Explainable, Transparent Autonomous Agents and Multi-Agent Systems: First International Workshop, EXTRAAMAS 2019, Montreal, QC, Canada, May 13–14, 2019, Revised Selected Papers

221 Pages·2019·10.59 MB·English

by  Davide Calvaresi, Amro Najjar, Michael Schumacher, Kary Främling

Davide Calvaresi · Amro Najjar ·
Michael Schumacher · Kary Främling (Eds.)
Explainable, Transparent
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7 Autonomous Agents and
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First International Workshop, EXTRAAMAS 2019
Montreal, QC, Canada, May 13–14, 2019
Revised Selected Papers
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Lecture Notes in Arti cial Intelligence 11763
Subseries of Lecture Notes in Computer Science
Series Editors
Randy Goebel
University of Alberta, Edmonton, Canada
Yuzuru Tanaka
Hokkaido University, Sapporo, Japan
Wolfgang Wahlster
DFKI and Saarland University, Saarbrücken, Germany
Founding Editor
Jörg Siekmann
DFKI and Saarland University, Saarbrücken, Germany
More information about this series at http://www.springer.com/series/1244
Davide Calvaresi Amro Najjar
(cid:129) (cid:129)
ä
Michael Schumacher Kary Fr mling (Eds.)
(cid:129)
Explainable, Transparent
Autonomous Agents and
Multi-Agent Systems
First International Workshop, EXTRAAMAS 2019
–
Montreal, QC, Canada, May 13 14, 2019
Revised Selected Papers
123
Editors
Davide Calvaresi AmroNajjar
University of AppliedSciences University of Luxembourg
of Western Switzerland (HES-SO) Esch-sur-Alzette, Luxembourg
Sierre, Switzerland
Kary Främling
Michael Schumacher UmeåUniversity
University of AppliedSciences Umeå,Sweden
of Western Switzerland (HES-SO)
Sierre, Switzerland
ISSN 0302-9743 ISSN 1611-3349 (electronic)
Lecture Notesin Artificial Intelligence
ISBN 978-3-030-30390-7 ISBN978-3-030-30391-4 (eBook)
https://doi.org/10.1007/978-3-030-30391-4
LNCSSublibrary:SL7–ArtificialIntelligence
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Preface
In the last decade, the dependency of our society towards intelligent systems has
dramatically escalated. For example, data-driven techniques (typical of machine
learning - ML) such as classification, regression, and clustering are increasingly
employed in semi-automated diagnosis based on medical image recognition, financial
market forecasting, and customer profiling.
The internal mechanisms for such systems are opaque and not understandable nor
explainable. Therefore, humans are still intertwined with those semi-automated
intelligent systems due to legal, ethical, and user-requirement reasons. Moreover, the
real world is characterized by uncountable and heterogeneous (possibly abstract)
stimuli concurring in the composition of complex/articulated information. Persisting
with specific and isolated solutions (heavily demanding continuous handcrafting)
would only enhance the already unsustainable human-overhead.
To cope with the real world heterogeneity and enforce AI/ML trustworthiness, the
recently born discipline named eXplainable Artificial Intelligence (XAI) moved
towardsDistributedArtificialIntelligence(DAI)approaches.Inparticular,theresearch
community is fostering the adoption of Multi-Agent Systems (MAS) embodying DAI
(e.g.,autonomousvehicles,robots,smartbuildings,andIoT)toenforceexplainability,
transparency, and (above all) trustworthiness.
This volume contains a selection of the extended papers presented at the First
International Workshop on EXplainable TRansparent Autonomous Agents and
Multi-AgentSystems(EXTRAAMAS2019),heldinconjunctionwiththeInternational
ConferenceonAutonomousAgentsandMulti-AgentSystems(AAMAS2019),during
May 13–14, 2019, in Montreal, Canada.
The EXTRAAMAS 2019 organizers would like to thank the publicity chairs and
Program Committee for their valuable work.
June 2019 Davide Calvaresi
Amro Najjar
Michael Schumacher
Kary Främling
Organization
General Chair
Davide Calvaresi University of Applied Sciences Western Switzerland,
Switzerland
Amro Najjar University of Luxembourg, Luxembourg
Michael Schumacher University of Applied Sciences Western Switzerland,
Switzerland
Kary Främling Umeå University, Sweden
Publicity Chairs
Yazan Mualla University Bourgogne Franche-Comté, France
Timotheus Kampik Umeå University, Sweden
Amber Zelvelder Umeå University, Sweden
Program Committee
