Selecting partners. - Bikramjit Banerjee; Sandip Sen (Agents, 2000)

Learning Mutual Trust. - Rajatish Mukherjee; Bikramjit Banerjee; Sandip Sen (Trust in Cyber-societies, 2000)

Combining Multiple Perspectives. - Bikramjit Banerjee; Sandip Debnath; Sandip Sen (ICML, 2000)

Using Bayesian Networks to Model Agent Relationships. - Bikramjit Banerjee; Anish Biswas; Manisha Mundhe; Sandip Debnath; Sandip Sen (Appl. Artif. Intell., 2000)

Fast Concurrent Reinforcement Learners. - Bikramjit Banerjee; Sandip Sen; Jing Peng (IJCAI, 2001)

Mining user session data to facilitate user interaction with a customer service knowledge base in RightNow Web. - Doug Warner; J. Neal Richter; Stephen D. Durbin; Bikramjit Banerjee (KDD, 2001)

Convergent Gradient Ascent in General-Sum Games. - Bikramjit Banerjee; Jing Peng (ECML, 2002)

Kernel Index for Relevance feedback Retrieval. - Jing Peng; Bikramjit Banerjee; Douglas R. Heisterkamp (FSKD, 2002)

Adaptive policy gradient in multiagent learning. - Bikramjit Banerjee; Jing Peng (AAMAS, 2003)

Performance Bounded Reinforcement Learning in Strategic Interactions. - Bikramjit Banerjee; Jing Peng (AAAI, 2004)

The Role of Reactivity in Multiagent Learning. - Bikramjit Banerjee; Jing Peng (AAMAS, 2004)

On-policy concurrent reinforcement learning. - Bikramjit Banerjee; Sandip Sen; Jing Peng (J. Exp. Theor. Artif. Intell., 2004)

Efficient No-Regret Multiagent Learning. - Bikramjit Banerjee; Jing Peng (AAAI, 2005)

On the performance of on-line concurrent reinforcement learners. - Bikramjit Banerjee (AAMAS, 2005)

Efficient learning of multi-step best response. - Bikramjit Banerjee; Jing Peng (AAMAS, 2005)

Unifying Convergence and No-Regret in Multiagent Learning. - Bikramjit Banerjee; Jing Peng (LAMAS, 2005)

RVsigma(t): a unifying approach to performance and convergence in online multiagent learning. - Bikramjit Banerjee; Jing Peng (AAMAS, 2006)

Reactivity and Safe Learning in Multi-Agent Systems. - Bikramjit Banerjee; Jing Peng (Adapt. Behav., 2006)

General Game Learning Using Knowledge Transfer. - Bikramjit Banerjee; Peter Stone 001 (IJCAI, 2007)

Generalized multiagent learning with performance bound. - Bikramjit Banerjee; Jing Peng (Auton. Agents Multi Agent Syst., 2007)

Congestion Avoidance in Multi-Agent-based Egress Simulation. - Bikramjit Banerjee; Matthew Bennett; Mike Johnson; Adel Ali (IC-AI, 2008)

Advancing the Layered Approach to Agent-Based Crowd Simulation. - Bikramjit Banerjee; Ahmed Abukmail; Landon Kraemer (PADS, 2008)

Layered Intelligence for Agent-based Crowd Simulation. - Bikramjit Banerjee; Ahmed Abukmail; Landon Kraemer (Simul., 2009)

Search Performance of Multi-Agent Plan Recognition in a General Model. - Bikramjit Banerjee; Landon Kraemer (Plan, Activity, and Intent Recognition, 2010)

Multi-Agent Plan Recognition: Formalization and Algorithms. - Bikramjit Banerjee; Landon Kraemer; Jeremy Lyle (AAAI, 2010)

Evaluation and Comparison of Multi-agent Based Crowd Simulation Systems. - Bikramjit Banerjee; Landon Kraemer (AGS, 2010)

Coalition structure generation in multi-agent systems with mixed externalities. - Bikramjit Banerjee; Landon Kraemer (AAMAS, 2010)

Validation of agent based crowd egress simulation. - Bikramjit Banerjee; Landon Kraemer (AAMAS, 2010)

Action discovery for reinforcement learning. - Bikramjit Banerjee; Landon Kraemer (AAMAS, 2010)

Fast a* with Iterative Resolution for Navigation. - Kyle Walsh; Bikramjit Banerjee (Int. J. Artif. Intell. Tools, 2010)

Branch and Price for Multi-Agent Plan Recognition. - Bikramjit Banerjee; Landon Kraemer (AAAI, 2011)

Adaptive Multi-robot Team Reconfiguration Using a Policy-Reuse Reinforcement Learning Approach. - Prithviraj Dasgupta; Ke Cheng; Bikramjit Banerjee (AAMAS Workshops, 2011)

Action Discovery for Single and Multi-Agent Reinforcement Learning. - Bikramjit Banerjee; Landon Kraemer (Adv. Complex Syst., 2011)

Sample Bounded Distributed Reinforcement Learning for Decentralized POMDPs. - Bikramjit Banerjee; Jeremy Lyle; Landon Kraemer; Rajesh Yellamraju (AAAI, 2012)

Informed Initial Policies for Learning in Dec-POMDPs. - Landon Kraemer; Bikramjit Banerjee (AAAI, 2012)

Efficient context free parsing of multi-agent activities for team and plan recognition. - Bikramjit Banerjee; Jeremy Lyle; Landon Kraemer (AAMAS, 2012)

Strategic best-response learning in multiagent systems. - Bikramjit Banerjee; Jing Peng (J. Exp. Theor. Artif. Intell., 2012)

