Multiagent Hierarchical Learning from Demonstration

Programming agent behaviors is a tedious task. In HITAB, agents learn a hierarchical finite state automata The difficulty increases in a multiagent setting due to the increased (HFA) represented as a Moore machine where individual size of the design space. Density of interactions, the states correspond to agent behaviors or another HFA. An number of agents and the agent's heterogeneity (both capabilities HFA is built iteratively: staring with a behavior library consisting and behaviors) all contribute to the larger design space. The now expanded One training approach is Learning from Demonstration behavior library is then used to train an even more (LfD) in which agents learn behaviors in real-time based on complex behavior which is then saved to the library, and provided examples from a human demonstrator.