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Tim Baarslag
Centrum Wiskunde & Informatica, Netherlands
Title: Joint decision making through automated negotiation
Abstract: In this talk, I will present my ongoing research on intelligent and autonomous systems that can negotiate in a wide variety of upcoming domains, such as smart energy trading, the Internet of Things, and digital privacy. One of the key challenges in designing a successful automated negotiator is that only limited information is available about the other parties. Therefore, we need to investigate various decision and learning techniques to determine what questions to ask, what offers to make, and when to accept. I will provide some intuition behind some of the solution concepts (e.g. the College Problem, Pandora's Rule, optimal stopping), which have a wide range of potential applications. I will also illustrate the current challenges in automated negotiation, together with a roadmap for the successful design of intelligent negotiation systems.
Bio: Tim Baarslag is a Senior Researcher at CWI (The Dutch research institute for Mathematics and Computer Science) and an Assistant Professor at Utrecht University. He is a Visiting Scholar at Massachusetts Institute of Technology (MIT), a Visiting Associate Professor at Nagoya University of Technology and a Visiting Fellow at the University of Southampton.
Centrum Wiskunde & Informatica, Netherlands
Title: Joint decision making through automated negotiation
Abstract: In this talk, I will present my ongoing research on intelligent and autonomous systems that can negotiate in a wide variety of upcoming domains, such as smart energy trading, the Internet of Things, and digital privacy. One of the key challenges in designing a successful automated negotiator is that only limited information is available about the other parties. Therefore, we need to investigate various decision and learning techniques to determine what questions to ask, what offers to make, and when to accept. I will provide some intuition behind some of the solution concepts (e.g. the College Problem, Pandora's Rule, optimal stopping), which have a wide range of potential applications. I will also illustrate the current challenges in automated negotiation, together with a roadmap for the successful design of intelligent negotiation systems.
Bio: Tim Baarslag is a Senior Researcher at CWI (The Dutch research institute for Mathematics and Computer Science) and an Assistant Professor at Utrecht University. He is a Visiting Scholar at Massachusetts Institute of Technology (MIT), a Visiting Associate Professor at Nagoya University of Technology and a Visiting Fellow at the University of Southampton.
Tim Baarslag studies intelligent and autonomous systems that can collaborate through the process of joint decision making. Practical applications include smart energy trading, the Internet of Things, trading platforms, autonomous vehicles, and digital privacy & security assistants. Tim Baarslag currently investigates how negotiation AI can coordinate multiple deals as part of an NWO Vidi grant, called COMBINE: Coordinating Multi-deal Bilateral Negotiations. His research is featured in Science Magazine, Artificial Intelligence, Wired, BBC Technology of Business, AI Magazine, MIT Technology Review, and New Scientist.
Tim Baarslag graduated from Utrecht University with a MSc in Mathematics and a BSc in Computer Science. He obtained his PhD from Delft University of Technology in 2014 on the topic of intelligent decision support systems for automated negotiation. Between 2014 and 2016, he was a Research Fellow at the University of Southampton, where he worked on negotiation techniques for privacy and consent.
Tim is the leading developer of Genius, a negotiation environment for the design and evaluation of automated negotiators. He is also an organizer of the annual International Automated Negotiating Agent Competition. Tim is a member of The Young Academy of the Royal Netherlands Academy of Arts and Sciences and a founding member of the ACM Future of Computing Academy for outstanding early career researchers. He serves as a PC member in top-level conferences such as AAAI and IJCAI, and as a reviewer in high-ranking journals such as Artificial Intelligence and JAAMAS. He is a recipient of the Cor Baayen Young Researcher Award by The European Research Consortium for Informatics and Mathematics and has been named Academic Pioneer by Elsevier, Young Talent by The Financial Daily, and Science Talent by New Scientist.
Yasser Mohammad
NEC, Japan
Title: Concurrent Negotiation in Supply Chains: Problems, Solutions, and Challenges
Abstract: Automated negotiation research has a long history with early results dating back to the 1950s. Recently, and with the increased reliance on intelligent systems for business operations, it is making steady steps in the industry as an agreement technology that provides more flexibility compared with traditional auctions. This talk focuses on a specific application of automated negotiation in a specific business context: concurrent negotiation in supply chains. We discuss different variations of the problem ranging from negotiation with outside options to full fledged concurrent negotiation with globally defined utility functions. We also presents some of the solutions proposed for each variation, and the challenges that are still to be addressed. We also discuss the Supply Chain Management League, one of ANAC's leagues since 2019 designed to advance research in this area and report some of the lessons learned from running this challenge for four years and our vision for its future.
Bio: Yasser Mohammad is a Principal Researcher at NEC, a researcher at AIST and an Associate Professor at Assiut University. He received his PhD from Kyoto University in 2009. Recipient of the JSPS long-term post-doctoral scholarship in 2012. Runner for the best paper in PRIMA 2020, and receiver of the best paper award in ICCAS 2014, IEEE SII 2011, IEA/AIE 2009 and 2008. Author of two books, six patents and over a hundred research articles in the areas of Robotics, Multiagent Systems and Pattern Recognition. His current focus is on industrial applications of automated negotiation.
