## 2013

# Compositional Analysis of Dynamic Bayesian Networks and Applications to Complex Dynamic System Decomposition

Yuchen Zhou, Shahan Yang and John Baras

*Proceedings of Conference on Systems Engineering Research (CSER’13), pp. 167-176, Atlanta, GA,* March 19-22, 2013

**Abstract**

Dynamic Bayesian networks (DBNs) can be eectively used to model various problems in complex dynamic systems. We perform
an empirical investigation on compositional analysis of DBNs using abstraction. In static systems and hidden Markov models,
computation of a metric called treewidth induces a tree decomposition that can be used to perform logical or probabilistic inference
and max + optimizations in time exponential in treewidth and linear in overall system size. Intuitively, the linear scaling means
that very large systems can be analyzed as long as they are suciently sparse and well structured. In these simple cases, summary
propagation, which uses two operations, summation (projection) and product (composition), suces to perform the inference or
optimization. Here, we begin an extension of this to structured networks of communicating dynamic systems.

We define generalizations of projection and composition operators that treat labeled Markov chains as primitive objects. The
projection operation, corresponding to summation, is implemented as label deletion followed by exact state reduction for Markov
chains, similar to Hopcroft’s DFA minimization algorithm, with O(n logm) complexity. The composition operation is the product
of state machines. We use canonical MDDs, similar to BDDs, to capture logical dependencies symbolically. Combining symbolic
representations with Markov chain lumping algorithms is a novel contribution. Using this approach, we have created a tool leveraging
model based systems engineering technologies. The communicating Markov chains are specified using UML Statecharts via
Papyrus extended using an ANTLR parsed domain specific language (DSL).

The tool reduces the number of states in networks of Markov chains by several orders of magnitude. In one example, a network
having a product state space of more than 600 million states is reduced to about 500 states. A feature of this technique is that
the state space is examined incrementally, meaning that the full state space is never explicitly represented, even as an input to the
reduction algorithm. The primary reduction appears to come from symmetry which is surprising because the technique includes no
explicit symmetry handling. We note that composition is ecient at least for systems with high symmetry. We describe applications
to a hospital intensive care unit (ICU) from a systems engineering perspective.