The modelling of dynamical systems from discrete observations is a challenge
faced by modern scientific and engineering data systems. Hamiltonian systems
are one such fundamental and ubiquitous class of dynamical systems. Hamiltonian
neural networks are state-of-the-art models that unsupervised-ly regress the
Hamiltonian of a dynamical system from discrete observations of its vector
field under the learning bias of Hamilton's equations. Yet Hamiltonian dynamics
are often complicated, especially in higher dimensions where the state space of
the Hamiltonian system is large relative to the number of samples. A recently
discovered remedy to alleviate the complexity between state variables in the
state space is to leverage the additive separability of the Hamiltonian system
and embed that additive separability into the Hamiltonian neural network.
Following the nomenclature of physics-informed machine learning, we propose
three separable Hamiltonian neural networks. These models embed additive
separability within Hamiltonian neural networks. The first model uses additive
separability to quadratically scale the amount of data for training Hamiltonian
neural networks. The second model embeds additive separability within the loss
function of the Hamiltonian neural network. The third model embeds additive
separability through the architecture of the Hamiltonian neural network using
conjoined multilayer perceptions. We empirically compare the three models
against state-of-the-art Hamiltonian neural networks, and demonstrate that the
separable Hamiltonian neural networks, which alleviate complexity between the
state variables, are more effective at regressing the Hamiltonian and its
vector field.

建议了三种具备可加性可分性的 Hamilton 神经网络模型，通过减少状态变量之间的复杂性，更有效地回归 Hamilton 的向量场。

分离型哈密尔顿神经网络

Separable Hamiltonian Neural Networks

In the timeline-based approach to planning, originally born in the space
sector, the evolution over time of a set of state variables (the timelines) is
governed by a set of temporal constraints. Traditional timeline-based planning
systems excel at the integration of planning with execution by handling
temporal uncertainty. In order to handle general nondeterminism as well, the
concept of timeline-based games has been recently introduced. It has been
proved that finding whether a winning strategy exists for such games is
2EXPTIME-complete. However, a concrete approach to synthesize controllers
implementing such strategies is missing. This paper fills this gap, outlining
an approach to controller synthesis for timeline-based games.

这篇论文介绍了一种基于时间轴的规划方法，其演变受一组时间约束条件控制。为了处理一般的非确定性，提出了基于时间轴的游戏的概念，并提出了一种控制器综合方法来支持时间轴游戏中的胜利策略实现。

面向时间轴游戏的控制器合成

Controller Synthesis for Timeline-based Games

The difficulty of deterministic planning increases exponentially with
search-tree depth. Black-box planning presents an even greater challenge, since
planners must operate without an explicit model of the domain. Heuristics can
make search more efficient, but goal-aware heuristics for black-box planning
usually rely on goal counting, which is often quite uninformative. In this
work, we show how to overcome this limitation by discovering macro-actions that
make the goal-count heuristic more accurate. Our approach searches for
macro-actions with focused effects (i.e. macros that modify only a small number
of state variables), which align well with the assumptions made by the
goal-count heuristic. Focused macros dramatically improve black-box planning
efficiency across a wide range of planning domains, sometimes beating even
state-of-the-art planners with access to a full domain model.

本文介绍了一种通过发现具有聚焦效应的宏操作，使目标计数启发式方法更准确的方法，大大提高了黑盒计划的效率。