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Date: September 3, 2012 Speaker: Dr. Jeremy Pitt, Department of Electrical Engineering / Institute for Security Science and Technology, Imperial College London Abstract: Open computing systems, from sensor networks to SmartGrids, face the same challenge: a set of autonomous, heterogenous agents, needing to collectivise and distribute resources without a centralised decision-making authority. This challenge is further complicated in an economy of scarcity, when there are fewer resources available than are required in total. We address this challenge through the axiomatisation in computational logic of Elinor's Ostrom's socio-economic principles of enduring institutions for common-pool resource management and Nicholas Rescher's canons of distributive justice for resource allocation. We discuss experimental results with self-organising electronic institutions showing that Ostrom's principles complemented by Rescher's canons are necessary and sufficient conditions for both endurance and fairness. We conclude with some remarks on the implications of these results for computational sustainability. Bio: Jeremy Pitt is Reader in Intelligent Systems in the Department of Electrical & Electronic Engineering at Imperial College London, where he is also Deputy Head of the Intelligent Systems & Networks Group, Director of the Information Systems Engineering course, and an Associate Director of Institute for Security Science and Technology. His research interests focus on the foundations and applications of computational logic in multi-agent systems, in particular agent societies, agent communication languages, and self-organising electronic institutions. He has been an investigator on more than 30 national and European research projects and has published more than 150 articles in journals and conferences. He is a Senior Member of the ACM, a Fellow of the BCS, and a Fellow of the IET, and is an Associate Editor of ACM Transactions on Autonomous and Adaptive Systems.
Date: September 4, 2012 Speaker: Prof Sandip Sen, Tandy School of Computer Science, University of Tulsa, USA Abstract: Agent based systems research and applications have become one of the core paradigms for developing distributed systems and decentralized solutions to myriad real-life problems. As a large number of agents interact locally, often interconnected by domain-specific topologies, using behaviors that were designed for isolated problem solving or limited to interaction between few agents, unpredictable global phenomena, both of the desirable and the catastrophic kind become increasingly common. While the study of emergent phenomena from local interactions between simple behaviors have been widely studied, the systematic study of emergent phenomena from more sophisticated behaviors have been sparse. The literature on emergent, global phenomena in multiagent systems have been almost exclusively limited to experimental investigation of particular domains by varying different environmental and behavioral parameters. While such studies have been able to identify some key unexpected phenomena, analytical approaches that predict the occurrence of such phenomena have been conspicuously absent. On the other hand, molecular biologists, physicists, and researchers in related fields have developed analytical models of mass interactions between entities with highly simplified behaviors. Though these models are meticulously verified by simulations and sometime matched up with real world data, the focus is often on identifying and explaining general trends and is not meant to predict infrequent, high-impact events. We argue for an integrated research approach, where we first perform thorough experimental investigation of different dynamic, large-scale multiagent systems of interest to identify infrequent but turnkey events and phenomena and subsequently develop analytical models to characterize and predict the onset, development, peaking, and subsequent dissolution of such phenomena. We are particularly interested in studying interactions between sophisticated, strategic agent behaviors. We will present a sampling of our results involving domains and phenomena ranging from norm emergence to community evolution and instabilities in systems of distributed learners to opinion dynamics convergence patterns in agent societies. Bio: Sandip Sen is a Professor in the Tandy School of Computer Science at the University of Tulsa, USA with primary research interests in multiagent systems, machine learning, and evolutionary computation. He completed his PhD in the area of intelligent, distributed scheduling from the University of Michigan in December, 1993. He has authored approximately 250 papers in workshops, conferences, and journals in several areas of artificial intelligence. In 1997 he received the prestigious CAREER award given to outstanding young faculty by the US National Science Foundation. He has served on the program committees of most major national and international conferences in the field of intelligent agents including AAAI, IJCAI, ICMAS, AA, AAMAS, ICGA, etc. He served as the co-chair of the Program Committee of the 5th International Conference on Autonomous Agents held in Montreal Canada in 2001 and as a General Co-Chair of AAMAS-2010 held in Toronto, Canada and PRIMA-2012 held in Kuching, Malaysia. He regularly reviews papers for major AI journals and serves on the review panels of the US NSF and other international funding agencies for evaluating agent systems related projects. He has chaired multiple workshops and symposia on agent learning and reasoning. He has presented several tutorials on different multiagent systems topics in association with the leading international conferences on autonomous agents and multiagent systems. |