Mapping Human-Agent Co-Learning and Co-Adaptation: A Scoping Review

TL;DR

This scoping review examines the terminology, types of intelligent agents, task domains, and cognitive frameworks used in human-agent co-learning and co-adaptation research, highlighting the field's diversity and conceptual nuances.

Contribution

It provides a comprehensive overview of existing literature, clarifies terminology inconsistencies, and identifies research gaps in human-agent co-learning and co-adaptation studies.

Findings

Diverse terminology and definitions used across studies

Various intelligent agents and task domains explored

Different cognitive frameworks applied to measure co-learning and co-adaptation

Abstract

Several papers have delved into the challenges of human-AI-robot co-learning and co-adaptation. It has been noted that the terminology used to describe this collaborative relationship in existing studies needs to be more consistent. For example, the prefix "co" is used interchangeably to represent both "collaborative" and "mutual," and the terms "co-learning" and "co-adaptation" are sometimes used interchangeably. However, they can reflect subtle differences in the focus of the studies. The current scoping review's primary research question (RQ1) aims to gather existing papers discussing this collaboration pattern and examine the terms researchers use to describe this human-agent relationship. Given the relative newness of this area of study, we are also keen on exploring the specific types of intelligent agents and task domains that have been considered in existing research (RQ2). This exploration is significant as it can shed light on the diversity of human-agent interactions, from one-time to continuous learning/adaptation scenarios. It can also help us understand the dynamics of human-agent interactions in different task domains, guiding our expectations towards research situated in dynamic, complex domains. Our third objective (RQ3) is to investigate the cognitive theories and frameworks that have been utilized in existing studies to measure human-agent co-learning and co-adaptation. This investigation is crucial as it can help us understand the theoretical underpinnings of human-agent collaboration and adaptation, and it can also guide us in identifying any new frameworks proposed specifically for this type of relationship.