Upper Expected Meeting Times for Interdependent Stochastic Agents

TL;DR

This paper models and computes bounds on the expected meeting times of interdependent stochastic agents with epistemic uncertainty using imprecise Markov chains, providing a framework for analyzing their first encounter times.

Contribution

It introduces a novel approach to bound meeting times of uncertain stochastic agents using imprecise Markov chains, extending to multiple agents with symmetry considerations.

Findings

Exact bounds on meeting times can be computed under epistemic uncertainty.

The approach is tight and can be extended to multiple agents.

Symmetries help mitigate combinatorial complexity.

Abstract

We analyse the problem of meeting times for interdependent stochastic agents: random walkers whose behaviour is stochastic but controlled by their selections from some set of allowed actions, and the inference problem of when these agents will be in the same state for the first time. We consider the case where we are epistemically uncertain about the selected actions of these agents, and show how their behaviour can be modelled using imprecise Markov chains. This allows us to use results and algorithms from the literature, to exactly compute bounds on their meeting time, which are tight with respect to our epistemic uncertainty models. We focus on the two-agent case, but discuss how it can be naturally extended to an arbitrary number of agents, and how the corresponding combinatorial explosion can be partly mitigated by exploiting symmetries inherent in the problem.