Towards human-agent knowledge fusion (HAKF) in support of distributed coalition teams

TL;DR

This paper explores the initial development of human-agent knowledge fusion (HAKF) to enhance trust and collaboration in distributed coalition teams by enabling transparent, adaptive, and context-aware interactions between humans and machine agents.

Contribution

It introduces the concept of HAKF, outlining key requirements and demonstrating its potential to improve human-machine teaming in complex, uncertain environments.

Findings

Identified key requirements for human-agent knowledge fusion.

Proposed a framework for transparent and adaptive agent behavior.

Showed potential benefits in complex event processing scenarios.

Abstract

Future coalition operations can be substantially augmented through agile teaming between human and machine agents, but in a coalition context these agents may be unfamiliar to the human users and expected to operate in a broad set of scenarios rather than being narrowly defined for particular purposes. In such a setting it is essential that the human agents can rapidly build trust in the machine agents through appropriate transparency of their behaviour, e.g., through explanations. The human agents are also able to bring their local knowledge to the team, observing the situation unfolding and deciding which key information should be communicated to the machine agents to enable them to better account for the particular environment. In this paper we describe the initial steps towards this human-agent knowledge fusion (HAKF) environment through a recap of the key requirements, and an explanation of how these can be fulfilled for an example situation. We show how HAKF has the potential to bring value to both human and machine agents working as part of a distributed coalition team in a complex event processing setting with uncertain sources.