通用的学习算法来学习他们对世界的强大内部模型，这包括长期记录的视频数据集和自我监管学习算法，而无需强烈的归纳偏差。

儿童视角下的自我监督学习视频表征

Self-supervised learning of video representations from a child's  perspective

As humans, we use linguistic elements referencing time, such as before or
tomorrow, to easily share past experiences and future predictions. While
temporal aspects of the language have been considered in computational
linguistics, no such exploration has been done within the field of emergent
communication. We research this gap, providing the first reported temporal
vocabulary within emergent communication literature. Our experimental analysis
shows that a different agent architecture is sufficient for the natural
emergence of temporal references, and that no additional losses are necessary.
Our readily transferable architectural insights provide the basis for the
incorporation of temporal referencing into other emergent communication
environments.

人类使用引用时间的语言元素（如昨天或明天）来轻松分享过去的经历和未来的预测。我们的研究通过在紧急通信文学领域进行首次报道的时间词汇提供了对这一差距的研究，实验分析结果表明不同的代理架构足以自然产生时间参考，并且不需要额外的损失。我们的架构见解易于传递，为将时间参考引入其他紧急通信环境提供了基础。

关于新兴交流中的时间参照

On Temporal References in Emergent Communication

In recent years session-based recommendation has emerged as an increasingly
applicable type of recommendation. As sessions consist of sequences of events,
this type of recommendation is a natural fit for Recurrent Neural Networks
(RNNs). Several additions have been proposed for extending such models in order
to handle specific problems or data. Two such extensions are 1.) modeling of
inter-session relations for catching long term dependencies over user sessions,
and 2.) modeling temporal aspects of user-item interactions. The former allows
the session-based recommendation to utilize extended session history and
inter-session information when providing new recommendations. The latter has
been used to both provide state-of-the-art predictions for when the user will
return to the service and also for improving recommendations. In this work we
combine these two extensions in a joint model for the tasks of recommendation
and return-time prediction. The model consists of a Hierarchical RNN for the
inter-session and intra-session items recommendation extended with a Point
Process model for the time-gaps between the sessions. The experimental results
indicate that the proposed model improves recommendations significantly on two
datasets over a strong baseline, while simultaneously improving return-time
predictions over a baseline return-time prediction model.

本文提出了一种基于 Hierarchical RNN 和 Point Process 模型的联合模型，在推荐和用户回归时间预测方面显著提高了推荐质量。