Great storytellers know how to take us on a journey. They direct characters
to act -- not necessarily in the most rational way -- but rather in a way that
leads to interesting situations, and ultimately creates an impactful experience
for audience members looking on.
If audience experience is what matters most, then can we help artists and
animators *directly* craft such experiences, independent of the concrete
character actions needed to evoke those experiences? In this paper, we offer a
novel computational framework for such tools. Our key idea is to optimize
animations with respect to *simulated* audience members' experiences. To
simulate the audience, we borrow an established principle from cognitive
science: that human social intuition can be modeled as "inverse planning," the
task of inferring an agent's (hidden) goals from its (observed) actions.
Building on this model, we treat storytelling as "*inverse* inverse planning,"
the task of choosing actions to manipulate an inverse planner's inferences. Our
framework is grounded in literary theory, naturally capturing many storytelling
elements from first principles. We give a series of examples to demonstrate
this, with supporting evidence from human subject studies.

该论文提供了一个基于计算框架的颠覆性方法，帮助艺术家和动画师通过模拟观众体验来优化动画效果，旨在通过逆向规划的方式实现故事情节的优化。

反向反向规划

Acting as Inverse Inverse Planning

The version 3.0 of the DELPHES fast-simulation is presented. The goal of
DELPHES is to allow the simulation of a multipurpose detector for
phenomenological studies. The simulation includes a track propagation system
embedded in a magnetic field, electromagnetic and hadron calorimeters, and a
muon identification system. Physics objects that can be used for data analysis
are then reconstructed from the simulated detector response. These include
tracks and calorimeter deposits and high level objects such as isolated
electrons, jets, taus, and missing energy. The new modular approach allows for
greater flexibility in the design of the simulation and reconstruction
sequence. New features such as the particle-flow reconstruction approach,
crucial in the first years of the LHC, and pile-up simulation and mitigation,
which is needed for the simulation of the LHC detectors in the near future,
have also been implemented. The DELPHES framework is not meant to be used for
advanced detector studies, for which more accurate tools are needed. Although
some aspects of DELPHES are hadron collider specific, it is flexible enough to
be adapted to the needs of electron-positron collider experiments.

DELPHES 3.0 是一个快速模拟工具，具有模拟探测器响应的能力，旨在为各种表观研究提供多功能探测器的模拟。可以从模拟探测器响应中重建各种物理对象，包括跟踪、块状量以及高级对象，如孤立电子、喷注、tau 子和缺失能量。