Reaching agreement without global coordination is of fundamental interest in large-scale autonomous multi-agent systems, such as computing, communication, or sensor networks. We investigated an application of the Naming Game [(Baronchelli et al. (2006)] in the context of sensor networks. The Naming Game is a stylized model, employing local communications that captures generic and essential features of the spontaneous emergence of “synthetic” languages in networked agent-based systems. Here, the agreement process corresponds to the autonomous creation of a shared classification scheme (synthetic language). Envision a scenario where mobile or static sensor nodes are deployed in a large spatially-extended region, and the environment is unknown, possibly hostile, the tasks are unforeseeable, and the sensor nodes have no prior classification scheme or language to communicate regarding detecting and sensing objects. Since subsequent efficient operation of the sensor network relies on unique object identification, autonomous development of a common “language” (shared classification scheme) for all nodes is crucial at the exploration stage after network deployment. Another possible application of the Naming Game algorithm is an autonomous key creation scheme for encrypted communication in a community of sensor nodes. Phys. Rev. E 77, (2008)Phys. Rev. E 77, (2008) Language Games in Large-Scale Spatially-Extended Random Networks Gyorgy Korniss, Rensselaer Polytechnic Institute, DMR Figure. Snapshots of the time evolution of the Naming Game in a Random Geometric Graph, modeling sensor networks, during the process of reaching global agreement on the naming of a single object. Different colors correspond to different “words” (black corresponding to agents with more than one word in their list of synonyms). Starting from “scratch” (nodes have an empty word list), global agreement is reached in a time proportional to the number of nodes. The physical analogue of the agreement process, coarsening, is well-known from materials theory.

Education and Training at Rensselaer and Missippi State: graduate students (4): Qiming Lu, Lauren OMalley, Rui Huang (Rensselaer); Poonam Verma (MSU). post-doc (1): Ibrahim Abou Hamad (MSU); pre-college teachers (2): Tammie Borland (Questar III), Ken Wester (MSMS).Questar III Summer Graduate Student Program at the Los Alamos National Laboratory Los Alamos National Laboratory (LANL) is a key national resource for the development and integration of leading- edge science and technology to solve problems of national and global security. Scalable simulations of agent-based systems, complex materials, and critical infrastructure modeling represent one of the recent key directions undertaken by the Laboratory in fulfilling its mission. Collaborations with LANL researchers and visits to LANL have been an integral part of our research and training activities in the past 8 years. Graduate student Qiming Lu (Rensselear) is spending 10 weeks this summer at LANL, studying damage spreading in Random Boolean Networks, under the direction and supervison of Dr. Christof Teuscher. Outreach activities: Questar III New Visions program Questar III New Visions program at Rensselaer: G. Korniss developed presentations for local high-school students to introduce them to basic concepts of simulations and modeling (Spring ). In this program, spanning through the full year, high-school seniors (from around the Albany, NY region) can explore basic concepts in science (and other areas as well), e.g., through guest speakers, mentoring, and a senior project. M. Novotny at Mississippi State University (MSU) is co-teaching (with Mr. Ken Wester) a high-school junior-level class in trig-based physics for students at the state-wide public residential high-school, the Mississippi School for Mathematics and Science (MSMS). Novotny’s lectures bring into the classroom forefront science advances. For example, during discussions of friction, leading-edge research on friction at the nanoscale, slip-stick models, and applications to both earthquake prediction and nano-indentation were discussed. Education, Training, and Outreach Gyorgy Korniss, Rensselaer Polytechnic Institute, DMR