Hierarchical Parallel Markov Models of Interaction
Finite state techniques are popular in entertainment software production, but they complicate the modeling of certain aspects of social engagement. In this paper we examine the problem of building probabilistic finite-state interaction models that allow both hierarchical composition of behaviors, and their parallel engagement. Finally, we propose an extension that resolves the difficulties for a class of common cases.
How to Cite
Zubek, R., & Horswill, I. (2021). Hierarchical Parallel Markov Models of Interaction. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 1(1), 141-146. https://doi.org/10.1609/aiide.v1i1.18731