Systematic vs. Non-systematic Algorithms for Solving the MPE Task

by   Radu Marinescu, et al.

The paper continues the study of partitioning based inference of heuristics for search in the context of solving the Most Probable Explanation task in Bayesian Networks. We compare two systematic Branch and Bound search algorithms, BBBT (for which the heuristic information is constructed during search and allows dynamic variable/value ordering) and its predecessor BBMB (for which the heuristic information is pre-compiled), against a number of popular local search algorithms for the MPE problem. We show empirically that, when viewed as approximation schemes, BBBT/BBMB are superior to all of these best known SLS algorithms, especially when the domain sizes increase beyond 2. This is in contrast with the performance of SLS vs. systematic search on CSP/SAT problems, where SLS often significantly outperforms systematic algorithms. As far as we know, BBBT/BBMB are currently the best performing algorithms for solving the MPE task.



There are no comments yet.


page 1

page 2

page 5

page 8


A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks

This paper extends the work in [Suzuki, 1996] and presents an efficient ...

Symbiosis of Search and Heuristics for Random 3-SAT

When combined properly, search techniques can reveal the full potential ...

Contrained Generalization For Data Anonymization - A Systematic Search Based Approach

Data generalization is a powerful technique for sanitizing multi-attribu...

Improving probability selecting based weights for Satisfiability Problem

The Boolean Satisfiability problem (SAT) is important on artificial inte...

Weight-Based Variable Ordering in the Context of High-Level Consistencies

Dom/wdeg is one of the best performing heuristics for dynamic variable o...

Heuristic Search as Evidential Reasoning

BPS, the Bayesian Problem Solver, applies probabilistic inference and de...

Algorithm Portfolio Design: Theory vs. Practice

Stochastic algorithms are among the best for solving computationally har...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.