Interactive Visual Reasoning under Uncertainty
Manjie Xu, Guangyuan Jiang, Wei Liang, Chi Zhang, Yixin Zhu

TL;DR
This paper introduces IVRE, an interactive environment for testing artificial agents' reasoning under uncertainty, revealing current AI limitations compared to human reasoning in ambiguous scenarios.
Contribution
The paper presents IVRE, a novel environment for evaluating and understanding artificial agents' ability to reason and experiment under uncertain, ambiguous conditions.
Findings
Modern AI agents underperform humans in IVRE scenarios.
Current learning methods struggle with interactive reasoning under uncertainty.
IVRE highlights the need for advanced reasoning capabilities in AI.
Abstract
One of the fundamental cognitive abilities of humans is to quickly resolve uncertainty by generating hypotheses and testing them via active trials. Encountering a novel phenomenon accompanied by ambiguous cause-effect relationships, humans make hypotheses against data, conduct inferences from observation, test their theory via experimentation, and correct the proposition if inconsistency arises. These iterative processes persist until the underlying mechanism becomes clear. In this work, we devise the IVRE (pronounced as "ivory") environment for evaluating artificial agents' reasoning ability under uncertainty. IVRE is an interactive environment featuring rich scenarios centered around Blicket detection. Agents in IVRE are placed into environments with various ambiguous action-effect pairs and asked to determine each object's role. They are encouraged to propose effective and efficient…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
Taxonomy
TopicsAI-based Problem Solving and Planning · Explainable Artificial Intelligence (XAI) · Cognitive Science and Mapping
