Privacy Preserving Planning in Stochastic Environments

Guy Shani, Roni Stern, Tommy Hefner

PosterID: 15
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Collaborative privacy preserving planning (CPPP) has gained much attention in the past decade. To date, CPPP has focused on domains with deterministic action effects. In this paper, we extend CPPP to domains with stochastic action effects. We show how such environments can be modeled as an MDP. We then focus on the popular RTDP algorithm for computing value functions for MDPs, extending it to the stochastic CPPP setting. We provide two versions of RTDP: first a complete version identical to executing centralized RTDP, and then an approximate version that sends an order of magnitude less messages, but computes good policies in practice. We provide experiments on domains adapted from the deterministic CPPP literature.

Session E9: Multi-Agent Planning
Canb 10/27/2020, 21:00 – 22:00
10/31/2020, 04:00 – 05:00
Paris 10/27/2020, 11:00 – 12:00
10/30/2020, 18:00 – 19:00
NYC 10/27/2020, 06:00 – 07:00
10/30/2020, 13:00 – 14:00
LA 10/27/2020, 03:00 – 04:00
10/30/2020, 10:00 – 11:00