For some problems, humans may not be able to accurately judge the goodness of
AI-proposed solutions. Irving et al. (2018) propose that in such cases, we may
use a debate between two AI systems to amplify the problem-solving capabilities
of a human judge. We introduce a mathematical framework that can model debates
of this type and propose that the quality of debate designs should be measured
by the accuracy of the most persuasive answer. We describe a simple instance of
the debate framework called feature debate and analyze the degree to which such
debates track the truth. We argue that despite being very simple, feature
debates nonetheless capture many aspects of practical debates such as the
incentives to confuse the judge or stall to prevent losing. We then outline how
these models should be generalized to analyze a wider range of debate
phenomena.

研究人员提出在 AI 解决方案无法得到准确判断的问题中，可通过两个 AI 系统之间的辩论来增强人类法官的问题解决能力，并介绍了一个可建模此类辩论的数学框架，并将辩论设计的质量测量为最有力回答的准确性。

人工智能辩论中真相何时受青睐？

(When) Is Truth-telling Favored in AI Debate?

The NeuroSAT neural network architecture was recently introduced for
predicting properties of propositional formulae. When trained to predict the
satisfiability of toy problems, it was shown to find solutions and
unsatisfiable cores on its own. However, the authors saw "no obvious path" to
using the architecture to improve the state-of-the-art. In this work, we train
a simplified NeuroSAT architecture to directly predict the unsatisfiable cores
of real problems. We modify several high-performance SAT solvers to
periodically replace their variable activity scores with NeuroSAT's prediction
of how likely the variables are to appear in an unsatisfiable core. The
modified MiniSat solves 10% more problems on SAT-COMP 2018 within the standard
5,000 second timeout than the original does. The modified Glucose solves 11%
more problems than the original, while the modified Z3 solves 6% more. The
gains are even greater when the training is specialized for a specific
distribution of problems; on a benchmark of hard problems from a scheduling
domain, the modified Glucose solves 20% more problems than the original does
within a one-hour timeout. Our results demonstrate that NeuroSAT can provide
effective guidance to high-performance SAT solvers on real problems.

本文介绍了使用改进的 NeuroSAT 架构，通过训练简化的神经网络来直接预测实际问题的不可满足核，以提供有效的指导高性能 SAT 求解器在解决特定问题分布时的问题上的应用。

利用不可满足核心预测指导高性能 SAT 求解器

Guiding High-Performance SAT Solvers with Unsat-Core Predictions

Artificial Intelligence (AI) approaches to problem-solving and
decision-making are becoming more and more complex, leading to a decrease in
the understandability of solutions. The European Union's new General Data
Protection Regulation tries to tackle this problem by stipulating a "right to
explanation" for decisions made by AI systems. One of the AI paradigms that may
be affected by this new regulation is Answer Set Programming (ASP). Thanks to
the emergence of efficient solvers, ASP has recently been used for
problem-solving in a variety of domains, including medicine, cryptography, and
biology. To ensure the successful application of ASP as a problem-solving
paradigm in the future, explanations of ASP solutions are crucial. In this
survey, we give an overview of approaches that provide an answer to the
question of why an answer set is a solution to a given problem, notably
off-line justifications, causal graphs, argumentative explanations and why-not
provenance, and highlight their similarities and differences. Moreover, we
review methods explaining why a set of literals is not an answer set or why no
solution exists at all.

该论文为对 ASP 的解释性研究做出了概述。主要关注 ASP 的不同解释方式及其异同，并探讨其在未来的成功应用中的重要性。

回答应答集规划中的 “为什么” - 解释方法综述

Answering the "why" in Answer Set Programming - A Survey of Explanation  Approaches

D-Wave only guarantees to support coefficients with 4 to 5 bits of resolution
or precision. This paper describes a method to extend the functionality of the
D-Wave to solve problems that require the support of higher precision
coefficients.

该论文描述了一种扩展 D-Wave 的功能，以解决需要高精度系数支持的问题。

扩展 D-Wave 以支持更高精度系数

Extending the D-Wave with support for Higher Precision Coefficients

We present a model for compositional distributional semantics related to the
framework of Coecke et al. (2010), and emulating formal semantics by
representing functions as tensors and arguments as vectors. We introduce a new
learning method for tensors, generalising the approach of Baroni and Zamparelli
(2010). We evaluate it on two benchmark data sets, and find it to outperform
existing leading methods. We argue in our analysis that the nature of this
learning method also renders it suitable for solving more subtle problems
compositional distributional models might face.

介绍一种基于张量及向量的学习方法，并在数据集上进行了评估，表现超过了现有的先进方法，同时也适用于解决组合分布式模型可能遇到的更微妙问题。