Conversational Sensing

TL;DR

This paper introduces a conversational approach using natural language and controlled natural language for information fusion and situational awareness in security and emergency response scenarios, enabling richer human-machine interactions.

Contribution

It presents a novel conversational protocol leveraging CNL for collaborative sensing, reasoning, and information fusion among humans and machines at network edges.

Findings

Prototype conversational agent successfully gathers CNL info from untrained users.

Supports transformation of eyewitness reports into actionable data.

Enhances situational awareness through semantic fusion and reasoning.

Abstract

Recent developments in sensing technologies, mobile devices and context-aware user interfaces have made it possible to represent information fusion and situational awareness as a conversational process among actors - human and machine agents - at or near the tactical edges of a network. Motivated by use cases in the domain of security, policing and emergency response, this paper presents an approach to information collection, fusion and sense-making based on the use of natural language (NL) and controlled natural language (CNL) to support richer forms of human-machine interaction. The approach uses a conversational protocol to facilitate a flow of collaborative messages from NL to CNL and back again in support of interactions such as: turning eyewitness reports from human observers into actionable information (from both trained and untrained sources); fusing information from humans and physical sensors (with associated quality metadata); and assisting human analysts to make the best use of available sensing assets in an area of interest (governed by management and security policies). CNL is used as a common formal knowledge representation for both machine and human agents to support reasoning, semantic information fusion and generation of rationale for inferences, in ways that remain transparent to human users. Examples are provided of various alternative styles for user feedback, including NL, CNL and graphical feedback. A pilot experiment with human subjects shows that a prototype conversational agent is able to gather usable CNL information from untrained human subjects.