Sensor-Based Terrain Acquisition: a "Seed Spreader" Strategy. - Vladimir J. Lumelsky; Snehasis Mukhopadhyay; Kang Sun (IROS, 1989)

Associative learning of Boolean functions. - Snehasis Mukhopadhyay; M. A. L. Thathachar 001 (IEEE Trans. Syst. Man Cybern., 1989)

Dynamic path planning in sensor-based terrain acquisition. - Vladimir J. Lumelsky; Snehasis Mukhopadhyay; Kang Sun (IEEE Trans. Robotics Autom., 1990)

Associative learning in random environments using neural networks. - Kumpati S. Narendra; Snehasis Mukhopadhyay (IEEE Trans. Neural Networks, 1991)

Disturbance rejection in nonlinear systems using neural networks. - Snehasis Mukhopadhyay; Kumpati S. Narendra (IEEE Trans. Neural Networks, 1993)

Adaptive control of nonlinear multivariable systems using neural networks. - Kumpati S. Narendra; Snehasis Mukhopadhyay (Neural Networks, 1994)

A Two-Level Approach to Learning in Nonstationary Environments. - Wai Lam; Snehasis Mukhopadhyay (AI, 1996)

Detection of Shifts in User Interests for Personalized Information Filtering. - Wai Lam; Snehasis Mukhopadhyay; Javed Mostafa; Mathew J. Palakal (SIGIR, 1996)

Adaptive control using neural networks and approximate models. - Kumpati S. Narendra; Snehasis Mukhopadhyay (IEEE Trans. Neural Networks, 1997)

A Multilevel Approach to Intelligent Information Filtering: Model, System, and Evaluation. - Javed Mostafa; Snehasis Mukhopadhyay; Wai Lam; Mathew J. Palakal (ACM Trans. Inf. Syst., 1997)

On designing and implementing a collaborative system using the distributed-object model of Java-RMI. - Rajeev R. Raje; Snehasis Mukhopadhyay; Michael Boyles; Nila Patel; Artur Papiez (Parallel Distributed Comput. Pract., 1998)

A Bidding Mechanism for Web-Based Agents Involved in Information Classification. - Michael Boyles; Javed Mostafa; Snehasis Mukhopadhyay; Mathew J. Palakal; Artur Papiez; Nila Patel; Rajeev R. Raje (World Wide Web, 1998)

Multi-agent Adaptive Dynamic Programming. - Snehasis Mukhopadhyay; Joby Varghese (MICAI, 2000)

A Comparison Between Single-agent and Multi-agent Classification of Documents. - Shengquan Peng; Snehasis Mukhopadhyay; Rajeev R. Raje; Mathew J. Palakal; Javed Mostafa (IPDPS, 2001)

Detecting Gene Relations from MEDLINE Abstracts. - Matthew J. Stephens; Mathew J. Palakal; Snehasis Mukhopadhyay; Rajeev R. Raje; Javed Mostafa (Pacific Symposium on Biocomputing, 2001)

SIFTER-II: a heterogeneous agent society for information filtering. - Rajeev R. Raje; Mingyong Qiao; Snehasis Mukhopadhyay (SAC, 2001)

A Multi-Level Text Mining Method to Extract Biological Relationships. - Mathew J. Palakal; Matthew J. Stephens; Snehasis Mukhopadhyay; Rajeev R. Raje; Simon Rhodes (CSB, 2002)

A comparative study of genetic sequence classification algorithms. - Snehasis Mukhopadhyay; Changhong Tang; Jeffrey Huang; Mulong Yu; Mathew J. Palakal (NNSP, 2002)

An intelligent biological information management system. - Mathew J. Palakal; Snehasis Mukhopadhyay; Javed Mostafa (SAC, 2002)

Concept extraction and association from cancer literature. - Yueyu Fu; Travis Bauer; Javed Mostafa; Mathew J. Palakal; Snehasis Mukhopadhyay (WIDM, 2002)

An intelligent biological information management system. - Mathew J. Palakal; Snehasis Mukhopadhyay; Javed Mostafa; Rajeev R. Raje; Mathias N'Cho; Santosh Mishra (Bioinform., 2002)

