Modeling and understanding future action decisions of players during online gaming

Fabrizia Auletta, Gaurav Patil, Rachel W. Kallen, Mario Di Bernardo, Michael J. Richardson

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

Abstract

Contemporary Supervised Machine Learning (SML) and explainable AI (artificial intelligence) methods can be employed to both model and understand the decision making behavior of human actors within a multi-agent task setting. Here, we apply such modeling approach to capture the decision-making behavior of human actors playing a 3-player online herding game called "Desert Herding". Of particular interest is whether the modeling approach can be employed to predict and understand the target switching strategies of human herders at variable prediction horizons and whether the explainable AI tool SHAP can be leveraged to identify the key informational variables (features) underlying the players' target selection decisions.

Original languageEnglish
Title of host publicationHAI 2022
Subtitle of host publicationProceedings of the 10th Conference on Human-Agent Interaction
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery, Inc
Pages324-326
Number of pages3
ISBN (Electronic)9781450393232
DOIs
Publication statusPublished - 5 Dec 2022
EventInternational Conference on Human-Agent Interaction (10th : 2022) - Christchurch, New Zealand
Duration: 5 Dec 20228 Dec 2022

Conference

ConferenceInternational Conference on Human-Agent Interaction (10th : 2022)
Abbreviated titleHAI ’22
Country/TerritoryNew Zealand
CityChristchurch
Period5/12/228/12/22

Keywords

  • artificial neural networks
  • decision-making
  • explainable-AI
  • joint-action
  • multi-agent interaction
  • supervised machine learning

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