Modeling and Analyzing Multi-agent Task Plans for Intelligent Virtual Training System using Petri Nets

Integrated virtual reality with intelligent tutoring system, a multi-agent architecture was proposed for intelligent virtual training system (IVTS) for mine safety training. In order to make sure IVTS agent´s task plans reliable and adaptive, a Petri nets-based declarative method was applied to model the virtual training task planning knowledge, which was represented as task planning knowledge Petri nets (TP-PNets), and an algorithm was implemented to construct TP-PNets. Then, hierarchy colored Petri nets (HCPN) was used to model multi-agent task planning behaviors for IVTS, and simulation and message sequence chart was used to analyze and verify the agent task planning HCPN model