Collaboration is an integral part of human dialogue. Typical task-oriented
dialogue games assign asymmetric roles to the participants, which limits their
ability to elicit naturalistic role-taking in collaboration and its
negotiation. We present a novel and simple online setup that favors balanced
collaboration: a two-player 2D object placement game in which the players must
negotiate the goal state themselves. We show empirically that human players
exhibit a variety of role distributions, and that balanced collaboration
improves task performance. We also present an LLM-based baseline agent which
demonstrates that automatic playing of our game is an interesting challenge for
artificial systems.

合作是人类对话的重要组成部分。本研究介绍了一种在线的二人 2D 物体放置游戏，促进均衡的合作，通过协商目标状态实现自然的角色扮演和任务表现的提升。同时，引入基于 LLM 的基准代理程序，探讨了人工系统面临的挑战。

引发平衡协作的对话游戏

A Dialogue Game for Eliciting Balanced Collaboration

Time is a crucial factor in modelling dynamic behaviours of intelligent
agents: activities have a determined temporal duration in a real-world
environment, and previous actions influence agents' behaviour. In this paper,
we propose a language for modelling concurrent interaction between agents that
also allows the specification of temporal intervals in which particular actions
occur. Such a language exploits a timed version of Abstract Argumentation
Frameworks to realise a shared memory used by the agents to communicate and
reason on the acceptability of their beliefs with respect to a given time
interval. An interleaving model on a single processor is used for basic
computation steps, with maximum parallelism for time elapsing. Following this
approach, only one of the enabled agents is executed at each moment. To
demonstrate the capabilities of language, we also show how it can be used to
model interactions such as debates and dialogue games taking place between
intelligent agents. Lastly, we present an implementation of the language that
can be accessed via a web interface. Under consideration in Theory and Practice
of Logic Programming (TPLP).

本文介绍一种语言，可用于建模智能体之间的并发交互，并允许指定特定操作发生的时间间隔，以及演示了如何使用该语言建模辩论和对话游戏。

基于时间并发语言的交错语义用于建模辩论和对话游戏

An Interleaving Semantics of the Timed Concurrent Language for  Argumentation to Model Debates and Dialogue Games

How does one measure "ability to understand language"? If it is a person's
ability that is being measured, this is a question that almost never poses
itself in an unqualified manner: Whatever formal test is applied, it takes
place on the background of the person's language use in daily social practice,
and what is measured is a specialised variety of language understanding (e.g.,
of a second language; or of written, technical language). Computer programs do
not have this background. What does that mean for the applicability of formal
tests of language understanding? I argue that such tests need to be
complemented with tests of language use embedded in a practice, to arrive at a
more comprehensive evaluation of "artificial language understanding". To do
such tests systematically, I propose to use "Dialogue Games" -- constructed
activities that provide a situational embedding for language use. I describe a
taxonomy of Dialogue Game types, linked to a model of underlying capabilites
that are tested, and thereby giving an argument for the \emph{construct
validity} of the test. I close with showing how the internal structure of the
taxonomy suggests an ordering from more specialised to more general situational
language understanding, which potentially can provide some strategic guidance
for development in this field.

论文提出了一种使用 “对话游戏” 来测试人工语言理解能力的方法，并借由建立一种对话游戏类型的分类来探讨测试的结构效度，以达到更全面的评估。

基于对话游戏的语言理解测试：动机、分类和策略

Dialogue Games for Benchmarking Language Understanding: Motivation,  Taxonomy, Strategy

In visual instruction-following dialogue games, players can engage in repair
mechanisms in face of an ambiguous or underspecified instruction that cannot be
fully mapped to actions in the world. In this work, we annotate Instruction
Clarification Requests (iCRs) in CoDraw, an existing dataset of interactions in
a multimodal collaborative dialogue game. We show that it contains lexically
and semantically diverse iCRs being produced self-motivatedly by players
deciding to clarify in order to solve the task successfully. With 8.8k iCRs
found in 9.9k dialogues, CoDraw-iCR (v1) is a large spontaneous iCR corpus,
making it a valuable resource for data-driven research on clarification in
dialogue. We then formalise and provide baseline models for two tasks:
Determining when to make an iCR and how to recognise them, in order to
investigate to what extent these tasks are learnable from data.

本文提出了一个新方法，即在多模态协作对话游戏中注释说明澄清请求，并使用此数据集为基础，展开了数据驱动的说明研究，探索了何时提出说明澄清请求以及如何互相认识它们的能力。