University of Southern California


ISI Site Signature




Aram Galstyan
 
 
  2016 (11)
Variational Information Maximization for Feature Selection . Gao, S.; Ver Steeg, G.; and Galstyan, A. In Advances in Neural Information Processing Systems, NIPS'16, 2016.
Variational Information Maximization for Feature Selection  [link]Paper   bibtex
Sifting Common Information from Many Variables . Ver Steeg, G.; Gao, S.; Reing, K.; and Galstyan, A. In 2016.
Sifting Common Information from Many Variables  [link]Paper   bibtex
Using Social Media, Online Social Networks, and Internet Search as Platforms for Public Health Interventions: A Pilot Study. Huesch, M. D.; Galstyan, A.; Ong, M. K.; and Doctor, J. N. Health Services Research, 51: 1273--1290. 2016.
Using Social Media, Online Social Networks, and Internet Search as Platforms for Public Health Interventions: A Pilot Study [link]Paper   doi   bibtex
Predicting online extremism, content adopters, and interaction reciprocity. Ferrara, E.; Wang, W.; Varol, O.; Flammini, A.; and Galstyan, A. In 2016.
Predicting online extremism, content adopters, and interaction reciprocity [link]Paper   bibtex
Toward Interpretable Topic Discovery via Anchored Correlation Explanation . Ver Steeg, G.; Gao, S.; Reing, K.; and Galstyan, A. In ICML Workshop on Human Interpretability in Machine Learning (WHI'16), 2016.
Toward Interpretable Topic Discovery via Anchored Correlation Explanation  [link]Paper   bibtex
Latent Space Models for Multimodal Social Data. Cho, Y. S.; Ferrara, E.; Ver Steeg, G.; and Galstyan, A. In WWW'16, 2016.
Latent Space Models for Multimodal Social Data [link]Paper   bibtex
Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text . Garg, S.; Galstyan, A.; Hermjakob, U.; and Marcu, D. In AAAI'16, 2016.
Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text  [link]Paper   bibtex
The Information Sieve . Ver Steeg, G.; and Galstyan, A. In ICML'16, 2016.
The Information Sieve  [link]Paper   bibtex
Modeling Concept Dependencies in a Scientific Corpus . Gordon, J.; Zhu, L.; Galstyan, A.; Natarajan, P.; and Burns, G. In ACL'16, 2016.
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The DARPA Twitter Bot Challenge. Subrahmanian, V. S.; Azaria, A.; Durst, S.; Kagan, V.; Galstyan, A.; Lerman, K.; Zhu, L.; Ferrara, E.; Flammini, A.; Menczer, F.; Waltzman, R.; Stevens, A.; Dekhtyar, A.; Gao, S.; Hogg, T.; Kooti, F.; Liu, Y.; Varol, O.; Shiralkar, P.; Vydiswaran, V. G. V.; Mei, Q.; and Huang, T. IEEE Computer Magazine, 49: 38-46. June 2016.
The DARPA Twitter Bot Challenge [link]Paper   bibtex
Emergence of Leadership in Communication. Allahverdyan, A. E.; and Galstyan, A. PLOS One, accepted. 2016.
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  2015 (8)
Estimating Mutual Information by Local Gaussian Approximation . Gao, S.; Ver Steeg, G.; and Galstyan, A. In Proc. of 31st Conference on Uncertainty in Artificial Intelligence (UAI'15), 2015.
Estimating Mutual Information by Local Gaussian Approximation  [pdf]Paper   bibtex
Memory-induced mechanism for self-sustaining activity in networks. Allahverdyan, A. E.; Steeg, G. V.; and Galstyan, A. Phys. Rev. E, 92: 062824. Dec 2015.
Memory-induced mechanism for self-sustaining activity in networks [link]Paper   doi   bibtex
Information-Theoretic Characterization of Blood Panel Predictors for Brain Atrophy and Cognitive Decline in the Elderly . Madsen, S. K.; Ver Steeg, G.; Mezher, A.; Jahanshad, N.; Nir, T. M.; Hua, X.; Gutman, B. A.; Galstyan, A.; and Thompson, P. M. In International Symposium on Biomedical Imaging, ISBI, 2015.
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Efficient Estimation of Mutual Information for Strongly Dependent Variables . Gao, S.; Ver Steeg, G.; and Galstyan, A. In AISTATS'15, 2015.