Homogeneous Agent-Based Distributed Information Filtering. - Rajeev R. Raje; Mingyong Qiao; Snehasis Mukhopadhyay; Mathew J. Palakal; Shengquan Peng; Javed Mostafa (Clust. Comput., 2002)

Feature Decomposition Architectures for Neural Networks: Algorithms, Error Bounds, and Applications. - Haiying Wang; Snehasis Mukhopadhyay; Shiaofen Fang (Int. J. Neural Syst., 2002)

Neural and adaptive systems: fundamentals through simulations: José C. Principe, Neil R. Euliano and W. Curt Lefebvre; John Wiley & Sons, Inc., USA, 2000, ISBN 0-471-35167-9. - Snehasis Mukhopadhyay (Autom., 2003)

Simulation Studies of Different Dimensions of Users' Interests and their Impact on User Modeling and Information Filtering. - Javed Mostafa; Snehasis Mukhopadhyay; Mathew J. Palakal (Inf. Retr., 2003)

Multi-agent information classification using dynamic acquaintance lists. - Snehasis Mukhopadhyay; Shengquan Peng; Rajeev R. Raje; Mathew J. Palakal; Javed Mostafa (J. Assoc. Inf. Sci. Technol., 2003)

Identification of Biological Relationships from Text Documentsusing Efficient Computational Methods. - Mathew J. Palakal; Matthew J. Stephens; Snehasis Mukhopadhyay; Rajeev R. Raje; Simon Rhodes (J. Bioinform. Comput. Biol., 2003)

Automated Web navigation using multiagent adaptive dynamic programming. - Joby Varghese; Snehasis Mukhopadhyay (IEEE Trans. Syst. Man Cybern. Part A, 2003)

Genetic Sequence Classification and its Application to Cross-Species Homology Detection. - Snehasis Mukhopadhyay; Changhong Tang; Jeffrey Huang; Mathew J. Palakal (J. VLSI Signal Process., 2003)

A learning approach to the database selection problem in the presence of dynamic user interests and database contents. - Pooja Bajracharya; Snehasis Mukhopadhyay (ASIST, 2004)

BioMap: toward the development of a knowledge base of biomedical literature. - Kamal Kumar 002; Mathew J. Palakal; Snehasis Mukhopadhyay; Matthew J. Stephens; Huian Li (SAC, 2004)

COBioSIFTER - A CORBA-Based Distributed Multi-Agent Biological Information Management System. - Rajeev R. Raje; Daocheng Zhu; Snehasis Mukhopadhyay; Liying Tang; Mathew J. Palakal; Javed Mostafa (Clust. Comput., 2004)

Generating association graphs of non-cooccurring text objects using transitive methods. - Niranjan Jayadevaprakash; Snehasis Mukhopadhyay; Mathew J. Palakal (SAC, 2005)

Distributed multi-agent information filtering - A comparative study. - Snehasis Mukhopadhyay; Shengquan Peng; Rajeev R. Raje; Javed Mostafa; Mathew J. Palakal (J. Assoc. Inf. Sci. Technol., 2005)

Automated Metadata Generation and its Application to Biological Association Extraction. - Snehasis Mukhopadhyay; Niranjan Jayadevaprakash (AINA Workshops, 2008)

Multi-way Association Extraction from Biological Text Documents Using Hyper-Graphs. - Snehasis Mukhopadhyay; Mathew J. Palakal; Kalyan Maddu (BIBM, 2008)

Knowledge Extraction and Extrapolation Using Ancient and Modern Biomedical Literature. - Harsha Gopal Goud Vaka; Snehasis Mukhopadhyay (AINA Workshops, 2009)

A Genetic Association Study between Breast Cancer and Osteoporosis Using Transitive Text Mining. - Bi-Hua Cheng; Harsha Gopal Goud Vaka; Snehasis Mukhopadhyay (BIBM, 2009)

Hypotheses Generation Pertaining to Ayurveda Using Automated Vocabulary Generation and Transitive Text Mining. - Harsha Gopal Goud Vaka; Snehasis Mukhopadhyay (NBiS, 2009)

