Understanding the intent of today's enemy necessitates changes in intelligence collection, processing, and dissemination.
Unlike cold war antagonists, today's enemies operate in small, agile, and distributed cells whose tactics do not map well
to established doctrine. This has necessitated a proliferation of advanced sensor and intelligence gathering techniques at
level 0 and level 1 of the Joint Directors of Laboratories fusion model. The challenge is in leveraging modeling and
simulation to transform the vast amounts of level 0 and level 1 data into actionable intelligence at levels 2 and 3 that
include adversarial intent. Currently, warfighters are flooded with information (facts/observables) regarding what the
enemy is presently doing, but provided inadequate explanations of adversarial intent and they cannot simulate 'what-if'
scenarios to increase their predictive situational awareness. The Fused Intent System (FIS) aims to address these
deficiencies by providing an environment that answers 'what' the adversary is doing, 'why' they are doing it, and 'how'
they will react to coalition actions. In this paper, we describe our approach to FIS which includes adversarial 'soft-factors'
such as goals, rationale, and beliefs within a computational model that infers adversarial intent and allows the
insertion of assumptions to be used in conjunction with current battlefield state to perform what-if analysis. Our
approach combines ontological modeling for classification and Bayesian-based abductive reasoning for explanation and
has broad applicability to the operational, training, and commercial gaming domains.