NEC, Japan
Title: Concurrent Negotiation in Supply Chains: Problems, Solutions, and Challenges
Abstract: Automated negotiation research has a long history with early results dating back to the 1950s. Recently, and with the increased reliance on intelligent systems for business operations, it is making steady steps in the industry as an agreement technology that provides more flexibility compared with traditional auctions. This talk focuses on a specific application of automated negotiation in a specific business context: concurrent negotiation in supply chains. We discuss different variations of the problem ranging from negotiation with outside options to full fledged concurrent negotiation with globally defined utility functions. We also presents some of the solutions proposed for each variation, and the challenges that are still to be addressed. We also discuss the Supply Chain Management League, one of ANAC's leagues since 2019 designed to advance research in this area and report some of the lessons learned from running this challenge for four years and our vision for its future.
Bio: Yasser Mohammad is a Principal Researcher at NEC, a researcher at AIST and an Associate Professor at Assiut University. He received his PhD from Kyoto University in 2009. Recipient of the JSPS long-term post-doctoral scholarship in 2012. Runner for the best paper in PRIMA 2020, and receiver of the best paper award in ICCAS 2014, IEEE SII 2011, IEA/AIE 2009 and 2008. Author of two books, six patents and over a hundred research articles in the areas of Robotics, Multiagent Systems and Pattern Recognition. His current focus is on industrial applications of automated negotiation.
Lirong Xia
Rensselaer Polytechnic Institute, USA
Title: The Smoothed and Semi-Random Possibilities of Social Choice
Abstract: Social choice studies how to aggregate agents' preferences to make a collective decision. It plays a critical role in many group decision making scenarios in human society as well as in multi-agent systems. The difficulty of designing desirable social choice mechanisms has been widely acknowledged, partly due to the wide presence of worst-case paradoxes and impossibility theorems. While there is a large body of literature on using average-case analysis to circumvent the impossibilities, the models in previous work were criticized for being unrealistic, and technical tools to go beyond a few voting rules and a few distributions are lacking. We take a worst average-case approach by proposing a natural, general, and more realistic semi-random model that resembles the celebrated smoothed analysis under semi-random models. We characterize the conditions and rates for the semi-random likelihood of Condorcet's paradox, the ANR impossibility theorem, and the Gibbard-Satterthwaite theorem to vanish, by representing them as unions of polyhedra and characterizing the semi-random likelihood for a Poisson Multinomial Variable to be in the polyhedra. Straightforward applications of our theorems to the Impartial Culture distribution address long-standing open questions. Our results illustrate the smoothed and semi-random possibilities of social choice, and help build a more realistic foundation of social choice that goes beyond worst cases.
Bio: Lirong Xia is an associate professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI). He was a CRCS fellow and NSF CI Fellow at the Center for Research on Computation and Society at Harvard University. He received his Ph.D. in Computer Science and M.A. in Economics from Duke University. His research focuses on the intersection of computer science and microeconomics. He is the recipient of an NSF CAREER award, a Simons-Berkeley Research Fellowship, a Rensselaer James M. Tien'66 Early Career Award, and was named as one of "AI's 10 to watch" by IEEE Intelligent Systems.
Rensselaer Polytechnic Institute, USA
Title: The Smoothed and Semi-Random Possibilities of Social Choice
Abstract: Social choice studies how to aggregate agents' preferences to make a collective decision. It plays a critical role in many group decision making scenarios in human society as well as in multi-agent systems. The difficulty of designing desirable social choice mechanisms has been widely acknowledged, partly due to the wide presence of worst-case paradoxes and impossibility theorems. While there is a large body of literature on using average-case analysis to circumvent the impossibilities, the models in previous work were criticized for being unrealistic, and technical tools to go beyond a few voting rules and a few distributions are lacking. We take a worst average-case approach by proposing a natural, general, and more realistic semi-random model that resembles the celebrated smoothed analysis under semi-random models. We characterize the conditions and rates for the semi-random likelihood of Condorcet's paradox, the ANR impossibility theorem, and the Gibbard-Satterthwaite theorem to vanish, by representing them as unions of polyhedra and characterizing the semi-random likelihood for a Poisson Multinomial Variable to be in the polyhedra. Straightforward applications of our theorems to the Impartial Culture distribution address long-standing open questions. Our results illustrate the smoothed and semi-random possibilities of social choice, and help build a more realistic foundation of social choice that goes beyond worst cases.
Bio: Lirong Xia is an associate professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI). He was a CRCS fellow and NSF CI Fellow at the Center for Research on Computation and Society at Harvard University. He received his Ph.D. in Computer Science and M.A. in Economics from Duke University. His research focuses on the intersection of computer science and microeconomics. He is the recipient of an NSF CAREER award, a Simons-Berkeley Research Fellowship, a Rensselaer James M. Tien'66 Early Career Award, and was named as one of "AI's 10 to watch" by IEEE Intelligent Systems.
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