Efficient Estimation of Mutual Information for Strongly Dependent Variables  [link]Paper   bibtex
Maximally Informative Hierarchical Representations of High-Dimensional Data . Ver Steeg, G.; and Galstyan, A. In AISTATS'15, 2015.
Maximally Informative Hierarchical Representations of High-Dimensional Data  [link]Paper   bibtex
Understanding Confounding Effects in Linguistic Coordination: An Information-Theoretic Approach. Gao, S. A. V.; and Greg AND Galstyan, A. PLoS ONE, 10(6): e0130167. 06 2015.
Understanding Confounding Effects in Linguistic Coordination: An Information-Theoretic Approach [link]Paper   doi   bibtex
Using Online Social Media and Social Networks as a Public Health Intervention . Huesch, M.; Doctor, J. N; and Galstyan, A. In CESR-Schaeffer Working Paper No. 2015-011, 2015.
Using Online Social Media and Social Networks as a Public Health Intervention  [link]Paper   bibtex
Active Inference for Binary Symmetric Hidden Markov Models . Allahverdyan, A.; and Galstyan, A. Journal of Statistical Physics, 161(2): 452--466. 2015.
Active Inference for Binary Symmetric Hidden Markov Models  [link]Paper   bibtex
  2014 (10)
Scalable Link Prediction in Dynamic Networks via Non-Negative Matrix Factorization. Zhu, L.; Ver Steeg, G.; and Galstyan, A. In preprint, 2014.
Scalable Link Prediction in Dynamic Networks via Non-Negative Matrix Factorization [link]Paper   bibtex
Discovering Structure in High-Dimensional Data Through Correlation Explanation. Ver Steeg, G.; and Galstyan, A. In Advances in Neural Information Processing Systems, NIPS'14, 2014.
Discovering Structure in High-Dimensional Data Through Correlation Explanation [link]Paper   bibtex
Opinion Dynamics with Confirmation Bias. Allahverdyan, A.; and Galstyan, A. PLoS ONE, 9(7): e99557. 07 2014.
Opinion Dynamics with Confirmation Bias [link]Paper   doi   bibtex
Latent self-exciting point process model for spatial-temporal networks. Cho, Y.; Galstyan, A.; Brantingham, P. J.; and Tita, G. Discrete and Continuous Dynamical Systems - Series B, 19(5): 1335-1354. 2014.
Latent self-exciting point process model for spatial-temporal networks [link]Paper   doi   bibtex
Where and Why Users ``Check In". Cho, Y. S.; Ver Steeg, G.; and Galstyan, A. In AAAI'14, 2014.
Where and Why Users ``Check In" [pdf]Paper   bibtex
Tripartite Graph Clustering for Dynamic Sentiment Analysis on Social Media. Zhu, L.; Galstyan, A.; Cheng, J.; and Lerman, K. In SIGMOD'14, 2014.
Tripartite Graph Clustering for Dynamic Sentiment Analysis on Social Media [link]Paper   bibtex   36 downloads
Demystifying Information-Theoretic Clustering . Ver Steeg, G.; Galstyan, A.; Sha, F.; and DeDeo, S. In Proc. of ICML'14, 2014.
Demystifying Information-Theoretic Clustering  [link]Paper   bibtex   28 downloads
Hidden Markov Models for the Activity Profile of Terrorist Groups. Raghavan, V.; Galstyan, A.; and Tartakovsky, A. G. Annals of Applied Statistics, 7: 1837-2457. 2014.
Hidden Markov Models for the Activity Profile of Terrorist Groups [link]Paper   bibtex   13 downloads
Phase Transitions in Community Detection: A Solvable Toy Model. Ver Steeg, G.; Moore, C.; Galstyan, A.; and Allahverdyan, A. E EPL (Europhysics Letters), 106(4): 48004. 2014.
Phase Transitions in Community Detection: A Solvable Toy Model [link]Paper   bibtex   22 downloads
Modeling Temporal Activity Patterns in Dynamic Social Networks. Raghavan, V.; Steeg, G. V.; Galstyan, A.; and Tartakovsky, A. G. IEEE Transactions on Computational Social Systems, 1(1): 89-107. March 2014.
Modeling Temporal Activity Patterns in Dynamic Social Networks [link]Paper   doi   bibtex
  2013 (4)
Explaining Away Stylistic Coordination . Gao, S.; Ver Steeg, G.; and Galstyan, A. In WIN Workshop, New York, 2013.
