An innovative Steiner tree based approach for polygon partitioning
The Search Space Toolkit (SST) is a suite of tools for investigating the properties of the continuous search spaces which arise in designing complex engineering artifacts whose evaluation requires signiicant computation by a numerical simulator. SST has been developed as part of NDA, a computational environment for (semi-)automated design of jet engine exhaust nozzles for supersonic aircraft which resulted from a collaboration between computer scientists at Rutgers University and design engineers at General Electric and Lockheed. Though the design spaces for this sort of engineering artifact are mainly continuous, they typically include features such as unevaluable points, multiple local optima, and large derivatives which cause diiculties for standard numerical optimization methods. The search spaces which SST explores also diier signiicantly from the discrete search spaces that typically arise in artiicial intelligence research, and properly searching such spaces requires a synergistic combination of numerical methods and AI techniques and is a fundamental AI research area. By promoting the design space to be a rst class entity, rather than a \black box" buried in the interface between an (unconstrained) optimizer and a simulator, SST allows a more principled approach to automated design.