Agent-based modeling has generated considerable interest in recent years as a tool for exploring many of the processes that can be modeled as bottom up processes. This has accelerated with the availability of software packages, such as Swarm and StarLogo, that allow for relatively complex simulations to be constructed by researchers with limited computer-programming backgrounds. A typical use of agent-based models is to simulate scenarios where large numbers of individuals are inhabiting a landscape, interacting with their landscape and each other by relatively simple rules, and observing the emergent behavior of the system (population) over time. It has been a natural extension in this sort of a study to create a landscape from a “real world” example, typically imported through a geographic information system (GIS). In most cases, the landscape is represented either as a static object, or a “stage” upon which the agents act (see Briggs et al. , Girnblett et al., and Remm). In some cases, an approximation of a dynamic landscape has been added to the simulation in a way that is completely exogenous to the population being simulated; the dynamic conditions are read from historical records, in effect “playing a tape” of conditions, to which the population reacts through time (such as Dean et al. and Kohler et al. ). There has also been many simulations where dynamic landscape processes have been modeled through “bottom up” processes, where localized processes in landscapes are simulated, and the global emergent processes are observed. Topmodel is a Fortran-based implementation of this concept for hydrologic processes; and PCRaster has used similar software constructs to simulate a variety of landscape processes, with sophisticated visualization and data-gathering tools. In both of these examples, the landscape is represented as a regular lattice or cell structure. There are also many examples of “home grown” tools (simulations created for a specific project), applying cellular automata (CA) rules to landscapes to simulate urban growth, wildfire , lava flows, and groundwater flow. There are also examples of how agent-based modeling tools were employed to model dynamic landscape processes such as forest dynamics, i.e., Arborgames. In these models the landscape was the object of the simulation, and free-roaming agents were not considered as part of the model.
A Cognitive Robotic System for a Human-Following Robot
