Sandip Sen
Personal Homepage:
http://www.mcs.utulsa.edu/~sandipCurrent place of employment:
University of TulsaSandip Sen is a Professor of Computer Science in the University of Tulsa with primary research interests in multiagent systems, machine learning, and genetic algorithms. He completed his PhD in the area of intelligent, distributed scheduling from the University of Michigan in December, 1993. He has authored approximately 150 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 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 was the co-chair of the Program Committee of the 5th International Conference on Autonomous Agents held in Montreal Canada in 2001. He regularly reviews papers for major AI journals and serves on the panels of the National Science Foundation for evaluating agent systems related projects. He has chaired multiple workshops and symposia on agent learning and reasoning. He has presented several tutorials on multiagent systems in association with the leading international conferences on autonomous agents and multiagent systems.
Publications by Sandip Sen (bibliography)
» 2007 «
Banerjee, Dipyaman and Sen, Sandip (2007): Reaching Pareto Optimality in Prisoner's Dilemma Using Conditional Joint Action Learning. In Journal of Autonomous Agents and Multiagent Systems, 15 (1) pp. 91-108
“We consider the learning problem faced by two self-interested agents
repeatedly playing a general-sum stage game.We assume that the players can observe
each other’s actions but not the payoffs received by the other player. The concept
of Nash Equilibrium in repeated games provides an individually rational solution for
playing such games and can be achieved by playing the Nash Equilibrium strategy for
the single-shot game in every iteration. Such a strategy, however can sometimes lead
to a Pareto-Dominated outcome for games like Prisoner’s Dilemma. So we prefer
learning strategies that converge to a Pareto-Optimal outcome that also produces a
Nash Equilibrium payoff for repeated two-player, n-action general-sum games. The
Folk Theorem enable us to identify such outcomes. In this paper, we introduce the
Conditional Joint Action Learner (CJAL) which learns the conditional probability of
an action taken by the opponent given its own actions and uses it to decide its next
course of action.We empirically show that under self-play and if the payoff structure
of the Prisoner’s Dilemma game satisfies certain conditions, a CJAL learner, using a
random exploration strategy followed by a completely greedy exploitation technique,
will learn to converge to a Pareto-Optimal solution. We also show that such learning
will generate Pareto-Optimal payoffs in a large majority of other two-player general
sum games. We compare the performance of CJAL with that of existing algorithms
such as WOLF-PHC and JAL on all structurally distinct two-player conflict games
with ordinal payoffs.”
Copyrights may apply
» 2003 «
Airiau, Stéphane, Sen, Sandip and Richard, Grégoire (2003): Strategic Bidding for Multiple Units in Simultaneous and Sequential Auctions. In: HICSS 2003 2003. p. 28. Available online
» 2000 «
Debnath, Sandip, Sen, Sandip and Blackstock, Brent (2000): LawBot: A Multiagent Assistant for Legal Research. In IEEE Internet Computing, 4 (6) pp. 32-37
» 1998 «
Sen, Sandip (1998): Evolution and Learning in Multiagent Systems. In International Journal of Human-Computer Studies, 48 (1) pp. 1-7
Haynes, Thomas and Sen, Sandip (1998): Learning Cases to Resolve Conflicts and Improve Group Behavior. In International Journal of Human-Computer Studies, 48 (1) pp. 31-49
“Groups of agents following fixed behavioral rules can be limited in performance and efficiency. Adaptability and flexibility are key components of intelligent behavior which allow agent groups to improve performance in a given domain using prior problem-solving experience. We motivate the utility of individual learning by group members in the context of overall group behavior. In particular, we propose a framework in which individual group members learn cases from problem-solving experiences to improve their model of other group members. We use a testbed problem from the Distributed Artificial Intelligence literature to show that simultaneous learning by group members can lead to significant improvement in group performance and efficiency over agent groups following static behavioral rules.”
Copyrights may apply
Sen, Sandip, Arora, Neeraj and Roychowdhury, Shounak (1998): Using Limited Information to Enhance Group Stability. In International Journal of Human-Computer Studies, 48 (1) pp. 69-82
“The performance of individual agents in a group depends critically on the quality of information available to them about local and global goals and resources. In general, it is assumed that the more accurate and comprehensive the available information, the better is the expected performance of the individual and the group. This conclusion can be challenged in a number of scenarios. We investigate the use of limited information by agents in choosing between one of several different options, and conclude that if agents are kept ignorant about, or they deliberately ignore, any number of options, the group can converge faster to a stable and optimal configuration. We present a probabilistic analysis that sheds light on the observed phenomenon of quicker system convergence with less global information. This analysis suggests a desirable adaptive behavior on the part of individual agents. Experiments with agents following these adaptive behavior exhibits faster convergence. We also demonstrate how a couple of coalition formation schemes can improve the rate of convergence. A variable coalition formation mechanism is found to be more effective than a static one.”
Copyrights may apply
» 1997 «
Sen, Sandip, Haynes, Thomas and Arora, Neeraj (1997): Satisfying User Preferences while Negotiating Meetings. In International Journal of Human-Computer Studies, 47 (3) pp. 407-427
“Our research agenda focuses on building software agents that can facilitate and streamline group problem solving in organizations. We are particularly interested in developing intelligent agents that can partially automate routine information processing tasks by representing and reasoning with the preferences and biases of associated users. The distributed meeting scheduler is a collection of agents, responsible for scheduling meetings for their respective users. Users have preferences on when they like to meet, e.g. time of day, day of week, status of other invitees, topic of the meeting, etc. The agent must balance such concerns, proposing and accepting meeting times that satisfy as many of these criteria as possible. For example, a user might prefer not to meet at lunchtime unless the president of the company is hosting the meeting. We apply techniques from voting theory to arrive at consensus choices for meeting times while balancing different preferences.”
Copyrights may apply
» 1991 «
Sen, Sandip and Durfee, Edmund H. (1991): A Formal Study of Distributed Meeting Scheduling: Preliminary Results. In: Jong, Peter de (ed.) Proceedings of the Conference on Organizational Computing Systems 1991 November 6-8, 1991, Atlanta, Georgia, USA. pp. 55-68. Available online
“Automating routine organizational tasks, such as meeting scheduling, requires a careful balance between the individual (respecting his or her privacy and personal preferences) and the organization (making efficient use of time and other resources). We argue that meeting scheduling is an inherently distributed process, and that negotiating over meetings can be viewed as a distributed search process. Keeping the process tractable requires introducing heuristics to guide distributed schedulers' decisions about what information to exchange and whether or not to propose the same tentative time for several meetings. While we have intuitions about how such heuristics could affect scheduling performance and efficiency, rigorously verifying these intuitions requires a more formal model of the meeting schedule problem and process. We present our preliminary work toward this goal, as well as experimental results that validate some of the predictions of our formal model. Our model provides a springboard into deeper investigations of important issues in distributed artificial intelligence as well, and we outline our ongoing work in this direction.”
Copyrights may apply
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Mar 16th, 2010
Changes to this page (author)
24 Feb 2010: Enabled abstracts to be shown on Sandip Sen's author page.12 Jun 2009: Author was edited 02 Jun 2009: Author was edited
08 Aug 2007: Author was added to the bibliography (approved by an editor)
08 Aug 2007: Article in Journal/Periodical was added to the page (approved by an editor)
28 Apr 2003: Added the author to the bibliography
