Structured Representation of Complex Stochastic Systems

This paperconsidersthe problem of representingcomplex systems that evolve stochastically over time. Dynamic Bayesian networks provide a compact representation for stochastic processes. Unfortunately, they are often unwieldy since they cannot explicitly model the complex organizational structure of many real life systems: the fact that processes are typically composed of several interacting subprocesses, each of which can, in turn, be further decomposed. We propose a hierarchically structured representation language which extends both dynamic Bayesian networks and the object-oriented Bayesian network framework of [9], and show that our language allows us to describe such systems in a natural and modular way. Our language supports a natural representation for certain system characteristics that are hard to capture using more traditional frameworks. For example, it allows us to represent systems where some processes evolve at a different rate than others, or systems where the processes i...

• Type: Conference
• Year: 1998
• Where: AAAI
• Authors: Nir Friedman, Daphne Koller, Avi Pfeffer
• Keywords: AAAI 1998, Bayesian Network Framework, Bayesian Networks, Dynamic Bayesian Networks, Intelligent Agents