Andrea Omicini Università di Bologna, Italy
Ofra Amir Technion IE&M, Israel
Joost Broekens TU Delft, The Netherlands
Olivier Boissier ENS, Mines Saint-Étienne, France
J. Carlos N. Sanchez Umeå University, Sweden
Tathagata Chakraborti IBM Research AI, USA
Salima Hassas Lyon 1, France
Gauthier Picard EMSE Saint-Étienne, France
Jean-Guy Mailly Laboratoire d’Informatique de Paris Descartes, France
Aldo F. Dragoni Università Politecnica delle Marche, Italy
Patrick Reignier LIG Grenoble, France
Stephane Galland UTBM, France
Laurent Vercouter INSA Rouen, France
Helena Lindgren Umeå University, Sweden
Grégory Bonnet University of Caen, France
Jean-Paul Calbimonte University of Applied Sciences Western Switzerland,
Switzerland
Sarath Sreedharan Arizona State University, USA
Brent Harrison Georgia Institute of technology, USA
Koen Hindriks VU, The Netherlands
Laëtitia Matignon University Lyon 1, France
Simone Stumpf London City University, UK
Michael W. Floyd Knexus Research, USA
Kim Baraka CMU, USA
viii Organization
Dustin Dannenhauer Naval Research Lab, USA
Daniele Magazzeni King’s College London, UK
Cesar A. Tacla UTFPR, Brazil
Mor Vered University of Melbourne, Australia
Ronal Singh University of Melbourne, Australia
Divya Pandove Glover Park Group, USA
Husanbir Singh Pannu Thapar institute of Engineering and Technology, India
Avleen Malhi Thapar Institute of Engineering and Technology, India
Stefano Bromuri Open University of the Netherlands, The Netherlands
Rob Wortham University of Bath, UK
Fabien Dubosson University of Applied Sciences Western Switzerland,
Switzerland
Giuseppe Albanese University of Applied Sciences Western Switzerland,
Switzerland
Suna Bensch Umeå University, Sweden
Timotheus Kampik Umeå University, Sweden
Flavien Balbo École des Mines de Saint-Étienne, France
Yazan Mualla UTBM, France
Nicola Falcionelli Università Politecnica delle Marche, Italy
A. El Fallah Seghrouchni Sorbonne University, France
Önder Gürcan CEA, France
Stefan Sarkadi King’s College London, UK
Contents
Explanation and Transparency
Towards a Transparent Deep Ensemble Method Based
on Multiagent Argumentation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Naziha Sendi, Nadia Abchiche-Mimouni, and Farida Zehraoui
Effects of Agents’ Transparency on Teamwork. . . . . . . . . . . . . . . . . . . . . . 22
Silvia Tulli, Filipa Correia, Samuel Mascarenhas, Samuel Gomes,
Francisco S. Melo, and Ana Paiva
Explainable Robots
Explainable Multi-Agent Systems Through Blockchain Technology. . . . . . . . 41
Davide Calvaresi, Yazan Mualla, Amro Najjar, Stéphane Galland,
and Michael Schumacher
Explaining Sympathetic Actions of Rational Agents . . . . . . . . . . . . . . . . . . 59
Timotheus Kampik, Juan Carlos Nieves, and Helena Lindgren
Conversational Interfaces for Explainable AI:
A Human-Centred Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
Sophie F. Jentzsch, Sviatlana Höhn, and Nico Hochgeschwender
Opening the Black Box
Explanations of Black-Box Model Predictions by Contextual
Importance and Utility. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
Sule Anjomshoae, Kary Främling, and Amro Najjar
Explainable Artificial Intelligence Based Heat Recycler Fault Detection
in Air Handling Unit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
Manik Madhikermi, Avleen Kaur Malhi, and Kary Främling
Explainable Agent Simulations
Explaining Aggregate Behaviour in Cognitive Agent Simulations
Using Explanation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
Tobias Ahlbrecht and Michael Winikoff
BEN: An Agent Architecture for Explainable and Expressive
Behavior in Social Simulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
Mathieu Bourgais, Patrick Taillandier, and Laurent Vercouter
x Contents
Planning and Argumentation
Temporal Multiagent Plan Execution: Explaining What Happened. . . . . . . . . 167
Gianluca Torta, Roberto Micalizio, and Samuele Sormano
Explainable Argumentation for Wellness Consultation. . . . . . . . . . . . . . . . . 186
Isabel Sassoon, Nadin Kökciyan, Elizabeth Sklar, and Simon Parsons
Explainable AI and Cognitive Science
A Historical Perspective on Cognitive Science and Its Influence
on XAI Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205
Marcus Westberg, Amber Zelvelder, and Amro Najjar
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221