Over the last couple of decades, there has been a considerable effort devoted
to the problem of updating logic programs under the stable model semantics
(a.k.a. answer-set programs) or, in other words, the problem of characterising
the result of bringing up-to-date a logic program when the world it describes
changes. Whereas the state-of-the-art approaches are guided by the same basic
intuitions and aspirations as belief updates in the context of classical logic,
they build upon fundamentally different principles and methods, which have
prevented a unifying framework that could embrace both belief and rule updates.
In this paper, we will overview some of the main approaches and results related
to answer-set programming updates, while pointing out some of the main
challenges that research in this topic has faced.

该研究论文概述了与稳定模型语义下逻辑程序更新相关的主要方法和挑战，探讨了在信仰和规则更新方面存在的一些区别。

Answer-Set 程序更新简史

A Brief History of Updates of Answer-Set Programs

We initiate the study of incentive-compatible forecasting competitions in
which multiple forecasters make predictions about one or more events and
compete for a single prize. We have two objectives: (1) to incentivize
forecasters to report truthfully and (2) to award the prize to the most
accurate forecaster. Proper scoring rules incentivize truthful reporting if all
forecasters are paid according to their scores. However, incentives become
distorted if only the best-scoring forecaster wins a prize, since forecasters
can often increase their probability of having the highest score by reporting
more extreme beliefs. In this paper, we introduce two novel forecasting
competition mechanisms. Our first mechanism is incentive compatible and
guaranteed to select the most accurate forecaster with probability higher than
any other forecaster. Moreover, we show that in the standard single-event,
two-forecaster setting and under mild technical conditions, no other
incentive-compatible mechanism selects the most accurate forecaster with higher
probability. Our second mechanism is incentive compatible when forecasters'
beliefs are such that information about one event does not lead to belief
updates on other events, and it selects the best forecaster with probability
approaching 1 as the number of events grows. Our notion of incentive
compatibility is more general than previous definitions of dominant strategy
incentive compatibility in that it allows for reports to be correlated with the
event outcomes. Moreover, our mechanisms are easy to implement and can be
generalized to the related problems of outputting a ranking over forecasters
and hiring a forecaster with high accuracy on future events.

本研究旨在通过引入两种新的预测竞赛机制，提高预测者的准确性，避免预测者为赢得比赛而夸大预测结果，并使预测者的报告与事件结果相对应。