Robert G. Price and Scott D. Goodwin
To save precious time and space, many games and simulations use static terrain and fixed (or random) reconstruction of areas that a player leaves and later revisits. This can result in noticeable differences between the reconstructed area and the player’s recollections (or expectations). These differences can lessen a player’s immersion in the game, or the usefulness of the simulation. We propose an approach for environment reconstruction that uses a Bayesian Network to quickly and easily calculate likely effects that external factors have on the environment. The reconstruction of revisited areas becomes less disconcerting and permits the incorporation of plausible changes based on unobserved, yet reasonably expected, events that could have occurred during the player’s absence.