Many aspects of human learning have been proposed as a process of
constructing mental programs: from acquiring symbolic number representations to
intuitive theories about the world. In parallel, there is a long-tradition of
using information processing to model human cognition through Rate Distortion
Theory (RDT). Yet, it is still poorly understood how to apply RDT when mental
representations take the form of programs. In this work, we adapt RDT by
proposing a three way trade-off among rate (description length), distortion
(error), and computational costs (search budget). We use simulations on a
melody task to study the implications of this trade-off, and show that
constructing a shared program library across tasks provides global benefits.
However, this comes at the cost of sensitivity to curricula, which is also
characteristic of human learners. Finally, we use methods from partial
information decomposition to generate training curricula that induce more
effective libraries and better generalization.

我们通过在多个任务之间构建共享的程序库，提出了一种折中方法，其中在描述长度、误差和计算成本之间进行三向权衡，从而适应了心理表示采用编程形式的情况，该方法在仿真的旋律任务中表明共享程序库提供了全局权益，但这是以对教材敏感的代价为代价，这也是人类学习者的特点。最后，我们使用部分信息分解的方法生成能够引起更有效的程序库和更好泛化的训练教材。

与速率失真理论相协调的程序归纳

Harmonizing Program Induction with Rate-Distortion Theory

In presence of multiple objectives to be optimized in Search-Based Software
Engineering (SBSE), Pareto search has been commonly adopted. It searches for a
good approximation of the problem's Pareto optimal solutions, from which the
stakeholders choose the most preferred solution according to their preferences.
However, when clear preferences of the stakeholders (e.g., a set of weights
which reflect relative importance between objectives) are available prior to
the search, weighted search is believed to be the first choice since it
simplifies the search via converting the original multi-objective problem into
a single-objective one and enable the search to focus on what only the
stakeholders are interested in.
This paper questions such a "weighted search first" belief. We show that the
weights can, in fact, be harmful to the search process even in the presence of
clear preferences. Specifically, we conduct a large scale empirical study which
consists of 38 systems/projects from three representative SBSE problems,
together with two types of search budget and nine sets of weights, leading to
604 cases of comparisons. Our key finding is that weighted search reaches a
certain level of solution quality by consuming relatively less resources at the
early stage of the search; however, Pareto search is at the majority of the
time (up to 77% of the cases) significantly better than its weighted
counterpart, as long as we allow a sufficient, but not unrealistic search
budget. This, together with other findings and actionable suggestions in the
paper, allows us to codify pragmatic and comprehensive guidance on choosing
weighted and Pareto search for SBSE under the circumstance that clear
preferences are available. All code and data can be accessed at:
this https URL.

本文探讨了在 Search-Based Software Engineering (SBSE) 中，当清晰的利益相关者偏好给定时，是否应该优先使用加权搜索，结果表明，即使在存在明确偏好的情况下，权重也可能对搜索进程有害。具体来说，作者进行了大规模的实证研究，结果在通常搜索预算的情况下表明，帕累托搜索在绝大多数情况下，甚至达到 77％的情况下比其加权对应物有显着优势。