Work

Open issues in open world learning

Year: 2025

Type: article

Abstract: Meaningful progress has been made in open world learning (OWL), enhancing the ability of agents to detect, characterize, and incrementally learn novelty in dynamic environments. However, novelty remains a persistent challenge for agents relying on state‐of‐the‐art learning algorithms. This article considers the current state of OWL, drawing on insights from a recent DARPA research program on this topic. We identify open issues that impede further advancements spanning theory, design, and evaluation. In particular, we emphasize the challenges posed by dynamic scenarios that are crucial to understand for ensuring the viability of agents designed for real‐world environments. The article provides suggestions for setting a new research agenda that effectively addresses these open issues.

Source: AI Magazine

DOI: 10.1002/aaai.70001

Authors: Steve Cruz, Katarina Doctor, Christopher Funk, Walter Scheirer

Institutions: University of Notre Dame, United States Naval Research Laboratory, Kitware (United States)

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,400

Funders: Defense Advanced Research Projects Agency, Advanced Research Projects Agency, Army Research Office