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EXTRAAMAS 2022
EXplainable and TRAnsparent AI and Multi-Agent Systems: Fourth International Workshop
Auckland, New Zealand, 09/05/2022–10/05/2022
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 fourth “International workshop on Explainable Intelligence in fourth 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 in 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 interest
Special Focus: 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
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