We describe a class of tasks called dialogue decision problems, in which AI
assistants must collaborate with one or more humans via natural language to
help them make complex decisions. We formalize three domains in which users
face everyday decisions: (1) choosing an assignment of reviewers to conference
papers, (2) planning a multi-step itinerary in a city, and (3) negotiating
travel plans for a group of friends. In each of these settings, AI assistants
and users have disparate abilities that they must combine to arrive at the best
decision: assistants can access and process large amounts of information, while
users have preferences and constraints external to the system. For each task,
we build a dialogue environment where agents receive a reward based on the
quality of the final decision they reach. Using these environments, we collect
human-human dialogues with humans playing the role of assistant. To compare how
current AI assistants communicate in these settings, we present baselines using
large language models in self-play. Finally, we highlight a number of
challenges models face in decision-oriented dialogues, ranging from efficient
communication to reasoning and optimization, and release our environments as a
testbed for future modeling work.

研究了对话决策问题的一个类别，其中 AI 助手与一个或多个人通过自然语言协作，帮助他们做出复杂的决策。为不同的任务建立了对话环境，并使用这些环境收集了人与人的对话作为基线，同时提出了模型在决策导向的对话中面临着一些挑战，并释放了环境作为未来建模工作的测试基地。