4th International Workshop on
EXplainable TRAnsparent AI and Multi-Agent Systems
(EXTRAAMAS 2022)


#Important Dates
Deadline for Submissions: 1 March 2022
Notification of acceptance: 10 March 2022
Camera-ready: 20 May 2022
Workshop day: 9-10 May 2022


#Call for Papers
The aim of the workshop is to gather researchers interested in developing an explainable agency for goal-oriented agents and robots supported by machine learning mechanisms. In particular, participants are invited to submit papers addressing whichever phase of explainability (e.g., generation, communication, and reception) fostering transparency in Autonomous Agents and Multi-Agent Systems (MAS) and robots. Compliance with such requirements is becoming necessary in most systems where agent-oriented approaches are increasingly employed.

Therefore, the purpose of this second “International workshop on Explainable Intelligence in Autonomous Agent and Multi-Agent Systems” (EXTRAAMAS) is five-fold:
(i) to strengthen the common ground for the study and development of explainable and understandable autonomous agents, robots, and Multi-Agent Systems (MAS),
(ii) to explore how agent explainability and machine learning interpretability can be combined to achieve systems capable of both perceptual data-driven and cognitive goal-driven explainability
(iii) to investigate the potential of agent-based systems for personalized user-aware explainable AI,
(iv) to assess the impact of transparent and explained solutions on the user/agents behaviors,
(v) to discuss motivating examples and concrete applications in which the lack of explainability leads to problems, which would be resolved by explainability,
(vi) to assess and discuss the first demonstrators and proof of concepts paving the way for the next generation systems,
(vii) to explore the emerging interactions and synergies between XAI and law



## Topics of EXTRAAMAS (non-exclusive list) ##

# Special Track I: XAI & Law
- The legal requirements of explainability
- How does the (technical) human-in-the-loop approach relate to the (legal) notion of automated decision making?
- XAI application in the domain of law
- (X)AI for legal explanations
- (X)AI for explaining legal decisions

#Special Track II: The chist-ERA of XAI
- Human- and agent-based argumentation for XAI
- XAI and reinforcement learning
- Knowledge graphs for XAI
- Computational creativity and Planning for XAI
- XAI and robotics
- Symbolic knowledge extraction/injection
- Graph neural networks for XAI
- XAI for deep-learning-aided diagnoses
- Case-based reasoning in XAI
- Complex-networks for XAI
- Success stories and surveys about XAI

# Explainable Agents and Robots
- Explainable agent architectures
- Personalized XAI
- Explainable & Expressive robots
- Explainable human-robot collaboration
- Reinforcement Learning Agents
- Multi-modal explanations

# XAI & Ethics
- Social XAI
- AI, ethics, and explainability
- XAI vs AI

# XAI & MAS
- Multi-actors interaction in XAI
- XAI for agent/robots teams
- Simulations for XAI

# Interdisciplinary Aspects
- Cognitive and social sciences perspectives on explanations
- HCI for XAI
- Legal aspects of explainable agents
- Explanation visualization

# XAI Machine learning and Knowledge Representation
- Bridging symbolic and subsymbolic XAI
- Knowledge generation from interpretations
- Explanation visualization
- Explainable knowledge generation



# Workshop Chairs
Dr. Davide Calvaresi, HES-SO, Switzerland
Dr. Amro Najjar, University of Luxembourg, Luxembourg
Prof. Kary Främling, Umea University Sweden and Aalto University, Finland,
Prof. Michael Winikoff, Victoria University Wellington.

# Special Tracks Chairs
Dr. Reka Markovich, University of Luxembourg
Giovanni Ciatto, University of Bologna

# Advisory Board
Dr. Tim Miller, School of Computing and Information Systems at The University of Melbourne.
Prof. Leon van der Torre, University of Luxembourg, Luxembourg
Prof. Virginia Dignum, Umea University, Sweden
Prof. Michael Ignaz Schumacher, HES-SO, Switzerland