Invited Speakers

Ross D. King: Automating Chemistry and Biology using Robot Scientists
Ross D. King

Slides of the presentation

Abstract. Science is an excellent application area for AI. A Robot Scientist is a physically implemented robotic system that utilises techniques from artificial intelligence to execute cycles of automated scientific experimentation. A Robot Scientist can automatically execute cycles of: hypothesis formation, selection of efficient experiments to discriminate between hypotheses, execution of experiments using laboratory automation equipment, and analysis of results. The aim of developing Robot Scientists is to better understand science, and to make scientific research more efficient. The Robot Scientist “Adam” was the first machine to autonomously hypothesise and experimentally confirm novel scientific knowledge, while the more recent Robot Scientist “Eve” was developed to automate and integrate drug discovery. The talk gives an overview of these developments and the underlying technologies.

Ross D. King is Professor of Machine Intelligence at the University of Manchester, UK. His main research interests are in the interface between computer science and biology/chemistry. The research achievement he is most proud of is originating the idea of a "Robot Scientist": using laboratory robotics to physically implement a closed-loop scientific discovery system. His Robot Scientist "Adam" was the first machine to hypothesise and experimentally confirm scientific knowledge. His new robot "Eve" is searching for drugs against neglected tropical diseases. His work on this subject has been published in the top scientific journals, Science and Nature, and has received wide publicity. He is also very interested in NP problems, computational economics, and computational aesthetics.

Francesca Rossi: Safety constraints and ethical principles in collective decision making systems
Francesca Rossi

Slides of the presentation

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.

Francesca Rossi is a professor of computer science at the University of Padova, Italy. Currently she is on sabbatical at Harvard with a fellowship of the Radcliffe Institute for Advanced Study. Her research interests include constraint reasoning, preferences, multi-agent systems, computational social choice and artificial intelligence. She has been president of the international association for constraint programming (ACP) and is now the president of IJCAI. She has been program chair of CP 2003 and of IJCAI 2013. She is on the editorial board of Constraints, Artificial Intelligence, AMAI and KAIS, and is Associate Editor in Chief of JAIR. She has published over 160 articles in international journals, proceedings of international conferences or workshops, and as book chapters. She has co-authored a book, edited 16 volumes of conference proceedings, collections of contributions, and special issue of international journals, and has co-edited the Handbook of Constraint Programming.

Molham Aref: Declarative Probabilistic Programming
Molham Aref

Slides of the presentation

Abstract. I will summarize our work on a declarative programming language that offers native language support for expressing predictive (e.g. machine learning) and prescriptive (e.g. combinatorial optimization) analytics. The presentation gives an overview of the platform and the language. In particular, it focuses on the important role of integrity constraints, which are used not only for maintaining data integrity, but also, for example, for the specification of complex optimization problems and probabilistic programming.

Molham Aref is the founder and CEO of LogicBlox and Predictix. He has over 23 years of experience leading teams that deliver high value predictive and prescriptive analytics solutions to some of the world's largest enterprises. Previously, he was CEO of Optimi (acquired by Ericsson), a leader in wireless network simulation and optimization and has held senior leadership positions at Retek (now Oracle Retail) and HNC Software (now FICO). He received his Bachelors in Computer Engineering, M.S. in Electrical Engineering, and M.S in Computer Science from Georgia Tech.


Important Dates

Main Conference

Paper submission
May 22, 2015
Acceptance notification
June 22, 2015
Final version due
July 11, 2015
Early Bird Registration
July 29, 2015
KI Conference
September 21 - 25, 2015

Doctoral Consortium

Submission
June 7, 2015
Notification
June 22, 2015
Doctoral Consortium
September 22, 2015

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Springer

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