Iterative Planning for Deterministic QDec-POMDPs

14 pages•Published: September 17, 2018

Abstract

QDec-POMDPs are a qualitative alternative to stochastic Dec-POMDPs for goal-oriented plan- ning in cooperative partially observable multi-agent environments. Although QDec-POMDPs share the same worst case complexity as Dec-POMDPs, previous research has shown an ability to scale up to larger domains while producing high quality plan trees. A key difficulty in distributed execution is the need to construct a joint plan tree branching on the combinations of observations of all agents. In this work, we suggest an iterative algorithm, IMAP, that plans for one agent at a time, taking into considerations collaboration constraints about action execution of previous agents, and generating new constraints for the next agents. We explain how these constraints are generated and handled, and a backtracking mechanism for changing constraints that cannot be met. We provide experimental results on multi-agent planning domains, showing our methods to scale to much larger problems with several collaborating agents and huge state spaces.

Keyphrases: contingent planning, distributed planning, multi-agent planning, Planning under uncertainty, POMDPs

In: Daniel Lee, Alexander Steen and Toby Walsh (editors). GCAI-2018. 4th Global Conference on Artificial Intelligence, vol 55, pages 15--28

Links:	https://easychair.org/publications/paper/zQJx
	https://doi.org/10.29007/4t8s