Explaining Away Stylistic Coordination  [pdf]Paper   bibtex   3 downloads
Coevolutionary networks of reinforcement-learning agents. Kianercy, A.; and Galstyan, A. Phys. Rev. E, 88: 012815. Jul 2013.
Coevolutionary networks of reinforcement-learning agents [link]Paper   doi   bibtex   6 downloads
Sentiment Prediction using Collaborative Filtering. Kim, J.; Yoo, J.; Lim, H.; Qiu, H.; Kozareva, Z.; and Galstyan, A. In ICWSM'13, 2013.
Sentiment Prediction using Collaborative Filtering [pdf]Paper   bibtex   6 downloads
Statistical Tests for Contagion in Observational Social Network Studies. Ver Steeg, G.; and Galstyan, A. In AISTATS'13, 2013.
Statistical Tests for Contagion in Observational Social Network Studies [link]Paper   bibtex   5 downloads
  2012 (6)
Information-Theoretic Measures of Influence Based on Content Dynamics . Ver Steeg, G.; and Galstyan, A. In in Proc. of WSDM'13, Rome, Italy, 2012.
Information-Theoretic Measures of Influence Based on Content Dynamics  [link]Paper   bibtex   12 downloads
Generative Models for Spatial-Temporal Processes with Applications to Predictive Criminology. Cho, Y. S.; Galstyan, A.; Brantingham, J.; and Tita, G. In 9th Bayesian Modeling Applications, UAI'12, 2012.
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Hidden Markov Models for the Activity Profile of Terrorist Groups. Raghavan, V.; Galstyan, A.; and Tartakovsky, A. G. submitted to Annals of Applied Statistics, . 2012.
Hidden Markov Models for the Activity Profile of Terrorist Groups [link]Paper   bibtex   4 downloads
Information Transfer in Social Media. Ver Steeg, G.; and Galstyan, A. In WWW'12, 2012.
Information Transfer in Social Media [link]Paper   bibtex   15 downloads
Adaptive Agents on Evolving Networks . Kianercy, A.; Galstyan, A.; and Allahverdyan, A. In AAMAS-2012, 2012.
Adaptive Agents on Evolving Networks  [pdf]Paper   bibtex   4 downloads
Dynamics of Boltzmann Q learning in two-player two-action games. Kianercy, A.; and Galstyan, A. Phys. Rev. E, 85: 041145. Apr 2012.
Dynamics of Boltzmann Q learning in two-player two-action games [link]Paper   doi   bibtex   5 downloads
  2011 (8)
Le Chatelier's principle in replicator dynamics. Allahverdyan, A. E.; and Galstyan, A. Phys. Rev. E, 84: 041117. Oct 2011.
Le Chatelier's principle in replicator dynamics [link]Paper   doi   bibtex
Information Transfer in Social Media. Ver Steeg, G.; and Galstyan, A. In Workshop on Information in Networks, 2011.
Information Transfer in Social Media [link]Paper   bibtex   1 download
Comparative Analysis of Viterbi Training and ML Estimation for HMMs. Allahverdyan, A.; and Galstyan, A. In Advances in Neural Information Processing Systems (NIPS), 2011.
Comparative Analysis of Viterbi Training and ML Estimation for HMMs [pdf]Paper   bibtex   2 downloads
Continuous Strategy Replicator Dynamics for Multi--Agent Learning. Galstyan, A. accepted for publication in JAAMAS, . 2011.
Continuous Strategy Replicator Dynamics for Multi--Agent Learning [link]Paper   bibtex   8 downloads
A Sequence of Relaxations Constraining Hidden Variable Models. Ver Steeg, G.; and Galstyan, A. In Proc. of the 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011); Best paper runner up, 2011.
A Sequence of Relaxations Constraining Hidden Variable Models [link]Paper   bibtex   5 downloads
Social Mechanics: An Empirically Grounded Science of Social Media. Lerman, K.; Hogg, T.; Galstyan, A.; and Ver Steeg, G. In The Future of Social Web workshop, ICWSM-11, 2011.
Social Mechanics: An Empirically Grounded Science of Social Media [link]Paper   bibtex
Co-evolution of Selection and Influence in Social Networks. Cho, Y. S.; Ver Steeg, G.; and Galstyan, A. In Proc. of the Twenty-Fifth Conference on Artificial Intelligence (AAAI-11), 2011.