Visualizing a Correlative Multi-level Graph of Biology Entity Interactions. - Qian You; Shiaofen Fang; Snehasis Mukhopadhyay; Harsha Gopal Goud Vaka; Jake Yue Chen (NBiS, 2009)

Comparison of Some Single-agent and Multi-agent Information Filtering Systems on a Benchmark Text Data Set. - Snehasis Mukhopadhyay; Shengquan Peng; Rajeev R. Raje; Mathew J. Palakal; Javed Mostafa (SEKE, 2009)

Reinforcement Learning for Human-Machine Collaborative Optimization: Application in Ground Water Monitoring. - Meghna Babbar-Sebens; Snehasis Mukhopadhyay (SMC, 2009)

To communicate or not to communicate: A decision-theoretic approach to decentralized adaptive control. - Kumpati S. Narendra; Snehasis Mukhopadhyay (ACC, 2010)

Software Service Selection by Multi-level Matching and Reinforcement Learning. - Rajeev R. Raje; Snehasis Mukhopadhyay; Sucheta Phatak; Rashmi Shastri; Lahiru S. Gallege (BIONETICS, 2010)

Text mining for bone biology. - Andrew Hoblitzell; Snehasis Mukhopadhyay; Qian You; Shiaofen Fang; Yuni Xia; Joseph Bidwell (HPDC, 2010)

Decentralized and Partially Decentralized Reinforcement Learning for Distributed Combinatorial Optimization Problems. - Omkar J. Tilak; Snehasis Mukhopadhyay (ICMLA, 2010)

A novel reinforcement learning framework for sensor subset selection. - Omkar J. Tilak; Snehasis Mukhopadhyay; Mihran Tuceryan; Rajeev R. Raje (ICNSC, 2010)

Multi-way association extraction and visualization from biological text documents using hyper-graphs: Applications to genetic association studies for diseases. - Snehasis Mukhopadhyay; Mathew J. Palakal; Kalyan Maddu (Artif. Intell. Medicine, 2010)

Decentralized and partially decentralized reinforcement learning for designing a distributed wetland system in watersheds. - Omkar J. Tilak; Meghna Babbar-Sebens; Snehasis Mukhopadhyay (SMC, 2011)

Partially decentralized reinforcement learning in finite, multi-agent Markov decision processes. - Omkar J. Tilak; Snehasis Mukhopadhyay (AI Commun., 2011)

Multilevel text mining for bone biology. - Omkar J. Tilak; Andrew Hoblitzell; Snehasis Mukhopadhyay; Qian You; Shiaofen Fang; Yuni Xia; Joseph Bidwell (Concurr. Comput. Pract. Exp., 2011)

Decentralized Indirect Methods for Learning Automata Games. - Omkar J. Tilak; Ryan Martin; Snehasis Mukhopadhyay (IEEE Trans. Syst. Man Cybern. Part B, 2011)

Interactive pattern mining on hidden data: a sampling-based solution. - Mansurul Bhuiyan; Snehasis Mukhopadhyay; Mohammad Al Hasan (CIKM, 2012)

Decentralized pursuit learning automata in batch mode. - Vidya Bhushan Singh; Snehasis Mukhopadhyay; Meghna Babbar-Sebens (SCIS&ISIS, 2012)

User Modelling for Interactive Optimization Using Neural Network. - Vidya Bhushan Singh; Snehasis Mukhopadhyay; Meghna Babbar-Sebens (SMC, 2013)

User modeling with limited data: Application to stakeholder-driven watershed design. - Snehasis Mukhopadhyay; Vidya Bhushan Singh; Meghna Babbar-Sebens (SMC, 2014)

A web-based software tool for participatory optimization of conservation practices in watersheds. - Meghna Babbar-Sebens; Snehasis Mukhopadhyay; Vidya Bhushan Singh; Adriana Debora Piemonti (Environ. Model. Softw., 2015)

Fast Reinforcement Learning using multiple models. - Kumpati S. Narendra; Yu Wang 091; Snehasis Mukhopadhyay (CDC, 2016)

Fuzzy and deep learning approaches for user modeling in wetland design. - Andrew Hoblitzell; Meghna Babbar-Sebens; Snehasis Mukhopadhyay (SMC, 2016)

