Spatially-explicit autonomous agents for modelling recreation use in complex wilderness landscapes

Increasing pressures by recreators on public lands has forced U.S. federal agencies such as the Forest Service to re-evaluate forest management activities and adopt new practices for assessing recreation use Because natural ecosystems, including the human dimension of such systems, are extremely complex, integrated systems, models capable of linking and spanning multiple production processes, and geographic and temporal scales, are needed to support forest management decisions. The goal of the research reported in this paper is to develop a new form of intelligent decision support and simulation system (IDSS) which uses autonomous agents to assist natural resource managers in assessing and managing dynamic recreation behavior, social interactions and resulting conflicts in wilderness settings. We present a framework for modeling recreation conflicts between recreation use groups in the redrock country of Arizona, U.S.A. The power of GIS is utilised for accurately representing complex dynamic landscapes, and together with advanced vision detection and assessment capabilties, is used for simulating conflicting recreational activities in these landscapes. We describe linkages between the dMARS Distributed Multi-agent Reasoning System, the Swarm Multi-agent Simulation System and a GIS system to develop goal-oriented autonomous recreation agents capable of reasoning and decision-making in their quest to seek and derive satisfactory recreational experiences, while minimising recreational conflicts in crowded backcountry environments. We briefly describe each of the components of the framework and outline our approach for calibrating our agents against empirical recreation data collected in Sedona, Arizona for this study.