EXTRAAMAS2019: EXplainable TRansparent Autonomous Agent and Multi-Agent Systems Concordia University Montreal, Canada, May 13-17, 2019 |
Conference website | https://extraamas.ehealth.hevs.ch/ |
Submission link | https://easychair.org/conferences/?conf=extraamas2019 |
Submission deadline | February 20, 2019 |
Human decisions are increasingly relying on Artificial Intelligence (AI) techniques implementing autonomous decision making and distributed problem-solving. However, reasoning and dynamics powering such systems are becoming increasingly opaque. Therefore, the societal awareness about the lack of transparency and the need for explainability is rising. As a consequence, new legal constraints and grant solicitations have been defined to enforce transparency and explainability in IT systems. An example is the new General Data Protection Regulation (GDPR) which became effective in Europe in May 2018. Emphasizing the need for transparency in AI systems, recent studies pointed out that equipping intelligent systems with explanative abilities has a positive impact on users, (e.g., contributing to overcome discomfort, confusion, and self-deception due to the lack of understanding). For all these reasons, Explainable Artificial Intelligence (XAI) has recently re-emerged and is considered to be a hot topic in AI, attracting research from domains such as machine learning, robot planning, and multi-agent systems.
Agents and Multi-Agent Systems (MAS) can have two core contributions for XAI. The first is in the context of personal intelligent systems providing tailored and personalized feedback (e.g., recommendations and coaching systems). Autonomous agent and multi-agent approaches have recently gained noticeable results and scientific relevance in different research domains (e.g., e-health, UAVs, smart environments). However, despite possibly being correct, the outcomes of such agent-based systems, as well as their impact and effect on users, can be negatively affected by the lack of clarity and explainability of their dynamics and rationality. Nevertheless, if explainable, their understanding, reliability, and acceptance can be enhanced. In particular, user personal features (e.g., user context, expertise, age, and cognitive abilities), which are already used to compute the outcome, can be employed in the explanation process providing a user-tailored solution. The second axis is agent/robot teams or mixed human-agent teams. In this context, succeeding in collaboration necessitates a mutual understanding of the status of other agents/users/ their capacities and limitations. This ensures efficient teamwork and avoids potential dangers caused by misunderstandings. In such a scenario, explainability goes beyond single human-agent settings into agent-agent or even mixed agent-human team explainability.
Submission Guidelines
All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:
Submissions will be reviewed by at least three members of the programme committee, who are experts in the field. The acceptance of the submitted papers will depend on their quality, relevance, and originality.
To conduct this process, the chairs will rely on easychair to make the reviewing procedure traceable, transparent and accessible. In the case of accepted papers characterized by relevant demands (e.g., clarifications, changes, corrections) set by the reviewers, the final acceptance will be subject to their accomplishment.
Committees
Program Committee
- Andrea Omicini, Università di Bologna, Italy.
- Ofra Amir Technion IE&M, Israel.
- Joost Broekens TU Delft, Netherlands.
- Olivier Boissier ENS- Mines, Saint-Etienne, France.
- Juan Carlos Nieves Sanchez Umea University, Sweden.
- Tathagata Chakraborti, IBM Research AI, USA.
- Salima Hassas, Lyon 1, France.
- Gauthier Picard, EMSE Saint-Etienne, France.
- Jean-Guy Mailly, Laboratoire d'Informatique de Paris Descartes (LIPADE), 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, Umea University, Sweden.
- Grégory Bonnet, University of Caen, France.
- Jean-Paul Calbimonte, HES-SO Valais-Wallis, Switzerland.
- Sarath Sreedharan, Arizona State University, USA.
- Brent Harrison, Georgia Institute of technology, USA.
- Koen Hindriks, VU, Netherlands.
- Laëtitia Matignon, University Lyon 1, France.
- Simone Stumpf, London City University, UK.
- Michael W. Floyd, Knexus Research, USA.
- Kim Baraka, CMU, USA.
- Dustin Dannenhauer, Naval Research Lab, USA.
- Daniele Magazzeni, King’s college, UK.
- Cesar A. Tacla, UTFPR/Curitiba, BR.
- Stefano Bromuri, University of the Netherlands, Netherlands.
- Rob Wortham, University of Bath, Netherlands.
Organizing committee
- Prof. Kary Främling
- Dr. Davide Calvaresi
- Dr. Amro Najjar
- Prof. Michael Schumacher
Contact
See https://extraamas.ehealth.hevs.ch/index.html