Powerful domain-independent planners have been developed to solve various
types of planning problems. These planners often require a model of the acting
agent's actions, given in some planning domain description language. Manually
designing such an action model is a notoriously challenging task. An
alternative is to automatically learn action models from observation. Such an
action model is called safe if every plan created with it is consistent with
the real, unknown action model. Algorithms for learning such safe action models
exist, yet they cannot handle domains with conditional or universal effects,
which are common constructs in many planning problems. We prove that learning
non-trivial safe action models with conditional effects may require an
exponential number of samples. Then, we identify reasonable assumptions under
which such learning is tractable and propose SAM Learning of Conditional
Effects (Conditional-SAM), the first algorithm capable of doing so. We analyze
Conditional-SAM theoretically and evaluate it experimentally. Our results show
that the action models learned by Conditional-SAM can be used to solve
perfectly most of the test set problems in most of the experimented domains.

通过学习条件效应，我们提出了能够解决规划问题的第一个算法 ——SAM Learning of Conditional Effects (Conditional-SAM)。实验结果表明，Conditional-SAM 学习到的行动模型可以在大部分实验领域中解决测试集问题。

带有条件效果的 PDDL 领域的安全学习 -- 扩展版本

Safe Learning of PDDL Domains with Conditional Effects -- Extended  Version

Social science theories often postulate causal relationships among a set of
variables or events. Although directed acyclic graphs (DAGs) are increasingly
used to represent these theories, their full potential has not yet been
realized in practice. As non-parametric causal models, DAGs require no
assumptions about the functional form of the hypothesized relationships.
Nevertheless, to simplify the task of empirical evaluation, researchers tend to
invoke such assumptions anyway, even though they are typically arbitrary and do
not reflect any theoretical content or prior knowledge. Moreover, functional
form assumptions can engender bias, whenever they fail to accurately capture
the complexity of the causal system under investigation. In this article, we
introduce causal-graphical normalizing flows (cGNFs), a novel approach to
causal inference that leverages deep neural networks to empirically evaluate
theories represented as DAGs. Unlike conventional approaches, cGNFs model the
full joint distribution of the data according to a DAG supplied by the analyst,
without relying on stringent assumptions about functional form. In this way,
the method allows for flexible, semi-parametric estimation of any causal
estimand that can be identified from the DAG, including total effects,
conditional effects, direct and indirect effects, and path-specific effects. We
illustrate the method with a reanalysis of Blau and Duncan's (1967) model of
status attainment and Zhou's (2019) model of conditional versus controlled
mobility. To facilitate adoption, we provide open-source software together with
a series of online tutorials for implementing cGNFs. The article concludes with
a discussion of current limitations and directions for future development.

引入因果图归一化流（causal-graphical normalizing flows, cGNFs）这一新方法，该方法利用深度神经网络对以有向无环图（directed acyclic graphs, DAGs）表示的理论进行经验评估，模型所显示的数据的完整联合分布，而无需对函数形式进行严格的假设，从而可以对由 DAGs 确定的任何因果估计量进行灵活的半参数估计，包括总效应、条件效应、直接和间接效应以及路径特定效应。

DAG 中的深度学习

Deep Learning With DAGs

The recent approaches of extending the GRAPHPLAN algorithm to handle more
expressive planning formalisms raise the question of what the formal meaning of
"expressive power" is. We formalize the intuition that expressive power is a
measure of how concisely planning domains and plans can be expressed in a
particular formalism by introducing the notion of "compilation schemes" between
planning formalisms. Using this notion, we analyze the expressiveness of a
large family of propositional planning formalisms, ranging from basic STRIPS to
a formalism with conditional effects, partial state specifications, and
propositional formulae in the preconditions. One of the results is that
conditional effects cannot be compiled away if plan size should grow only
linearly but can be compiled away if we allow for polynomial growth of the
resulting plans. This result confirms that the recently proposed extensions to
the GRAPHPLAN algorithm concerning conditional effects are optimal with respect
to the "compilability" framework. Another result is that general propositional
formulae cannot be compiled into conditional effects if the plan size should be
preserved linearly. This implies that allowing general propositional formulae
in preconditions and effect conditions adds another level of difficulty in
generating a plan.

本论文介绍了一种名为 “编译方案” 的概念，并使用这种概念分析了一大类命题规划命题形式的表达能力，结果确认了 GRAPHPLAN 算法中有关条件效应的扩展是优化的，同时发现允许在先决条件和效果条件中使用一般命题公式会给生成计划带来额外的 Schwierigkeit。