Co-evolution of Selection and Influence in Social Networks [link]Paper   bibtex   15 downloads
Statistical mechanics of semi-supervised clustering in sparse graphs. Ver Steeg, G.; Galstyan, A.; and Allahverdyan, A. E Journal of Statistical Mechanics: Theory and Experiment, 2011(08): P08009. 2011.
Statistical mechanics of semi-supervised clustering in sparse graphs [link]Paper   bibtex   3 downloads
  2010 (4)
Co-Evolving Mixed Membership BlockModels. Cho, Y. S.; Ver Steeg, G.; and Galstyan, A. In NIPS workshopon Networks Across Disciplines, 2010.
Co-Evolving Mixed Membership BlockModels [pdf]Paper   bibtex
Replicator Dynamics of Co-Evolving Networks . Galstyan, A.; Kianercy, A.; and Allahverdyan, A. In AAAI Fall Symposium on Complex Adaptive Systems, Nov 2010.
Replicator Dynamics of Co-Evolving Networks  [pdf]Paper   bibtex   1 download
Community detection with and without prior information. Allahverdyan, A.; Ver Steeg, G.; and Galstyan, A. EPL (Europhysics Letters), 90(1): 18002. 2010.
Community detection with and without prior information [link]Paper   bibtex   3 downloads
Towards Modeling Social and Content Dynamics in Discussion Forums . Kim, J.; and Aram Galstyan, A. In NAACL Workshop on Computational Linguistics in a World of Social Media, Jun 2010.
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  2009 (4)
On Maximum a Posteriori Estimation of Hidden Markov Processes. Allahverdyan, A.; and Galstyan, A. In Proceedings of the 25th International Conference on Uncertainty in Artificial Intelligence, Jun 2009.
On Maximum a Posteriori Estimation of Hidden Markov Processes [link]Paper   bibtex   5 downloads
Clustering with prior information. Allahverdyan, A.; Galstyan, A.; and Ver Steeg, G. In NIPS workshop: Clustering: Science or Art? Towards Principled Approaches, 2009.
Clustering with prior information [pdf]Paper   bibtex   3 downloads
Maximizing influence propagation in networks with community structure. Galstyan, A.; Musoyan, V.; and Cohen, P. Phys. Rev. E, 79(5): 056102. May 2009.
Maximizing influence propagation in networks with community structure [link]Paper   doi   bibtex   1 download
TENTACLES: Self-configuring robotic radio networks in unknown environments. Chiu, H. C. H.; Ryu, B.; Zhu, H.; Szekely, P. A.; Maheswaran, R. T.; Rogers, C. M.; Galstyan, A.; Salemi, B.; Rubenstein, M.; and Shen, W. In IROS, pages 1383-1388, 2009.
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  2008 (4)
Comparing Diffusion Models for Graph--Based Semi--Supervised Learning. Galstyan, A.; and Cohen, P. R. In 6-th International Workshop on Mining and Learning with Graphs, 2008.
Comparing Diffusion Models for Graph--Based Semi--Supervised Learning [link]Paper   bibtex
Analysis of Social Voting Patterns on Digg. Lerman, K.; and Galstyan, A. In Proceedings of the 1st ACM SIGCOMM Workshop on Online Social Networks, 2008.
Analysis of Social Voting Patterns on Digg [link]Paper   bibtex   4 downloads
Influence Propagation in Modular Networks . Galstyan, A.; and Cohen, P. R. In AAAI Symposium on Social Information Processing, 2008.
Influence Propagation in Modular Networks  [link]Paper   bibtex
Comparative Analysis of Top-down and Bottom-up Methodologies for Multi-Agent Systems. Crespi, V.; Galstyan, A.; and Lerman, K. Autonomous Robots, 24(3): 303--313. April 2008.
Comparative Analysis of Top-down and Bottom-up Methodologies for Multi-Agent Systems [pdf]Paper   bibtex
  2007 (2)
Cascading dynamics in modular networks. Galstyan, A.; and Cohen, P. Phys. Rev. E, 75(3): 036109. Mar 2007.
Cascading dynamics in modular networks [link]Paper   doi   bibtex   5 downloads
Empirical Comparison of "Hard" and "Soft" Label Propagation for Relational Classification. Galstyan, A.; and Cohen, P. R. In Proceedings of 17th International Conference Inductive Logic Programming, pages 98-111, 2007.
