OptAgent: A Generalized Framework for the Optimization and Analysis of Agent-based Models

This project seeks to create OptAgent, an integrated software environment built on the existing foundation of OptQuest®, a metaheuristic-based optimization engine, to provide a generalized framework for the analysis and optimization of agent-based models by combining optimization algorithms, agent-based simulation, and distributed processing. The proposed research will develop additions and extensions of the core OptQuest engine to provide a tool tailored specifically to work with agent-based models. The OptAgent software will function in three stages. In the first stage a diverse set of solutions will be created and a Markov blanket-based feature selection methodology will identify the most influential variables. In the second stage the agent-based model optimization search process will be performed. This process will be enhanced by classification and discrimination rules developed from the initial solution set using satisfiability data mining. In the final stage the best combination of agent and environment parameters will be presented along with statistics providing insights into the behavior of the model across the myriad possible combinations of inputs. During Phase I the proposed research areas will be investigated and the most promising paths to implementation identified. Proof of concept software will be developed demonstrating the feasibility of the OptAgent application.