Identification and optimal control of large-scale systems using selective decentralization. - Thanh Nguyen 005; Snehasis Mukhopadhyay (SMC, 2016)

Multidisciplinary Optimization in Decentralized Reinforcement Learning. - Thanh Nguyen 005; Snehasis Mukhopadhyay (ICMLA, 2017)

Selectively decentralized Q-learning. - Thanh Nguyen 005; Snehasis Mukhopadhyay (SMC, 2017)

Uncertainty-Based Deep Learning Networks for Limited Data Wetland User Models. - Andrew Hoblitzell; Meghna Babbar-Sebens; Snehasis Mukhopadhyay (AIVR, 2018)

Interactive Machine Learning by Visualization: A Small Data Solution. - Huang Li; Shiaofen Fang; Snehasis Mukhopadhyay; Andrew J. Saykin; Li Shen 001 (IEEE BigData, 2018)

Machine Learning with Small Data for User Modeling of Watershed Stakeholders Engaged in Interactive Optimization. - Andrew Hoblitzell; Meghna Babbar-Sebens; Snehasis Mukhopadhyay (CSAI/ICIMT, 2018)

Reinforcement Learning Algorithms for Uncertain, Dynamic, Zero-Sum Games. - Snehasis Mukhopadhyay; Omkar J. Tilak; Subir Chakrabarti (ICMLA, 2018)

Two-phase selective decentralization to improve reinforcement learning systems with MDP. - Thanh Nguyen 005; Snehasis Mukhopadhyay (AI Commun., 2018)

Mutual Learning: Part I - Learning Automata. - Kumpati S. Narendra; Snehasis Mukhopadhyay (ACC, 2019)

Non-Stationary Reinforcement-Learning Based Dimensionality Reduction for Multi-objective Optimization of Wetland Design. - Andrew Hoblitzell; Meghna Babbar-Sebens; Snehasis Mukhopadhyay (ICRAI, 2019)

Why the 'selfish' optimizing agents could solve the decentralized reinforcement learning problems. - Thanh Nguyen 005; Snehasis Mukhopadhyay; Meghna Babbar-Sebens (AI Commun., 2019)

Mutual Learning: Part II -Reinforcement Learning. - Kumpati S. Narendra; Snehasis Mukhopadhyay (ACC, 2020)

Interactive Watershed Optimization in the Presence of Spatially-varying and Uncertain Stakeholder Preferences. - Meghna Babbar-Sebens; Kenneth R. Cannady-Shultz; Snehasis Mukhopadhyay (ICHMS, 2020)

An Interactive Approach to Bias Mitigation in Machine Learning. - Hao Wang; Snehasis Mukhopadhyay; Yunyu Xiao; Shiaofen Fang (ICCI*CC, 2021)

Mutual Reinforcement Learning with Heterogenous Agents. - Cameron Reid; Snehasis Mukhopadhyay (SMARTCOMP, 2021)

Mutual Learning in Optimization. - Kumpati S. Narendra; Snehasis Mukhopadhyay; Kasra Esfandiari (SMC, 2022)

Mutual Learning Algorithm for Kidney Cyst, Kidney Tumor and Kidney Stone Diagnosis. - Sabrina Chowdhury; Snehasis Mukhopadhyay; Kumpati S. Narendra (FedCSIS, 2023)

Monitoring Cyber Peer-Led Team Learning: A Multimodal Human-in-the-loop Approach. - Karen D'Souza; Pratibha Varma-Nelson; Shiaofen Fang; Snehasis Mukhopadhyay (HITLAML, 2023)

AI-Augmented Peer Led Team Learning for STEM Education. - Karen D'Souza; Lin Zhu; Pratibha Varma-Nelson; Shiaofen Fang; Snehasis Mukhopadhyay (SACI, 2023)

Mutual Learning for Pattern Recognition. - Sabrina Tarin Chowdhury; Snehasis Mukhopadhyay; Kumpati S. Narendra (SMC, 2023)

Mutual Learning in Optimization - Part II. - Kumpati S. Narendra; Lihao Zheng; Snehasis Mukhopadhyay (ACC, 2024)