Artificial intelligence and simulation
The use of simulation gaming in education, health, training, public policy, social change, and various technical and social disciplines is aiding decision makers and trainees to gain experience by acting on realistic dynamic scenarios. The number of nonentertainment games under development is rapidly increasing. For instance, the recent Serious Games initiative relies on using such simulation gaming environments to explore management and leadership challenges facing the public sector. The appreciation for the ideas, skills, technologies, and techniques used in commercial entertainment games is at an all time high. Many commercial games are already in use for purposes other than entertainment. Titles such as SIM CITY, CIVILIZATION HIDDEN AGENDA, and others have been used as learning tools in schools and universities across the globe. The need for increased level of reality and fidelity in such domain specific games calls for the use of advanced computational technology such as high resolution graphics, distributed and powerful gaming engines, and methods that bring realism and intelligence to actors and scenarios. Nowadays, agent directed simulation modeling is very popular for representing complex social phenomena. The premise of the agent paradigm, its related theory and methodologies, together with advances in multilevel modeling of complex systems of interactions, are opening new frontiers for advancing the physical, natural, social, military, and information sciences and engineering. Recent trends have made it clear that simulation model complexity will continue to increase dramatically in the coming decades. The dynamic and distributed nature of simulation gaming applications the significance of exploratory analysis of complex scientific phenomena, and the need for modeling the micro level interactions, collaboration, and cooperation among real world entities is bringing a shift in the way that simulation games are being conceptualized. Using intelligent agents in simulation models is based on the idea that it is possible to represent the behavior of active entities in the world in terms of the interactions of an assembly of agents with their own operational autonomy. The possibility to model complex situations whose overall structures emerge from interactions among individual entities and to cause structures on the macro level to emerge from the models at the micro level is making agent paradigm a critical enabler in model ing and simulation of complex adaptive systems.