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Keynote Lectures

Robust Human Interaction with Robotic Swarms
Katia Sycara, Carnegie Mellon University, United States

Causal Discovery from Big Data - Mission (Im)possible?
Tom Heskes, Radboud University Nijmegen, Netherlands

Computational Social Choice
Jérôme Lang, Université Paris-Dauphine, France

Multiagent Organizations
Jaime Sichman, University of São Paulo, Brazil

Social Agents
Eric Postma, Tilburg University, Netherlands

 

Robust Human Interaction with Robotic Swarms

Katia Sycara
Carnegie Mellon University
United States
 

Brief Bio

Katia Sycara is a Research Professor in the Robotics Institute at Carnegie Mellon University and holds (part time) the Sixth Century Chair in Computing at the University of Aberdeen, UK. She holds a B.S. in Applied Mathematics from Brown University, M.S. in Electrical Engineering from the University of Wisconsin and Ph.D. in Computer Science from Georgia Institute of Technology. She holds an Honorary Doctorate from the University of the Aegean. Prof. Sycara’s research expertise is in the areas of artificial intelligence and robotics with special emphasis in multi agent systems, case-based reasoning and learning, semantic web technologies, multi-robot systems and human-robot interaction. In particular, she has undertaken fundamental research in automated negotiation, argumentation, coalition formation, game theoretic methods for network security, reasoning about trust and deception, distributed algorithms for combinatorial optimization and control of robotic systems and human control of such systems. She has applied her research in multiple domains including electronic commerce, large scale emergency response, sensor networks, smart energy grids, environmental exploration and reconnaissance. Prof. Sycara is a Fellow of the Institute of Electrical and Electronic Engineers (IEEE), Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and the recipient of the 2002 ACM/SIGART Autonomous Agents Research Award. She is a past member of the Scientific Advisory Board of France Telecom, of the Scientific Advisory Board of the Greek National Center of Scientific Research "Demokritos" Information Technology Division. She has served on a large number of prestigious research evaluation panels of industry and government programs, participated in various National Academies studies, has given numerous invited talks, and has authored or co-authored more than 500 technical papers dealing with her areas of research. She has led multimillion dollar research efforts funded by DARPA, NASA, AFOSR, ONR, ARO, AFRL, NSF and industry. She co-founded the journal "Autonomous Agents and Multiagent Systems”, the premiere journal in this area of computer science and is currently on the editorial board of 5 additional journals. She is a founding director of the International Foundation of Autonomous Agents and Multi-Agent Systems and of the Semantic Web Science Association. She has participated as an expert in standards activities at W3C and Oasis.


Abstract
Autonomously coordinating robotic swarms are attracting increased attention due to recent advances in making robotic platforms robust and cheap. Robotic swarms coordinate autonomously via simple control laws. Such robotic swarms are attractive for large scale applications in unstructured and dynamic environments because of their scalability and robustness to failures, such as loss of swarm members. Potential applications of robotic swarms consisting of hundreds of members are envisioned to include environmental exploration, large scale monitoring of critical infrastructure, border protection, search and rescue, environmental cleanup, agriculture and others. Human operator presence and interaction with unmanned vehicle (UV) systems, is common and necessary. The human in the loop is an important component in real applications of multi-UV systems since humans (a) are better than the swarm at many visual object and pattern recognition tasks, (b) may have out-of-band information, and (c) impart mission and goal changes to the swarm members. However, swarm human monitoring and control presents difficulties due to (1) swarm nonlinear dynamics and scale, (2) limitations imposed by the environment (e.g., limited bandwidth, noise and latency) and (3) cognitive operator limitations. Drawing from multidisciplinary research in control theory, dynamic systems, AI and Human Factors, this talk will present work that addresses the above challenges and identify open problems and opportunities.



 

 

Causal Discovery from Big Data - Mission (Im)possible?

Tom Heskes
Radboud University Nijmegen
Netherlands
 

Brief Bio

Dr. Tom Heskes is a Professor in Artificial Intelligence, and he leads the Machine Learning Group at the Institute for Computing and Information Sciences, Radboud University Nijmegen, the Netherlands. He is further affiliated Principal Investigator at the Donders Institute for Brain, Cognition and Behaviour. Prof Heskes’ research is on artificial intelligence, in particular (Bayesian) machine learning. He works on Bayesian inference (causal discovery, approximate inference, hierarchical modeling); machine learning (multi-task learning, bias-variance decompositions); and neural networks (on-line learning, self-organizing maps, time-series prediction). He is involved in several projects that concern applications in, among others, brain-computer interfaces and neuroimaging, automated reasoning, genomics, and bioinformatics. Prof Heskes has published over 150 peer-reviewed research papers in the above areas. Prof Heskes is the Editor-in-Chief of Neurocomputing and Associate Editor of five other journals.


Abstract
Discovering causal relations from data lies at the heart of most scientific research today. In apparent contradiction with the adagio "correlation does not imply causation", recent theoretical insights indicate that such causal knowledge can also be derived from purely observational data, instead of only from controlled experimentation. In the "big data" era, such observational data is abundant and being able to actually derive causal relationships from very large data sets would open up a wealth of opportunities for improving business, science, government, and healthcare.



