EXplainable, TRansparent Autonomous Agents and Multi-Agent Systems
The main aim of this second “International workshop on EXplainable TRansparent Autonomous Agents and Multi-Agent Systems” (EXTRAAMAS) is four-folded:
to establish a common ground for the study and development of explainable and understandable autonomous agents, robots and Multi-Agent Systems (MAS),
to investigate the potential of agent-based systems in the development of personalized user-aware explainable AI,
to assess the impact of transparent and explained solutions on the user/agents behaviors, and
to discuss motivating examples and concrete applications in which the lack of explainability leads to problems, which would be resolved by explainability.
Contributions are encouraged in both theoretical and practical applications for transparent and explainable intelligence in agents and MAS. Papers presenting theoretical contributions, designs, prototypes, tools, subjective user tests, assessment, new or improved techniques, and general survey papers tracking current evolutions and future directions are welcome.
Papers can either be:
Full papers (max. 16 pages LNCS style).
Demo papers (max. 5 pages LNCS style incl. references). Demo papers should describe implementations of explainable agents and multi-agent systems or explainable AI methods and algorithms that are applicable to explainable agents.
The work presented in the demo paper should be practically applicable. The source code of the implementation should be shared, for example by providing a web link.
The maximal length of a demo paper is 5 pages incl. references.
Demos will be presented in a similar manner as ordinary papers; however, a demo presentation should be interactive and ideally show the running implementation "live".
Participants are invited to submit papers on all research and application aspects of explainable and transparent intelligence in agents and MAS, including, but not limited to:
Topics of EXTRAAMAS
Explainable Agents and Robots
Explainable agent architectures
Explainable & Expressive robots
Explainable human-robot collaboration
Reinforcement Learning Agents
XAI & Ethics
AI, ethics, and explainability
XAI vs AI
XAI & MAS
Multi-actors interaction in XAI
XAI for agent/robots teams
Simulations for XAI
Cognitive and social sciences perspectives on explanations