Modeling dynamic decision-making of virtual humans

Abstract

Imagine a person visiting an urban event. At each moment in time, the person has to weigh up different possible actions and make consecutive decisions. For instance, a person might be hungry or thirsty and would therefore like to go somewhere to eat or to drink, or a person might need to go to the toilet and thus go searching for the restrooms. Other possible desires might be to go dancing or to have a rest due to exhaustion. All these examples can be seen in the context of dynamic decision-making. To be able to implement the dynamic decision-making of virtual humans living their lives in a persistent microworld, an advanced concept to solve this—in artificial intelligence research commonly called action selection problem—is required. This article focuses on an novel approach to model the activation of motivations—as an attempt to answer the recurring question of the virtual humans “What to do next?”. The novelty is to use System Dynamics, in general defined as a top-down simulation approach, from the bottom-up inside each instance of the agent population and to implement an action selection mechanism on the basis of this methodology. This approach enables us to model the dynamic decision-making of the virtual humans with stocks and flows resulting in nonlinear motivation evolution. A case study in the context of an urban event documents the application of this innovative method.