Active Reasoning in an Open-World Environment
Manjie Xu Guangyuan Jiang Wei Liang Chi Zhang Yixin Zhu
Recent advances in vision-language learning have achieved notable success on
complete-information question-answering datasets through the integration of
extensive world knowledge. Yet, most models operate passively, responding to
questions based on pre-stored knowledge. In stark contrast, humans possess the
ability to actively explore, accumulate, and reason using both newfound and
existing information to tackle incomplete-information questions. In response to
this gap, we introduce $Conan$, an interactive open-world environment devised
for the assessment of active reasoning. $Conan$ facilitates active exploration
and promotes multi-round abductive inference, reminiscent of rich, open-world
settings like Minecraft. Diverging from previous works that lean primarily on
single-round deduction via instruction following, $Conan$ compels agents to
actively interact with their surroundings, amalgamating new evidence with prior
knowledge to elucidate events from incomplete observations. Our analysis on
$Conan$ underscores the shortcomings of contemporary state-of-the-art models in
active exploration and understanding complex scenarios. Additionally, we
explore Abduction from Deduction, where agents harness Bayesian rules to recast
the challenge of abduction as a deductive process. Through $Conan$, we aim to
galvanize advancements in active reasoning and set the stage for the next
generation of artificial intelligence agents adept at dynamically engaging in
environments.