Example-driven Diagrammatic Tools for Rapid Knowledge Acquisition

The last ten years has seen a revolution in the complexity and realism of human behavior models (HBMs). However, the cost of developing realistic HBMs continues to increase as much of the detailed and complex knowledge must be manually encoded to produce realistic behavior. The focus of this project is on reducing the cost of acquiring, validating and maintaining the knowledge used in realistic HBMs. Our approach is to develop tools that allow subject matter experts (SMEs) to visually specify behavior using abstract scenarios represented as diagrams. The SME can graphically describe the conditions under which actions and goals should be pursued, together with the associated reasons for those decisions. The system, guided by the expert’s choices, analyzes and automatically generalizes from the example scenarios, alerting the SME to inconsistencies and missing knowledge. The system incrementally generates an executable HBM whose behavior the SME can view and modify during development. By moving the language of discourse from symbolic programming languages to annotated diagrams, the SMEs specify knowledge directly without requiring the intervention of a knowledge engineer to translate between the representations.