Answer set programming is a declarative logic programming paradigm geared
towards solving difficult combinatorial search problems. While different logic
programs can encode the same problem, their performance may vary significantly.
It is not always easy to identify which version of the program performs the
best. We present the system Predictor (and its algorithmic backend) for
estimating the grounding size of programs, a metric that can influence a
performance of a system processing a program. We evaluate the impact of
Predictor when used as a guide for rewritings produced by the answer set
programming rewriting tools Projector and Lpopt. The results demonstrate
potential to this approach.

本文研究了答案集编程中如何解决难以计算的组合搜索问题，提出了一个名为 Predictor 的估计程序规模的系统，并探讨了在答案集编程重写工具 Projector 和 Lpopt 中使用该系统的潜力。

System Predictor: 答案集语义下逻辑程序的规模估计器

System Predictor: Grounding Size Estimator for Logic Programs under Answer Set Semantics

Answer set programming is a prominent declarative programming paradigm used
in formulating combinatorial search problems and implementing different
knowledge representation formalisms. Frequently, several related and yet
substantially different answer set programs exist for a given problem.
Sometimes these encodings may display significantly different performance.
Uncovering precise formal links between these programs is often important and
yet far from trivial. This paper presents formal results carefully relating a
number of interesting program rewritings. It also provides the proof of
correctness of system Projector concerned with automatic program rewritings for
the sake of efficiency. Under consideration in Theory and Practice of Logic
Programming (TPLP).

本文介绍了关于 ASP 中程序重写的正式结果，并提供了 Projector 系统的正确性证明，它可以对程序进行自动重写来提高效率。

逻辑程序之间的本质等价论证中的强等价和程序结构

Strong Equivalence and Program Structure in Arguing Essential  Equivalence between Logic Programs

The Algorithm Selection Problem is concerned with selecting the best
algorithm to solve a given problem on a case-by-case basis. It has become
especially relevant in the last decade, as researchers are increasingly
investigating how to identify the most suitable existing algorithm for solving
a problem instead of developing new algorithms. This survey presents an
overview of this work focusing on the contributions made in the area of
combinatorial search problems, where Algorithm Selection techniques have
achieved significant performance improvements. We unify and organise the vast
literature according to criteria that determine Algorithm Selection systems in
practice. The comprehensive classification of approaches identifies and
analyses the different directions from which Algorithm Selection has been
approached. This paper contrasts and compares different methods for solving the
problem as well as ways of using these solutions. It closes by identifying
directions of current and future research.

该论文综述了算法选择问题的研究现状及其在组合搜索问题中的性能提升，归纳了目前算法选择的多种方法并且讨论了使用这些方法的不同方向，最后总结出未来研究的方向。