Mobile Agents with Spatial Intelligence

Abstract

Recreation behavior simulation (RBSim) is a computer program that simulates the behavior of human recreators in high-use natural environments. Specifically RBSim uses concepts from recreation research and artificial intelligence (AI) and combines them with geographic information systems (GIS) to produce an integrated system for exploring the interactions between different recreation user groups within geographic space. RBSim joins two computer technologies: • Geographic information systems to represent the environment, and • Autonomous agents to simulate human behavior within geographic space. RBSim demonstrates the potential of combining the two technologies to explore the complex interactions between humans and the environment. The implications of this technology should also be applicable to the study of wildlife populations and other systems where there are complex interactions in the environment. RBSim uses autonomous agents to simulate recreator behavior. An autonomous agent is a computer simulation that is based on concepts from artificial life research. Agent simulations are built using object-oriented programming technology. The agents are autonomous because, once they are programmed, they can move about the landscape like software robots. The agents can gather data from their environment, make decisions from this information, and change their behavior according to the situation in which they find themselves. Each individual agent has its own physical mobility, sensory, and cognitive capabilities. This results in actions that echo the behavior of real animals (in this case, humans) in the environment. The process of building an agent is iterative and combines knowledge derived from empirical data with the intuition of the programmer. By continuing to program knowledge and rules into the agent, watching the behavior resulting from these rules, and comparing it to what is known about actual behavior, a rich and complex set of behaviors emerge. What is compelling about this type of simulation is that it is impossible to predict the behavior of any single agent in the simulation and, by observing the interactions between agents, it is possible to draw conclusions that are impossible using any other analytical process. RBSim is important because, until now, there have been no tools for recreation managers and researchers to systematically investigate different recreation management options.