Maximizing expected impact in an agent reputation network

Gavin Rens*, Abhaya Nayak, Thomas Meyer

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

2 Citations (Scopus)


We propose a new framework for reasoning about the reputation of multiple agents, based on the partially observable Markov decision process (POMDP). It is general enough for the specification of a variety of stochastic multi-agent system (MAS) domains involving the impact of agents on each other’s reputations. Assuming that an agent must maintain a good enough reputation to survive in the system, a method for an agent to select optimal actions is developed.

Original languageEnglish
Title of host publicationKI 2018
Subtitle of host publicationAdvances in Artificial Intelligence - 41st German Conference on AI, 2018, Proceedings
EditorsFrank Trollmann, Anni-Yasmin Turhan
Place of PublicationSwitzerland
PublisherSpringer-VDI-Verlag GmbH & Co. KG
Number of pages8
ISBN (Electronic)9783030001117
ISBN (Print)9783030001100
Publication statusPublished - 1 Jan 2018
Event41st German Conference on Artificial Intelligence, KI 2018 - Berlin, Germany
Duration: 24 Sept 201828 Sept 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11117 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference41st German Conference on Artificial Intelligence, KI 2018


  • Planning
  • Trust and reputation
  • Uncertainty


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