Empirical Comparison of "Hard" and "Soft" Label Propagation for Relational Classification [pdf]Paper   bibtex
  2006 (2)
Relational Classification Through Three--State Epidemic Dynamics. Galstyan, A.; and Cohen, P. R. In The 9th International Conference on Information Fusion, Florence, Italy, 2006.
Relational Classification Through Three--State Epidemic Dynamics [pdf]Paper   bibtex
Analysis of Dynamic Task Allocation in Multi-Robot Systems. Lerman, K.; Jones, C. V.; Galstyan, A.; and Matarić, M. J. International Journal of Robotics Research, 25(3): 225--242. 2006.
Analysis of Dynamic Task Allocation in Multi-Robot Systems [pdf]Paper   bibtex   3 downloads
  2005 (8)
Inferring Useful Heuristics from the Dynamics of Iterative Relational Classifiers. Galstyan, A.; and Cohen, P. R. In Proceedings of IJCAI-05, 19th International Joint Conference on Artificial Intelligence, 2005.
Inferring Useful Heuristics from the Dynamics of Iterative Relational Classifiers [pdf]Paper   bibtex   2 downloads
Identifying Covert Sub-Networks Through Iterative Node Classification. Galstyan, A.; and Cohen, P. R. In Proceedings of First International Conference on Intelligence Analysis, McLean, VA, 2005.
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A Systematic Approach to Composing and Optimizing Application Workflows. Deelman, E.; Galstyan, A.; Gil, Y.; Hall, M.; Lerman, K.; Nakano, A.; Vashista, P.; and Saltz, J. In Proceedings of Workshop on Patterns in High Performance Computing, 2005.
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Modeling and mathematical analysis of swarms of microscopic robots. Galstyan, A.; Hogg, T.; and Lerman, K. In Proceedings of IEEE Swarm Intelligence Symposium (SIS-2005), Pasadena, CA, jun 2005.
Modeling and mathematical analysis of swarms of microscopic robots [link]Paper   bibtex   3 downloads
Resource Allocation in the Grid with Learning Agents. Galstyan, A.; Czajkowski, K.; and Lerman, K. Journal of Grid Computing, 3(1--2): 91--100. June 2005.
Resource Allocation in the Grid with Learning Agents [pdf]Paper   bibtex
Comparative Analysis of Top-down and Bottom-up Methodologies for Multi-Agent Systems. Crespi, V.; Galstyan, A.; and Lerman, K. In Proceedings of International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-2005) (poster), Utrecht, Netherlands, jun 2005.
Comparative Analysis of Top-down and Bottom-up Methodologies for Multi-Agent Systems [pdf]Paper   bibtex   1 download
A Review of Probabilistic Macroscopic Models for Swarm Robotic Systems. Lerman, K.; Martinoli, A.; and Galstyan, A. In E., S.; and W., S., editor(s), Swarm Robotics Workshop: State-of-the-art Survey, of LNCS, pages 143--152. Springer-Verlag, Berlin Heidelberg, 2005.
A Review of Probabilistic Macroscopic Models for Swarm Robotic Systems [pdf]Paper   bibtex   buy   2 downloads
Analysis of a Stochastic Model of Adaptive Task Allocation in Robots. Galstyan, A.; and Lerman, K. In Breuckner, S.; Serugendo, G. D. M.; Karageorgos, A.; and Nagpal, R., editor(s), Engineering Self Organizing Systems: Methodology and Applicationsy, of LNAI, pages 167, Berlin Heidelberg, 2005. Springer-Verlag
Analysis of a Stochastic Model of Adaptive Task Allocation in Robots [pdf]Paper   bibtex
  2004 (6)
Resource Allocation in the Grid Using Reinforcement Learning. Galstyan, A.; Czajkowski, K.; and Lerman, K. In Proceedings of International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-2004) (poster), New York, NY, pages 1314-1315, Jul 2004.
Resource Allocation in the Grid Using Reinforcement Learning [pdf]Paper   bibtex
Distributed Online Localization in Sensor Networks Using a Moving Target. Galstyan, A.; Krishnamachari, B.; Pattem, S.; and Lerman, K. In Information Processing in Sensor Networks (IPSN-2004), Berkeley, CA, apr 2004.
