Point-Based Methods for Model Checking in Partially Observable Markov Decision Processes

01/11/2020
by   Maxime Bouton, et al.
0

Autonomous systems are often required to operate in partially observable environments. They must reliably execute a specified objective even with incomplete information about the state of the environment. We propose a methodology to synthesize policies that satisfy a linear temporal logic formula in a partially observable Markov decision process (POMDP). By formulating a planning problem, we show how to use point-based value iteration methods to efficiently approximate the maximum probability of satisfying a desired logical formula and compute the associated belief state policy. We demonstrate that our method scales to large POMDP domains and provides strong bounds on the performance of the resulting policy.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

01/30/2013

Planning with Partially Observable Markov Decision Processes: Advances in Exact Solution Method

There is much interest in using partially observable Markov decision pro...
01/05/2021

Improving Training Result of Partially Observable Markov Decision Process by Filtering Beliefs

In this study I proposed a filtering beliefs method for improving perfor...
09/09/2013

Technical Report: Distribution Temporal Logic: Combining Correctness with Quality of Estimation

We present a new temporal logic called Distribution Temporal Logic (DTL)...
09/04/2020

Technical Report: The Policy Graph Improvement Algorithm

Optimizing a partially observable Markov decision process (POMDP) policy...
04/04/2021

Reinforcement Learning with Temporal Logic Constraints for Partially-Observable Markov Decision Processes

This paper proposes a reinforcement learning method for controller synth...
12/23/2020

Identification of Unexpected Decisions in Partially Observable Monte-Carlo Planning: a Rule-Based Approach

Partially Observable Monte-Carlo Planning (POMCP) is a powerful online a...
04/28/2021

Rule-based Shielding for Partially Observable Monte-Carlo Planning

Partially Observable Monte-Carlo Planning (POMCP) is a powerful online a...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.