Lifting DecPOMDPs for Nanoscale Systems -- A Work in Progress

TL;DR

This paper introduces lifted DecPOMDPs to efficiently model large nanoscale systems like DNA nanonetworks, reducing computational complexity and applying it to a medical system, with future work focusing on solving and implementation.

Contribution

It proposes a novel lifted DecPOMDP framework that partitions agents to reduce complexity, specifically tailored for nanoscale systems such as DNA-based nanonetworks.

Findings

Reduced state space through agent partitioning

Application to nanoscale medical systems

Framework sets stage for future solution methods

Abstract

DNA-based nanonetworks have a wide range of promising use cases, especially in the field of medicine. With a large set of agents, a partially observable stochastic environment, and noisy observations, such nanoscale systems can be modelled as a decentralised, partially observable, Markov decision process (DecPOMDP). As the agent set is a dominating factor, this paper presents (i) lifted DecPOMDPs, partitioning the agent set into sets of indistinguishable agents, reducing the worst-case space required, and (ii) a nanoscale medical system as an application. Future work turns to solving and implementing lifted DecPOMDPs.