Distributed Online Localization in Sensor Networks Using a Moving Target [pdf]Paper   bibtex   2 downloads
Resource Allocation and Emergent Coordination in Wireless Sensor Networks. Galstyan, A.; Krishnamachari, B.; and Lerman, K. In Workshop on Sensor Networks at the The 19th National Conference on Artificial Intelligence (AAAI-04), 2004.
Resource Allocation and Emergent Coordination in Wireless Sensor Networks [pdf]Paper   bibtex   3 downloads
Two Paradigms for the Design of Artificial Collectives. Lerman, K.; and Galstyan, A. In Tumer, K.; and Wolpert, D., editor(s), Collectives and Design of Complex Systems, pages 231--256. Springer Verlag, 2004.
Two Paradigms for the Design of Artificial Collectives [pdf]Paper   bibtex   buy   2 downloads
Automatically Modeling Group Behavior of Simple Agents. Lerman, K.; and Galstyan, A. In Agent Modeling Workshop, AAMAS-04. 2004.
Automatically Modeling Group Behavior of Simple Agents [pdf]Paper   bibtex   buy
Hormone-Inspired Self-Organization and Distributed Control of Robotic Swarms. Shen, W.; Will, P.; Galstyan, A.; and Chuong, C. Auton. Robots, 17(1): 93--105. 2004.
Hormone-Inspired Self-Organization and Distributed Control of Robotic Swarms [pdf]Paper   doi   bibtex
  2003 (5)
Macroscopic Analysis of Adaptive Task Allocation in Robots. Lerman, K.; and Galstyan, A. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS-2003), Las Vegas, NV, pages 1951--1956, oct 2003.
Macroscopic Analysis of Adaptive Task Allocation in Robots [pdf]Paper   bibtex
Threshold Behavior in Boolean Network Model for SAT. Bugacov, A.; Galstyan, A.; and Lerman, K. In Proceedings of the International Conference on Artificial Intelligence (IC-AI-2003), Las Vegas, NV, pages 87--92, jun 2003.
Threshold Behavior in Boolean Network Model for SAT [pdf]Paper   bibtex   1 download
Agent Memory and Adaptation in Multi-Agent Systems. Lerman, K.; and Galstyan, A. In Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-2003), Melbourne, Australia, pages 797--803, Jul 2003.
Agent Memory and Adaptation in Multi-Agent Systems [pdf]Paper   bibtex   2 downloads
Resource Allocation Games with Changing Resource Capacities. Galstyan, A.; Kolar, S.; and Lerman, K. In Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-2003), Melbourne, Australia, pages 145--152, 2003.
Resource Allocation Games with Changing Resource Capacities [pdf]Paper   bibtex   2 downloads
A Methodology for Mathematical Analysis of Multi-Agent Systems. Lerman, K.; Galstyan, A.; and Hogg, T. unpublished, . 2003.
A Methodology for Mathematical Analysis of Multi-Agent Systems [link]Paper   bibtex   5 downloads
  2002 (3)
Adaptive Boolean Networks and Minority Games with Time-Dependent Cutoffs. Galstyan, A.; and Lerman, K. Physical Review E, 66: 015103. 2002.
Adaptive Boolean Networks and Minority Games with Time-Dependent Cutoffs [pdf]Paper   bibtex   2 downloads
Mathematical Model of Foraging in a Group of Robots: Effect of Interference. Lerman, K.; and Galstyan, A. Autonomous Robots, 13(2): 127--141. 2002.
Mathematical Model of Foraging in a Group of Robots: Effect of Interference [pdf]Paper   bibtex   1 download
Minority Games and Distributed Coordination in Non-Stationary Environments. Galstyan, A.; and Lerman, K. In Proc. of Int. Joint Conf. on Neural Networks, 2002.
Minority Games and Distributed Coordination in Non-Stationary Environments [pdf]Paper   bibtex
  2001 (2)
A Macroscopic Analytical Model of Collaboration in Distributed Robotic Systems. Lerman, K.; Galstyan, A.; Martinoli, A.; and Ijspeert, A. Artificial Life Journal, 7(4): 375--393. 2001.
A Macroscopic Analytical Model of Collaboration in Distributed Robotic Systems [pdf]Paper   bibtex   1 download
A General Methodology for Mathematical Analysis of Multi-Agent Systems. Lerman, K.; and Galstyan, A. Technical Report ISI-TR-529, University of California, Information Sciences Institute, 2001.
A General Methodology for Mathematical Analysis of Multi-Agent Systems [link]Paper   bibtex