Probabilistic Parameterized Polynomial Time

Abstract

We examine a parameterized complexity class for randomized computation where only the error bound and not the full runtime is allowed to depend more than polynomially on the parameter, based on a proposal by Kwisthout in [15,16]. We prove that this class, for which we propose the shorthand name PPPT, has a robust definition and is in fact equal to the intersection of the classes paraBPP and PP. This result is accompanied by a Cook-style proof of completeness for the corresponding promise class (under a suitable notion of reduction) for parameterized approximation versions of the inference problem in Bayesian networks, which is known to be PP-complete. With these definitions and results in place, we proceed by showing how it follows from this that derandomization is equivalent to efficient deterministic approximation methods for the inference problem. Furthermore, we observe as a straightforward application of a result due to Drucker in [8] that these problems cannot have polynomial size randomized kernels unless the polynomial hierarchy collapses to the third level. We conclude by indicating potential avenues for further exploration and application of this framework.

Creating Teams of Simple Agents for Specified Tasks: A Computational Complexity Perspective

“…Once these initial intractability maps have been derived for simplified team operation models, they should be extended to more realistic models incorporating stochasticity and uncertainty. Part of this can be done by using complexity analysis techniques that explicitly incorporate stochasticity [22,28,29]. Complexity-based frameworks that incrementally build on simplified operation models in a systematic and principled manner to create results applicable to more realistic models (analogous to those developed in linguistics [30] and cognitive science [31]) may also be of use in this endeavour.…”