Sen, Sandip


Modeling the Emergence and Convergence of Norms

AAAI Conferences

In many multi-agent systems, the emergence of norms is the primary factor that determines overall behavior and utility. Agent simulations can be used to predict and study the development of these norms. However, a large number of simulations is usually required to provide an accurate depiction of the agents’ behavior, and some rare contingencies may still be overlooked completely. The cost and risk involvedwith agent simulations can be reduced by analyzing a system theoretically and producing models of its behavior. We use such a theoretical approach to examine the dynamics of a population of agents playing a coordination game to determine all the norms to which the society can converge, and develop a system of linear recurrence relations that predict how frequently each of these norms will be reached, as well as the average convergence time. This analysis produces certain guarantees about system behavior that canot be provided by a purely empirical approach, and can be used to make predictions about the emergence of norms that numerically match those obtained through large-scale simulations.


Social Instruments for Robust Convention Emergence

AAAI Conferences

We present the notion of Social Instruments as mechanisms that facilitate the emergence of conventions from repeated interactions between members of a society. Specifically, we focus on two social instruments: rewiring and observation. Our main goal is to provide agents with tools that allow them to leverage their social network of interactions when effectively addressing coordination and learning problems, paying special attention to dissolving meta-stable subconventions. Initial experiments throw some light on how Self-Reinforcing Substructures (SRS) in the network prevent full convergence, resulting in reduced convergence rates. The use of an effective composed social instrument (observation + rewiring) allow agents to eliminate the subconventions that otherwise remained meta-stable.


Improving Search In Social Networks by Agent Based Mining

AAAI Conferences

Users share and access large volumes of information on social networking sites like Facebook, Flickr, del.icio.us, etc. Whereas a few of these sites have generic, impersonal searching mechanisms, we have developed an agent-based framework that mines the social network of a user to improve search results. Our Social Network-based Item Search (SNIS) system uses agents that utilize the connections of a user in the social network to facilitate the search for items of interest. Our approach generates targeted search results that can improve the precision of the result returned from a user's query. We have implemented the SNIS agent-based framework in Flickr, a photo-sharing social network, for searching for photos by using tag lists as search queries. We discuss the architecture of SNIS, motivate the searching scheme used, and demonstrate the effectiveness of the SNIS approach by presenting results. We also show how SNIS can be utilized for expertise location.


Reports on the AAAI 1999 Workshop Program

AI Magazine

The AAAI-99 Workshop Program (a part of the sixteenth national conference on artificial intelligence) was held in Orlando, Florida. Each workshop was limited to approximately 25 to 50 participants. Participation was by invitation from the workshop organizers. The workshops were Agent-Based Systems in the Business Context, Agents' Conflicts, Artificial Intelligence for Distributed Information Networking, Artificial Intelligence for Electronic Commerce, Computation with Neural Systems Workshop, Configuration, Data Mining with Evolutionary Algorithms: Research Directions (Jointly sponsored by GECCO-99), Environmental Decision Support Systems and Artificial Intelligence, Exploring Synergies of Knowledge Management and Case-Based Reasoning, Intelligent Information Systems, Intelligent Software Engineering, Machine Learning for Information Extraction, Mixed-Initiative Intelligence, Negotiation: Settling Conflicts and Identifying Opportunities, Ontology Management, and Reasoning in Context for AI Applications.


Reports on the AAAI 1999 Workshop Program

AI Magazine

The AAAI-99 Workshop Program (a part of the sixteenth national conference on artificial intelligence) was held in Orlando, Florida. The program included 16 workshops covering a wide range of topics in AI. Each workshop was limited to approximately 25 to 50 participants. Participation was by invitation from the workshop organizers. The workshops were Agent-Based Systems in the Business Context, Agents' Conflicts, Artificial Intelligence for Distributed Information Networking, Artificial Intelligence for Electronic Commerce, Computation with Neural Systems Workshop, Configuration, Data Mining with Evolutionary Algorithms: Research Directions (Jointly sponsored by GECCO-99), Environmental Decision Support Systems and Artificial Intelligence, Exploring Synergies of Knowledge Management and Case-Based Reasoning, Intelligent Information Systems, Intelligent Software Engineering, Machine Learning for Information Extraction, Mixed-Initiative Intelligence, Negotiation: Settling Conflicts and Identifying Opportunities, Ontology Management, and Reasoning in Context for AI Applications.


The AAAI Spring Symposia

AI Magazine

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, held the 1998 Spring Symposium Series on 23 to 25 March at Stanford University. The topics of the eight symposia were (1) Applying Machine Learning to Discourse Processing, (2) Integrating Robotic Research: Taking the Next Leap, (3) Intelligent Environments, (4) Intelligent Text Summarization, (5) Interactive and Mixed-Initiative Decision-Theoretic Systems, (6) Multimodal Reasoning, (7) Prospects for a Common-Sense Theory of Causation, and (8) Satisficing Models.


The AAAI Spring Symposia

AI Magazine

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, held the 1998 Spring Symposium Series on 23 to 25 March at Stanford University. The topics of the eight symposia were (1) Applying Machine Learning to Discourse Processing, (2) Integrating Robotic Research: Taking the Next Leap, (3) Intelligent Environments, (4) Intelligent Text Summarization, (5) Interactive and Mixed-Initiative Decision-Theoretic Systems, (6) Multimodal Reasoning, (7) Prospects for a Common-Sense Theory of Causation, and (8) Satisficing Models.


The 1996 AAAI Spring Symposia Reports

AI Magazine

The Association for the Advancement of Artificial Intelligence held its 1996 Spring Symposia Series on March 27 to 29 at Stanford University. This article contains summaries of the eight symposia that were conducted: (1) Acquisition, Learning, and Demonstration: Automating Tasks for Users; (2) Adaptation, Coevolution, and Learning in Multiagent Systems; (3) Artificial Intelligence in Medicine: Applications of Current Technologies; (4) Cognitive and Computational Models of Spatial Representation; (5) Computational Implicature: Computational Approaches to Interpreting and Generating Conversational Implicature; (6) Computational Issues in Learning Models of Dynamic Systems; (7) Machine Learning in Information Access; and (8) Planning with Incomplete Information for Robot Problems.


The 1996 AAAI Spring Symposia Reports

AI Magazine

The Association for the Advancement of Artificial Intelligence held its 1996 Spring Symposia Series on March 27 to 29 at Stanford University. This article contains summaries of the eight symposia that were conducted: (1) Acquisition, Learning, and Demonstration: Automating Tasks for Users; (2) Adaptation, Coevolution, and Learning in Multiagent Systems; (3) Artificial Intelligence in Medicine: Applications of Current Technologies; (4) Cognitive and Computational Models of Spatial Representation; (5) Computational Implicature: Computational Approaches to Interpreting and Generating Conversational Implicature; (6) Computational Issues in Learning Models of Dynamic Systems; (7) Machine Learning in Information Access; and (8) Planning with Incomplete Information for Robot Problems.


IJCAI-95 Workshop on Adaptation and Learning in Multiagent Systems

AI Magazine

The goal of the Workshop on Adaptation and Learning in Multiagent Systems was to focus on research that addresses unique requirements for agents learning and adapting to work in the presence of other agents. Recognizing the applicability and limitations of current machine-learning research as applied to multiagent problems and developing new learning and adaptation mechanisms particularly targeted to this class of problems were the primary research issues that we wanted the authors to address. This article outlines the presentations that were made at the workshop and the success of the workshop in meeting the established goals. Issues that need to be better understood are also presented.