We study a collaborative scenario where a user not only instructs a system to
complete tasks, but also acts alongside it. This allows the user to adapt to
the system abilities by changing their language or deciding to simply
accomplish some tasks themselves, and requires the system to effectively
recover from errors as the user strategically assigns it new goals. We build a
game environment to study this scenario, and learn to map user instructions to
system actions. We introduce a learning approach focused on recovery from
cascading errors between instructions, and modeling methods to explicitly
reason about instructions with multiple goals. We evaluate with a new
evaluation protocol using recorded interactions and online games with human
users, and observe how users adapt to the system abilities.

我们研究了一种协作场景，其中用户不仅指示系统完成任务，而且与系统一起行动。我们通过建立一个游戏环境来研究这个场景，并学习将用户的指令映射到系统的动作，以实现误差恢复和多目标指令的显式推理。我们通过新的评估协议和人类用户的交互和在线游戏进行评估，并观察用户如何适应系统的能力。

执行定位协作交互中的指令

Executing Instructions in Situated Collaborative Interactions

Syntax errors are generally easy to fix for humans, but not for parsers in
general nor LR parsers in particular. Traditional 'panic mode' error recovery,
though easy to implement and applicable to any grammar, often leads to a
cascading chain of errors that drown out the original. More advanced error
recovery techniques suffer less from this problem but have seen little
practical use because their typical performance was seen as poor, their worst
case unbounded, and the repairs they reported arbitrary. In this paper we
introduce the CPCT+ algorithm, and an implementation of that algorithm, that
address these issues. First, CPCT+ reports the complete set of minimum cost
repair sequences for a given location, allowing programmers to select the one
that best fits their intention. Second, on a corpus of 200,000 real-world
syntactically invalid Java programs, CPCT+ is able to repair 98.37% of files
within a timeout of 0.5s. Finally, CPCT+ uses the complete set of minimum cost
repair sequences to reduce the cascading error problem, where incorrect error
recovery causes further spurious syntax errors to be identified. Across the
test corpus, CPCT+ reports 435,812 error locations to the user, reducing the
cascading error problem substantially relative to the 981,628 error locations
reported by panic mode.

本文介绍了一种名为 CPCT+ 的算法来处理语法错误恢复，该算法在一个由 200,000 个 Java 程序组成的语法无效的语料库中，能够修复 98.37% 的文件，在减少哀号模式汇报的级联错误问题的同时，它直接报告给程序员一个特定的动作进行修复，适用于大多数语法，并简化最小平均成本修复序列的计算。