Pruning for Monte Carlo Distributed Reinforcement Learning in Decentralized POMDPs. - Bikramjit Banerjee (AAAI, 2013)

Concurrent reinforcement learning as a rehearsal for decentralized planning under uncertainty. - Landon Kraemer; Bikramjit Banerjee (AAMAS, 2013)

Model AI Assignments 2014. - Todd W. Neller; Laura E. Brown; Roger L. West; James E. Heliotis; Sean Strout; Ivona Bezáková; Bikramjit Banerjee; Daniel Lucas Thompson (AAAI, 2014)

Reinforcement Learning of Informed Initial Policies for Decentralized Planning. - Landon Kraemer; Bikramjit Banerjee (ACM Trans. Auton. Adapt. Syst., 2014)

The complexity of multi-agent plan recognition. - Bikramjit Banerjee; Jeremy Lyle; Landon Kraemer (Auton. Agents Multi Agent Syst., 2015)

Stackelberg Surveillance. - Bikramjit Banerjee; Landon Kraemer (Informatica (Slovenia), 2015)

Detection of Plan Deviation in Multi-Agent Systems. - Bikramjit Banerjee; Steven Loscalzo; Daniel Lucas Thompson (AAAI, 2016)

Reinforcement Learning in Partially Observable Multiagent Settings: Monte Carlo Exploring Policies with PAC Bounds. - Roi Ceren; Prashant Doshi; Bikramjit Banerjee (AAMAS, 2016)

Multi-agent reinforcement learning as a rehearsal for decentralized planning. - Landon Kraemer; Bikramjit Banerjee (Neurocomputing, 2016)

Exact and Heuristic Algorithms for Risk-Aware Stochastic Physical Search. - Daniel S. Brown; Jeffrey Hudack; Nathaniel Gemelli; Bikramjit Banerjee (Comput. Intell., 2017)

Multirobot Systems. - Tsz-Chiu Au; Bikramjit Banerjee; Prithviraj Dasgupta; Peter Stone 001 (IEEE Intell. Syst., 2017)

Multiagent Path Finding With Persistence Conflicts. - Bikramjit Banerjee; Caleb E. Davis (IEEE Trans. Comput. Intell. AI Games, 2017)

Autonomous Acquisition of Behavior Trees for Robot Control. - Bikramjit Banerjee (IROS, 2018)

A Framework and Method for Online Inverse Reinforcement Learning. - Saurabh Arora; Prashant Doshi; Bikramjit Banerjee (CoRR, 2018)

Model-Free IRL Using Maximum Likelihood Estimation. - Vinamra Jain; Prashant Doshi; Bikramjit Banerjee (AAAI, 2019)

Online Inverse Reinforcement Learning Under Occlusion. - Saurabh Arora; Prashant Doshi; Bikramjit Banerjee (AAMAS, 2019)

Team learning from human demonstration with coordination confidence. - Bikramjit Banerjee; Syamala Vittanala; Matthew Edmund Taylor (Knowl. Eng. Rev., 2019)

Maximum Entropy Multi-Task Inverse RL. - Saurabh Arora; Bikramjit Banerjee; Prashant Doshi (CoRR, 2020)

Cooperative-Competitive Reinforcement Learning with History-Dependent Rewards. - Keyang He; Bikramjit Banerjee; Prashant Doshi (AAMAS, 2021)

Min-Max Entropy Inverse RL of Multiple Tasks. - Saurabh Arora; Prashant Doshi; Bikramjit Banerjee (ICRA, 2021)

I2RL: online inverse reinforcement learning under occlusion. - Saurabh Arora; Prashant Doshi; Bikramjit Banerjee (Auton. Agents Multi Agent Syst., 2021)

Many Agent Reinforcement Learning Under Partial Observability. - Keyang He; Prashant Doshi; Bikramjit Banerjee (CoRR, 2021)

PALO bounds for reinforcement learning in partially observable stochastic games. - Roi Ceren; Keyang He; Prashant Doshi; Bikramjit Banerjee (Neurocomputing, 2021)

Human-agent transfer from observations. - Bikramjit Banerjee; Sneha Racharla (Knowl. Eng. Rev., 2021)

Online Inverse Reinforcement Learning with Learned Observation Model. - Saurabh Arora; Prashant Doshi; Bikramjit Banerjee (CoRL, 2022)

Reinforcement learning in many-agent settings under partial observability. - Keyang He; Prashant Doshi; Bikramjit Banerjee (UAI, 2022)

Reinforcement learning as a rehearsal for swarm foraging. - Trung Nguyen; Bikramjit Banerjee (Swarm Intell., 2022)

Latent Interactive A2C for Improved RL in Open Many-Agent Systems. - Keyang He; Prashant Doshi; Bikramjit Banerjee (CoRR, 2023)

Robust Individualistic Learning in Many-Agent Systems. - Keyang He; Prashant Doshi; Bikramjit Banerjee (PRIMA, 2024)

Quasimetric Value Functions with Dense Rewards. - Khadichabonu Valieva; Bikramjit Banerjee (CoRR, 2024)

Reinforcement actor-critic learning as a rehearsal in MicroRTS. - Shiron Manandhar; Bikramjit Banerjee (Knowl. Eng. Rev., 2024)

Modeling and reinforcement learning in partially observable many-agent systems. - Keyang He; Prashant Doshi; Bikramjit Banerjee (Auton. Agents Multi Agent Syst., 2024)

Adaptive Human-Robot Collaboration using Type-Based IRL. - Prasanth Sengadu Suresh; Prashant Doshi; Bikramjit Banerjee (UAI, 2025)