Keynote 1 – Paola Inverardi, University of L’Aquila, Italy
On the scope of self: privacy and ethics, who is the self?
Abstract: Software systems are increasingly autonomous in making decisions on behalf of potential users. In these systems, the power of self goes beyond the ability of substituting human agents operating on software systems and exceeds the system boundaries invading user prerogatives.
Privacy and ethical issues are at the top of the research agenda in (big) data management and AI, that offer a wide range of techniques often used as key (black box) components of autonomous systems. In this talk I discuss these issues from the software system’s developer perspective that uses such black box components and needs to preserve the user’s will centrality. I explore the use of an approach based on a partially synthesized software architecture to adapt the system to user privacy and ethical preferences. In this way the centrality of the user can be (partially) restored and the “black box self” effects mitigated.
Bio: Paola Inverardi is a professor at University of L’Aquila, where she has been since 1994. From 2013 she is Rector of University of L’Aquila. Her research interests are in the application of rigorous methods to software production in order to improve software quality. Her main research focus is in the field of software architectures and automatic synthesis of connectors for heterogeneous systems, adaptive (mobile) systems. She has received a Honorary Doctorate in computer science at Mälardalen University Swedenand a Honorary Doctorate in engineering at Shibaura University, Tokio Japan. She has received the 2013 IEEE TCSE Distinguished Service Award. She is member of Academia Europea.
Keynote 2 – Marco Barbina, Software Engineering in the Airborne and Space Division of Leonardo S.p.A, Italy
Implementation scenarios of Deep Learning in autonomous Avionics Sensors: Research Challenges and Future Directions
Abstract: A suite of Avionic Sensors on board of a modern platform generates an incredible amount of data that needs to be interpreted in a combined way by an expert human operator. In an operative scenario it is useful to adopt a complete suite of sensors like Radar, Visible, IR or Hyperspectral sensors, Transponders, ComInt, etc., on board of a remotely controlled or an autonomous aircraft and in the near future the aggregated bandwidth required to transmit these data towards a ground based operator will soon exceed the capacity of the datalink as well as the dimension and the complexity of the raw data will exceed the time to handle it. This generates the requirement for the elaboration and analysis of the data set in order to transmit to the ground and present to the operator only the relevant data. Considering this scenario it is logical to ask us which will be the deep learning algorithms most suitable for identification and classification of the features collected by the sensors. Furthermore, data analysis yields poor results unless more sensors are orchestrated and considered as a multi-agent system, each providing a partial view of a complex picture. The technological challenges of the avionic domain are also related to the safety requirements of a domain that just recently has started considering software trustworthy, is still struggling to accept multicore systems and will surely regard with great diffidence the parallel approach needed to allow practical real time Machine Learning and Deep Learning. In this talk I will discuss these open research and technical challenges and try to envision a possible scenario for the future.
Bio: Marco Barbina is Director of Software Engineering in the Airborne and Space Division of Leonardo S.p.A. and manages the teams responsible for the software in several fields ranging from space probes to safety critical and mission critical avionic equipment, from wide band data link and software defined radios to radars, from surveillance to electronic warfare and from flight training simulators to tactical UAVs. Has been Director of Modelling and Simulation Engineering and software coordinator for UAV ground control station products. Marco has received his Master degree in Electronic Engineering at the University of Padua specializing in digital imaging and multilayer video encoding.
Keynote 3 – Gábor Vásárhelyi, MTA-ELTE Statistical and Biological Physics Research Group and CollMot Robotics, Hungary
Title: Bio-inspired collective behaviour of autonomous outdoor drone swarms
Abstract: While individual drones have gone through a tremendous development in the last years towards autonomy and intelligent behavior, functional drone swarms are still very limited in number, due to the new levels of complexity a multi-agent system brings into the picture. The straightforward approach to multi-drone systems builds on the mindset of high individual intelligence and tries to increase the number of drones gradually. Contrarily, we investigate large natural multi-agent systems and create statistical physical models optimized for the minimally required intelligence to perform a given task collectively. With this methodology, the typical bottlenecks of multi-agent systems are in the focus and thus there are less unexpected dynamical issues emerging when system size is increased. With our self-organized, distributed approach, we were able to recreate most of the basic building blocks of collective behavior both in simulation and on actual outdoor drone swarms of dozens of agents: synchronized collective motion in free or confined spaces, collective object avoidance, self-organized formation flights, collective search, chase and escape scenarios and coordinated autonomous drone traffic. In this talk I will give an introduction to our modeling concept and show our various results and applications performed by our autonomous drone fleet.
Bio: Gábor Vásárhelyi was born in Budapest, Hungary, in 1979. He received his MSc in engineering-physics from the Technical University of Budapest, Hungary, in 2003, and his PhD in technical sciences (info-bionics) from Péter Pázmány Catholic University, Hungary, in 2007. Since 2009 he is with Eötvös University, Department of Biological Physics as leader of the Robotic Lab at Tamas Vicsek’s Research Group on collective motion. He is currently a senior research fellow at MTA-ELTE Statistical and Biological Physics Research Group and CEO of CollMot Robotics Ltd., a spin-off dedicated to multi-drone services. His research fields are connected to the collective motion and collective behavior of animals and robots (drones). Further information: www.hal.elte.hu/~vasarhelyi