Shared Model of Sense-making for Human-Machine Collaboration
  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).
