Shared Model of Sense-making for Human-Machine Collaboration

TL;DR

This paper introduces a general sense-making model to enhance human-machine collaboration, enabling analysts to instruct agents in complex, domain-specific situations through hypothesis generation, evidence discovery, and testing.

Contribution

It proposes a novel, evidence-based sense-making framework that improves agent understanding and collaboration in security-related scenarios.

Findings

Model facilitates understanding of complex situations.

Agents improve competence through iterative hypothesis testing.

Supports direct human instruction in domain-specific contexts.

Abstract

We present a model of sense-making that greatly facilitates the collaboration between an intelligent analyst and a knowledge-based agent. It is a general model grounded in the science of evidence and the scientific method of hypothesis generation and testing, where sense-making hypotheses that explain an observation are generated, relevant evidence is then discovered, and the hypotheses are tested based on the discovered evidence. We illustrate how the model enables an analyst to directly instruct the agent to understand situations involving the possible production of weapons (e.g., chemical warfare agents) and how the agent becomes increasingly more competent in understanding other situations from that domain (e.g., possible production of centrifuge-enriched uranium or of stealth fighter aircraft).