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EXTRAAMAS 2024 : EXplainable and TRAnsparent AI and Multi-Agent Systems


When May 6, 2024 - May 7, 2024
Where Auckland, NZ
Submission Deadline Mar 10, 2024
Notification Due Mar 25, 2024
Final Version Due Jun 15, 2024
Categories    XAI   explainability   law & ethics   explainable dialogs

Call For Papers

6th International Workshop on
EXplainable and TRAnsparent AI and Multi-Agent Systems

in conjunction with AAMAS 2024,
Auckland, New Zealand, 6-7 May 2024

#Important Dates
Paper submission: 10/03/2024
Notification of acceptance: 25/03/2025
Early registration deadline: 05/03/2024.
Workshop: 06-07/05/2023
Camera-ready (Springer post-proceedings): 10/06/2023
Submission link:

Running since 2019, EXTRAAMAS is a well-established workshop and forum on EXplainable and TRAnsparent AI and Multi-Agent Systems. It aims to discuss and disseminate research on explainable artificial intelligence, with a particular focus on intra/inter-agent explainability and cross-disciplinary perspectives. In its 6th edition, EXTRAAMAS identifies four particular focus topics with the ultimate goal of strengthening cutting-edge foundational and applied research. This, of course, comes in addition to the workshop's main theme, focusing, as usual, on XAI fundamentals. The four tracks for this year are:

#Track 1: XAI in symbolic and subsymbolic AI: the “AI dichotomy” separating symbolic AKA classical AI from connectionism AI has persisted for more than seven decades. Nevertheless, the advent of explainable AI has accelerated and intensified the efforts to bridge this gap since providing faithful explanations of black-box machine learning techniques would necessarily mean combining symbolic and subsymbolic AI. This track aims to discuss the recent works on this hot topic of AI.
Track chair: Giovanni Ciatto, University of Bologna, Italy.

#Track 2: XAI in negotiation and conflict resolution: Conflict resolution (e.g., agent-based negotiation, voting, argumentation, etc.) has been a prosperous domain within the MAS community since its foundation. However, as agents and the problems they are tackling become more complex, incorporating explainability becomes vital to assess the usefulness of the supposedly conflict-free solution. This is the main topic of this track, with a special focus on MAS negotiation and explainability.
Track Chair: Reyhan Aydoğan: Ozyegin University, Turkey

#Track 3: Prompts, Interactive Explainability and Dialogues: Appropriate everyday explanations about automated decision-making are context-dependent and interactive. An explanation must fill a 'gap' in the apparent knowledge of the user in a specific context. However, dynamic user modelling is hard. Explanatory dialogue allows designers to try out partial explanations and fine-tune or adjust the explanations based on feedback. This potential for dynamic adjustment can only be redeemed if the system has appropriate interactive capabilities, such as context modelling, user modelling, initiative handling, topic management and grounding. The rapid evolution of LLM and Chatbots has sparked a debate on how to make good use of the interactive capabilities of these new models for explainable AI. The use of LLM also has risks, especially concerning reliability. This triggers relevant methodological questions. How to ensure LLM use reliable data for answering? How to evaluate research based on black-box models? What are good techniques for prompt engineering? In this research track, we welcome new ideas as well as established research outcomes, on the wider topic of Interactive or Social Explainable AI.
Track chair: Joris Hulstijn, University of Luxembourg

#Track 4: XAI in Law and Ethics: complying with regulation (e.g. GDPR) is among the main objectives for XAI. The right to explanation is key to ensuring transparency of ever more complex AI systems dealing with a multitude of sensitive AI applications. This track discusses works related to explainability in AI ethics, machine ethics, and AI and law.
Track chair: Rachele Cari, University of Bologna, Italy

This year, EXTRAAMAS will feature a keynote delivered by Brian Lim (title TBD)

All accepted papers are eligible for publication in the Springer Lecture Notes of Artificial Intelligence conference proceedings (after revisions have been applied).

Track1: XAI in symbolic and subsymbolic AI
XAI for Machine learning
Explainable neural networks
Symbolic knowledge injection or extraction
Neuro-symbolic computation
Computational logic for XAI
Multi-agent architectures for XAI
Surrogate models for sub-symbolic predictors
Explainable planning (XAIP)
XAI evaluation

Track2: XAI in negotiation and conflict resolution
Explainable conflict resolution techniques/frameworks
Explainable negotiation protocols and strategies
Explainable recommendation systems
Trustworthy voting mechanisms
Argumentation for explaining the process itself
Argumentation for explaining and supporting the potential outcomes
Explainable user/agent profiling (e.g., learning user's preferences or strategies)
User studies and assessment of the aforementioned approaches
Applications (virtual coaches, robots, IoT)

Track3: Prompts, Interactive Explainability and Dialogue
Interactive capabilities for XAI
Arguments for persuasive explanations
Context modelling
User modelling
Initiative handling
Topic modelling
Grounding and acknowledgement
Prompt engineering
Research methodology for LLM applications
Responsible LLM applications

Track4: (X)AI in Law and Ethics
XAI in AI & Law
Fair AI
XAI & Machine Ethics
Bias reduction
Deception and XAI
Persuasive technologies and XAI
Nudging and XAI
Legal issues of XAI
Liability and XAI
XAI, Transparency, and the Law
Enforceability and XAI
Culture-aware systems and XAI

#Workshop Chairs
Dr. Davide Calvaresi, HES-SO, Switzerland
research areas: Real-Time Multi-Agent Systems, Explainable AI, BCT, eHealth,
mail:, web page, Google.scholar
Dr. Amro Najjar, University of Luxembourg, Luxembourg
research areas: Multi-Agent Systems, Explainable AI, AI
mail:, Google Scholar
Prof. Kary Främling, Umeå & Aalto University Sweden/Finland,
research areas: Explainable AI, Artificial Intelligence, Machine Learning, IoT
mail:, web page, Google Scholar
Prof. Andrea Omicini
research areas: Artificial Intelligence, Multi-agent Systems, Soft. Engineering
mail:, web page, Google Scholar

#Track Chairs
Dr. Giovanni Ciatto, University of Bologna, Italy –
Prof. Rehyan Aydogan, Ozyegin University, Turkey –
Rachele Carli, University of Bologna –
Joris HULSTIJN: University of Luxembourg –

#Advisory Board
Dr. Tim Miller, University of Melbourne
Prof. Leon van der Torre, UNILU
Prof. Virginia Dignum, Umea University
Prof. Michael Ignaz Schumacher

Primary Contacts
Davide Calvaresi -
Amro Najjar -

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