Abstract

In this talk, I will present a new formulation of the V-formation problem for migrating birds in terms of model predictive control (MPC). In this approach, to drive a flock towards a desired formation, an optimal velocity adjustment (acceleration) is performed at each time-step on each bird's current velocity using a model-based prediction window of T time-steps. I will present both centralized and distributed versions of this approach. The optimization criteria used is based on fitness metrics of candidate accelerations that V-formations are known to exhibit. These include velocity matching, clear view, and upwash benefit. This MPC-based approach is validated by showing that for a significant majority of simulation runs, the flock succeeds in forming the desired formation. These results help to better understand the emergent behavior of formation flight, and provide a control strategy for flocks of autonomous aerial vehicles. This talk represents joint work with Radu Grosu, Ashish Tiwari, and Junxing Yang.

Biography

Scott Smolka is a professor in the Department of Computer Science at the State University of New York, Stony Brook. He is also president and co-founder of Reactive Systems, Inc, and he has been on the faculty at SUNY at Stony Brook since 1982. Smolka holds an A.B. and A.M. in Mathematics from Boston University and a Ph.D. in Computer Science from Brown University.

Scott A. Smolka has made fundamental contributions in the areas of process algebra, model checking, probabilistic processes, and cardiac-cell modeling and analysis. He is perhaps best known for the algorithm he and Paris Kanellakis invented for deciding bisimulation. Smolka's research in these areas has resulted in over 150 publications, generating more than 7,000 citations. He has also been PI/Co-PI on grants totaling more than $18M. Moreover, he has extensive experience in building verification tools, including Winston and the Concurrency Factory.

Abstract

The future will see autonomous machines acting in the same environment as humans, in areas as diverse as driving, assistive technology, and health care. Think of self-driving cars, companion robots, and medical diagnosis support systems. We also believe that humans and machines will often need to work together and agree on common decisions. Thus hybrid collective decision making systems will be in great need. In this scenario, both machines and collective decision making systems should follow some form of moral values and ethical principles (appropriate to where they will act but always aligned to humans’), as well as safety constraints. In fact, humans would accept and trust more machines that behave as ethically as other humans in the same environment. Also, these principles would make it easier for machines to determine their actions and explain their behavior in terms understandable by humans. Moreover, often machines and humans will need to make decisions together, either through consensus or by reaching a compromise. This would be facilitated by shared moral values and ethical principles.

In this talk I will describe the main issues in these scenarios and I will argue that preference modelling and reasoning frameworks currently used in AI can be exploited to model also ethical principles in collective decision support systems.

Biography

Francesca Rossi is a professor of computer science at the University of Padova, Italy. Currently she is on leave at the IBM T.J. Watson Research centre and last year she was on sabbatical at Harvard with a Radcliffe fellowship. She has been president of the international association for constraint programming (ACP) and of IJCAI, as well as program chair of CP 2003 and of IJCAI 2013. She is an executive councillor of AAAI and co-chair of the AAAI committee on AI and ethics. She is in the editorial board of Constraints, Artificial Intelligence, AMAI, and KAIS. She is Associate Editor in Chief of JAIR since January 2015.

Her research interests focus on artificial intelligence, specifically they include constraint reasoning, preferences, multi-agent systems, and computational social choice. In the recent years she has expanded her interest to include ethical issues in the development and behaviour of AI systems, in particular for decision support systems for group decision making. She has published over 160 articles in international journals and proceedings of international conferences or workshops, and as book chapters. She has co-authored a book. She has edited 16 volumes, between conference proceedings, collections of contributions, and special issue of international journals. She has co-edited the Handbook of Constraint Programming (Elsevier, 2006).  She has more than 100 co-authors.

Abstract

[Marc Shapiro, Masoud Saeida Ardekani, Gustavo Petri] Comparisons of different consistency models often try to place them in a linear order of strong to weak. However the linear view is clearly inadequate, since it is well known, for instance, that Snapshot Isolation and Serialisability are incomparable. In the interest of a better understanding, we propose a new classification, along three dimensions, related to: (1) a total order of writes, (2) a causal order of reads, and (3) transactional composition of multiple operations. A model may be stronger than another on one dimension and weaker on another. We believe that this new classification scheme is both scientifically sound and has good explicative value.

Biography

Marc Shapiro does his research on distributed computer systems, data replication and consistency algorithms, and distributed garbage collection. He invented the proxy concept, which is now universal on the Internet. He published at SOSP and OSDI, the two most prestigious venues of the area (one of the only two French papers at both venues). He was instrumental in the creation of EuroSys, the main European venue in the area. He authored 86 international publications, 18 recognised software systems, and five patents. Dr Shapiro's research started with a PhD from Université Paul Sabatier for research performed at at LAAS in Toulouse, France (1980), followed by a post-doc at MIT, and a researcher position at CMIRH. He is a researcher at INRIA since 1984. He spent a one-year sabbatical at Cornell (1993—1994), and he led the Cambridge Distributed Systems group at Microsoft Research Cambridge (UK) from 1999 to 2005. He is currently a Senior Researcher for INRIA Paris, in the Regal group, located at LIP6.

Abstract

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Biography

Vincent Danos is Directeur de Recherches at CNRS with the Équipe Preuves, Programmes, Systèmes, an eminent centre for computer science research in France. He is also Chair of Computational Systems Biology at the University of Edinburgh (on sabbatical) and a member of the Board of the Centre de Recherches Interdisciplinaires (CRI) in Paris .

He was: Director of Synthsys the Centre for Synthetic and Systems Biology at Edinburgh in 2012-2013;  external faculty member of the Santa Fe Institute (2007-2009), and visiting Professor at the Harvard Medical School (2006-2009) at the Fontana lab.

He works on clean and scaleable domain-specific modeling/programming languages, mostly, but not only, for systems and synthetic biology. He has a research interest in cross-disciplinary activities and convergence on algorithmic/mathematical structures for modelling (social systems, economical systems, climate, multi-scale plant growth, etc).