 

 

Computational Social Choice

Jérôme Lang
Université Paris-Dauphine
France
 

Brief Bio

Jérôme Lang is a senior researcher ("directeur de recherche") at Centre National de la Recherche Scientifique. Since 2008 he is affiliated with the Laboratoire d'Analyse et de Modélisation de Systèmes d'Aide à la Décision (LAMSADE), Université Paris-Dauphine. From 1991 to 2008 he was a CNRS researcher at Institut de Recherche en Informatique de Toulouse. His research interests span a large part of Artificial Intelligence, especially Knowledge Representation and Multi-Agent Systems. His recent research activities focus on computational social choice.


Abstract
Computational social choice is an interdisciplinary field of study at the interface of social choice theory and computer science, promoting an exchange of ideas in both directions. On the one hand, it is concerned with the application of techniques developed in computer science, such as complexity analysis, algorithm design, or communication protocols, to the study of social choice mechanisms, such as voting procedures or fair division algorithms. On the other hand, computational social choice is concerned with importing concepts from social choice theory into computing. For instance, social welfare orderings originally developed to analyse the quality of resource allocations in human society are equally well applicable to problems in multiagent systems or network design. Computational social choice brings together ideas from computer science, artificial intelligence, logic, political science and economic theory, amongst others. Below we briefly introduce some representative problems that have been studied in the field.



 

 

Multiagent Organizations

Jaime Sichman
University of São Paulo
Brazil
 

Brief Bio

Jaime Simão Sichman is an Associated Professor at University of São Paulo, from where he has obtained both his B.E. and M.E. degrees. He was one of the first students to obtain an European label to his PhD degree, developed at the Institut National Polytechnique de Grenoble (INPG), France, since part of his research was carried out at the Istituto di Psicologia del CNR (currently ISTC), Rome, Italy. More recently, he has spent an abbreviated post-doctoral period at the University of Utrecht, at the Netherlands. His main research focus is multi-agent systems, more particularly social reasoning, organizational reasoning, multi-agent-based simulation, reputation and trust, and interoperability in agent systems. He has advised/co-advised 11 MSc, 9 PhD and several undergraduate students. With other colleagues, he was one of the founders of two subdomains in Multiagent systems, namely Multi-Agent-Based Simulation (MABS) and Coordination, Organization, Institutions and Norms in Agent Systems (COIN), that have originated two successful international workshop series. He has published more than 160 papers in national and international conferences and journals. He is member of the editorial board of the Journal of Artificial Societies and Social Simulation (JASSS), Mediterranean Journal of Artificial Intelligence, Computación y Sistemas, Iberoamerican Journal of Artificial Intelligence and the Knowledge Engineering Review. He has organized several workshops and international conferences and workshops; in particular he was the SBIA/IBERAMIA General Chair (2000), Program Co-Chair (2006), and AAMAS Tutorial Chair (2007) and Program Co-Chair (2009). He was a member of the Brazilian Computer Society Advisory Board between 2005 and 2009, and was the coordinator of its Artificial Intelligence Special Commission (CEIA) between 2000 and 2002. Currently, he is the director of the Centro de Computação Eletrônica (CCE) of the University of São Paulo.

He is a member of ACM since 2007. From 1998 on, he has been advising several teams from his institute that have participated in the ACM International Collegiate Programming Contest (ACM ICPC). He is a reviewer of ACM Transactions on Intelligent Systems and Technology (ACM TIST), and has participated in several Programme Committees of ACM promoted conferences, like ACM Symposium On Applied Computing (ACM SAC) and the ACM International Conference on Intelligent Agent Technology (ACM IAT).


Abstract
In the last years, social and organizational aspects of agency have become a major issue in MAS research. Recent applications of MAS on Web Services, Grid Computing and Ubiquitous Computing enforce the need of using these aspects in order to ensure some social order within these systems. One of the ways to assure such a social order is through the so-called multiagent organizations. Multiagent organizations are of two types: either the organization emerge from the activity of the individual agents or it is designed to facilitate and guide some specific global behavior. In the latter case, systems are characterized by the autonomy of the individual participants that however must be able to collaboratively achieve predetermined global goals, within a globally constrained environment. However, there is still a lack of a comprehensive view of the diverse concepts, models and approaches related to multiagent organizations. Moreover, most designers have doubts about how to put these concepts in practice, i.e., how to program them. This talk aims to give an answer to such questions.



 

 

Social Agents

Eric Postma
Tilburg University
Netherlands
 

Brief Bio

Eric Postma is a professor in Artificial Intelligence at the Tilburg center for Cognition and Communication, Tilburg University, The Netherlands. His main research interest is in computational models of vision and in the analysis of vocal and facial social signals. Professor Postma has published papers in international scientific journals ranging from cognitive science to artificial intelligence. In all his work, the combination of (mainly visual) sensing and machine learning play an important role.   



Abstract
Human intelligence evolved through social interaction. Existing work on artificial agents emphasises intelligent behaviour in the context of problem solving. In this presentation, the development of social skills is argued to be pivotal in the realisation of intelligent agents. Our group studies the dynamic coupling of nonverbal behaviour in interacting human and artificial agents. The results of these studies are presented and the implications for the development of future intelligent agents are sketched.



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