Intelligent Agents in Virtual Worlds

Abstract

Thanks to the advances in graphical hardware in the last years, virtual worlds are becoming increasingly popular. Moreover, there exist now standard formats for describing 3D scenes on the web and there is a number of development tools available such as, in the computer games community, editors to design new maps and agent behaviors for popular 3D action games such Unreal, Half-life, or Quake, not to mention general SDKs such as Discreet’s Gmax or Intrinsic’s Alchemy. Nevertheless, it is difficult to “populate” such worlds with virtual agents representing life-like creatures which could autonomously navigate and react to their changing environment, and also possibly interact with users. Pathplanning problems are thus a key issue in the so-called “Game AI” domain [4], and have been tackled up to now with traditional AI techniques such as the classical A* algorithm or more modern extensions, heavily relying on the complete knowledge of the complete virtual environment to define an optimal trajectory for computer-controlled characters. Such classical path-finding is however usually limited to a single goal point and basically amount to compute an optimal or near-optimal trajectory avoiding collision with known obstacles. We will see however that pathfinding can be rephrased as an optimization problem and that constraint-based techniques can be applied to give a reactive anytime algorithm that produce life-like, if not optimal, behaviors for virtual creatures [1]. For the constraint solving aspects, we use a method called Adaptive Search which is based on local search techniques applied to CSPs, inspired by methods such as GSAT and Tabu Search [2]. We are also interested in this work in more complex path-planning, where several goals have to be satisfied by the autonomous agent, which has thus to pass through several destination points in the virtual environment, and we call this problem multi-goal path-finding. This problem appears in a virtual environment or 3D game when, for instance, the user ask some helper agent to go and pick several items and to meet him at some further point. This could also be the case for an autonomous creature who has to “survive” in an unknown environment and for instance look for food but also for water, and so on so forth. In this case also considering multi-goal path-planning as an optimization problem (an instance of the well-known Traveling Salesman Problem) amounts to design new algorithms based on local search techniques, which are also reactive and exhibit opportunistic behaviors for artificial creatures [3].