
TL;DR
This paper investigates the computational power of Random Access Machines (RAMs) with stochastic capabilities, specifically whether using a pseudorandom function like RAND(y) offers an advantage over deterministic RAMs, closing a 30-year open problem.
Contribution
The paper fully characterizes the classes of languages recognizable by RAMs with stochastic RAND(y) functions, determining when stochasticity provides computational advantages.
Findings
Stochastic RAMs can be more powerful than deterministic ones for certain classes.
For some RAM models, randomness does not increase computational power.
The paper resolves Simon's 1981 open problem regarding stochastic RAMs.
Abstract
One of the fundamental open questions in computational complexity is whether the class of problems solvable by use of stochasticity under the Random Polynomial time (RP) model is larger than the class of those solvable in deterministic polynomial time (P). However, this question is only open for Turing Machines, not for Random Access Machines (RAMs). Simon (1981) was able to show that for a sufficiently equipped Random Access Machine, the ability to switch states nondeterministically does not entail any computational advantage. However, in the same paper, Simon describes a different (and arguably more natural) scenario for stochasticity under the RAM model. According to Simon's proposal, instead of receiving a new random bit at each execution step, the RAM program is able to execute the pseudofunction , which returns a uniformly distributed random integer in the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComputability, Logic, AI Algorithms · Algorithms and Data Compression · semigroups and automata theory
