Privacy preserving planning in stochastic environments

Guy Shani, Roni Stern, Tommy Hefner

Research output: Contribution to journalConference articlepeer-review

Abstract

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 Real-Time Dynamic Programming (RTDP) algorithm for computing value functions for MDPs, extending it to the stochastic CPPP setting. We provide two versions of RTDP: a complete version identical to executing centralized RTDP, and an approximate version that sends significantly fewer messages and computes competitive policies in practice. We experiment on domains adapted from the deterministic CPPP literature.

Original languageAmerican English
Pages (from-to)258-262
Number of pages5
JournalProceedings International Conference on Automated Planning and Scheduling, ICAPS
Volume30
StatePublished - 29 May 2020
Event30th International Conference on Automated Planning and Scheduling, ICAPS 2020 - Nancy, France
Duration: 26 Oct 202030 Oct 2020

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems and Management

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