The Complexity of Testing a Motivational Model of Action Selection for Virtual Humans Etienne de Sevin and Daniel Thalmann EPFL Computer Graphics Lab – LIG CH-1015 – Lausanne – Switzerland {etienne.desevin, daniel.thalmann}@ epfl.ch Abstract After implementing a motivational model of action selection applied to autonomous virtual humans inspired by models of animals’ decision-making, the problem consists of testing it in good conditions for validation. Indeed this model has to respect the six criteria that we define according to Tyrell’s requirements for designing a mechanism of action selection. So we use the real-time framework VHD++ for advanced virtual human simulation and we define a simulated environment with many conflicting motivations. Finally a video shows the first results where the virtual human’s decision-making follows the six criteria and so validates our model in the simulated environment. Keywords. Behavioral Animation, Virtual Humans, Action selection and Motivations. 1. Introduction Designing autonomous agents has been one of the major concerns in several fields of research, especially in computer graphics. To produce complex animations with minimal input from the animator, each autonomous agent must repeatedly solve the problem of action selection. The action selection problem is still discussed in ethology, and more widely in cognitive sciences, because of his multi-disciplinary approach. Indeed, among mutually conflicting actions, an animal can only perform one at a time. The difficulty resides in knowing how to choose “at each moment in time, the most appropriate action, out of a repertoire of possible actions”, and “how to apportion one’s available time so as to simultaneously satisfy several needs” [1]. To solve the problem of action selection, an action selection mechanism needs to be implemented and tested in good conditions for its validation. Therefore we implemented a motivational model of action selection [2] applied to virtual humans in which many hierarchical classifier systems (HCS) [3] are working in parallel (one for each motivation and their number is not limited). The activity is propagated throughout the hierarchical classifier system and selection of the most activated node is not carried out at each layer, as in classical hierarchy, but only at the end, as in a free flow hierarchy [4] (the action layer). In the end, the action chosen is the most activated according to the motivations and information environment. Figure 1: a hierarchical decision graph of our model for one motivation Since then the model has been much improved to validate it in a simulated environment with the aim of respecting the six criteria [2] that we defined respecting Tyrrell’s requirements [5] for designing a mechanism of action selection inspired by models of animals’ decision-making. In this article, we define what should be included in simulated environments in order to test all the capabilities of this model, which should follow the six criteria. Using the real-time development framework VHD++ [6] for advanced virtual human simulation, we define a simulated environment with ten conflicting motivations. A video shows the first results validating the model in the simulated environment. Motivation Internal Variable Motivated Behavior(s) Motivated Action(s) Locomotion Action(s) Environment Information Other Motivations - + HCS