Harmon, Scott J.2012-04-242012-04-242012-04-24http://hdl.handle.net/2097/13635Multiagent systems have been proposed as a way to create reliable, adaptable, and efficient systems. As these systems grow in complexity, configuration, tuning, and design of these systems can become as complex as the problems they claim to solve. As researchers in multiagent systems engineering, we must create the next generation of theories and tools to help tame this growing complexity and take some of the burden off the systems engineer. In this thesis, I propose guidance policies as a way to do just that. I also give a framework for multiagent system design, using the concept of guidance policies to automatically generate a set of constraints based on a set of multiagent system models as well as provide an implementation for generating code that will conform to these constraints. Presenting a formal definition for guidance policies, I show how they can be used in a machine learning context to improve performance of a system and avoid failures. I also give a practical demonstration of converting abstract requirements to concrete system requirements (with respect to a given set of design models).en-USMultiagent SystemsPoliciesArtificial IntelligenceModel CheckingSoftware EngineeringDissertationArtificial Intelligence (0800)Computer Science (0984)Engineering (0537)