Modeling and understanding future action decisions of players during online gaming

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

doi:10.1145/3527188.3563926

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 proceeding › Conference proceeding contribution › peer-review

1 Citation (Scopus)

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 language	English
Title of host publication	HAI 2022
Subtitle of host publication	Proceedings of the 10th Conference on Human-Agent Interaction
Place of Publication	New York, NY
Publisher	Association for Computing Machinery, Inc
Pages	324-326
Number of pages	3
ISBN (Electronic)	9781450393232
DOIs	https://doi.org/10.1145/3527188.3563926
Publication status	Published - 5 Dec 2022
Event	International Conference on Human-Agent Interaction (10th : 2022) - Christchurch, New Zealand Duration: 5 Dec 2022 → 8 Dec 2022

Conference

Conference	International Conference on Human-Agent Interaction (10th : 2022)
Abbreviated title	HAI ’22
Country/Territory	New Zealand
City	Christchurch
Period	5/12/22 → 8/12/22

Keywords

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

Access to Document

10.1145/3527188.3563926

Cite this

@inproceedings{7e5bc451f2bc4ff2b6aaee4feb6e2187,

title = "Modeling and understanding future action decisions of players during online gaming",

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.",

keywords = "artificial neural networks, decision-making, explainable-AI, joint-action, multi-agent interaction, supervised machine learning",

author = "Fabrizia Auletta and Gaurav Patil and Kallen, {Rachel W.} and {Di Bernardo}, Mario and Richardson, {Michael J.}",

year = "2022",

month = dec,

day = "5",

doi = "10.1145/3527188.3563926",

language = "English",

pages = "324--326",

booktitle = "HAI 2022",

publisher = "Association for Computing Machinery, Inc",

note = "International Conference on Human-Agent Interaction (10th : 2022), HAI {\textquoteright}22 ; Conference date: 05-12-2022 Through 08-12-2022",

}

Auletta, F, Patil, G , Kallen, RW, Di Bernardo, M & Richardson, MJ 2022, Modeling and understanding future action decisions of players during online gaming. in HAI 2022: Proceedings of the 10th Conference on Human-Agent Interaction. Association for Computing Machinery, Inc, New York, NY, pp. 324-326, International Conference on Human-Agent Interaction (10th : 2022), Christchurch, New Zealand, 5/12/22. https://doi.org/10.1145/3527188.3563926

Modeling and understanding future action decisions of players during online gaming. / Auletta, Fabrizia; Patil, Gaurav ; Kallen, Rachel W. et al.
HAI 2022: Proceedings of the 10th Conference on Human-Agent Interaction. New York, NY: Association for Computing Machinery, Inc, 2022. p. 324-326.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Modeling and understanding future action decisions of players during online gaming

AU - Auletta, Fabrizia

AU - Patil, Gaurav

AU - Kallen, Rachel W.

AU - Di Bernardo, Mario

AU - Richardson, Michael J.

PY - 2022/12/5

Y1 - 2022/12/5

N2 - 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.

AB - 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.

KW - artificial neural networks

KW - decision-making

KW - explainable-AI

KW - joint-action

KW - multi-agent interaction

KW - supervised machine learning

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U2 - 10.1145/3527188.3563926

DO - 10.1145/3527188.3563926

M3 - Conference proceeding contribution

AN - SCOPUS:85144608782

SP - 324

EP - 326

BT - HAI 2022

PB - Association for Computing Machinery, Inc

CY - New York, NY

T2 - International Conference on Human-Agent Interaction (10th : 2022)

Y2 - 5 December 2022 through 8 December 2022

ER -

Modeling and understanding future action decisions of players during online gaming

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Keywords

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