A Unified Decision-Theoretic Model for Information Gathering and Communication Planning

Jennifer Renoux, Tiago S. Veiga, Pedro U. Lima, Matthijs T.J. Spaan

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

We consider the problem of communication planning for human-machine cooperation in stochastic and partially observable environments. Partially Observable Markov Decision Processes with Information Rewards (POMDPs-IR) form a powerful framework for information-gathering tasks in such environments. We propose an extension of the POMDP-IR model, called a Communicating POMDP-IR (com-POMDP-IR), that allows an agent to proactively plan its communication actions by using an approximation of the human’s beliefs. We experimentally demonstrate the capability of our com-POMDPIR agent to limit its communication to relevant information and its robustness to lost messages.
Original languageEnglish
Title of host publication2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)
Subtitle of host publicationProceedings
PublisherIEEE
Pages67-74
Number of pages8
ISBN (Electronic)978-1-7281-6075-7
ISBN (Print)978-1-7281-6076-4
DOIs
Publication statusPublished - 2020
EventThe 29th IEEE International Conference on Robot and Human Interactive Communication - 2020 VIRTUAL CONFERENCE
Duration: 31 Aug 20204 Sep 2020
Conference number: 29

Conference

ConferenceThe 29th IEEE International Conference on Robot and Human Interactive Communication
Abbreviated titleRo-MAN 2020
Period31/08/204/09/20

Bibliographical note

Virtual/online event due to COVID-19

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