To appear, Fifteenth National Conf. on Artificial Intelligence (AAAI), 1998.
This paper considers the problem of representing complex 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 [Koller and Pfeffer, 1997], 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 interact
only intermittently. We provide a simple inference mechanism for our representation via
translation to Bayesian networks, and suggest ways in which the inference algorithm can
exploit the additional
structure encoded in our representation.
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