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2020 – today
- 2024
[j21]Keyang He, Prashant Doshi, Bikramjit Banerjee:
Modeling and reinforcement learning in partially observable many-agent systems. Auton. Agents Multi Agent Syst. 38(1): 12 (2024)- [i6]Khadichabonu Valieva, Bikramjit Banerjee:
Quasimetric Value Functions with Dense Rewards. CoRR abs/2409.08724 (2024) - 2023
- [i5]Keyang He, Prashant Doshi, Bikramjit Banerjee:
Latent Interactive A2C for Improved RL in Open Many-Agent Systems. CoRR abs/2305.05159 (2023) - 2022
- [j20]Trung Nguyen
, Bikramjit Banerjee:
Reinforcement learning as a rehearsal for swarm foraging. Swarm Intell. 16(1): 29-58 (2022) - [c41]Saurabh Arora, Prashant Doshi, Bikramjit Banerjee:
Online Inverse Reinforcement Learning with Learned Observation Model. CoRL 2022: 1468-1477 - [c40]Keyang He, Prashant Doshi, Bikramjit Banerjee:
Reinforcement learning in many-agent settings under partial observability. UAI 2022: 780-789 - 2021
- [j19]Saurabh Arora, Prashant Doshi, Bikramjit Banerjee:
I2RL: online inverse reinforcement learning under occlusion. Auton. Agents Multi Agent Syst. 35(1): 4 (2021) - [j18]Roi Ceren, Keyang He, Prashant Doshi, Bikramjit Banerjee:
PALO bounds for reinforcement learning in partially observable stochastic games. Neurocomputing 420: 36-56 (2021) - [j17]Bikramjit Banerjee, Sneha Racharla:
Human-agent transfer from observations. Knowl. Eng. Rev. 36: e2 (2021) - [c39]Keyang He, Bikramjit Banerjee, Prashant Doshi:
Cooperative-Competitive Reinforcement Learning with History-Dependent Rewards. AAMAS 2021: 602-610 - [c38]Saurabh Arora, Prashant Doshi, Bikramjit Banerjee:
Min-Max Entropy Inverse RL of Multiple Tasks. ICRA 2021: 12639-12645 - [i4]Keyang He, Prashant Doshi, Bikramjit Banerjee:
Many Agent Reinforcement Learning Under Partial Observability. CoRR abs/2106.09825 (2021) - 2020
- [i3]Saurabh Arora, Bikramjit Banerjee, Prashant Doshi:
Maximum Entropy Multi-Task Inverse RL. CoRR abs/2004.12873 (2020) - [i2]Keyang He, Bikramjit Banerjee, Prashant Doshi:
Cooperative-Competitive Reinforcement Learning with History-Dependent Rewards. CoRR abs/2010.08030 (2020)
2010 – 2019
- 2019
- [j16]Bikramjit Banerjee, Syamala Vittanala, Matthew Edmund Taylor:
Team learning from human demonstration with coordination confidence. Knowl. Eng. Rev. 34: e12 (2019) - [c37]Vinamra Jain, Prashant Doshi, Bikramjit Banerjee:
Model-Free IRL Using Maximum Likelihood Estimation. AAAI 2019: 3951-3958 - [c36]Saurabh Arora, Prashant Doshi, Bikramjit Banerjee:
Online Inverse Reinforcement Learning Under Occlusion. AAMAS 2019: 1170-1178 - 2018
- [c35]Bikramjit Banerjee:
Autonomous Acquisition of Behavior Trees for Robot Control. IROS 2018: 3460-3467 - [i1]Saurabh Arora, Prashant Doshi, Bikramjit Banerjee:
A Framework and Method for Online Inverse Reinforcement Learning. CoRR abs/1805.07871 (2018) - 2017
- [j15]Daniel S. Brown, Jeffrey Hudack, Nathaniel Gemelli, Bikramjit Banerjee:
Exact and Heuristic Algorithms for Risk-Aware Stochastic Physical Search. Comput. Intell. 33(3): 524-553 (2017) - [j14]Tsz-Chiu Au, Bikramjit Banerjee, Prithviraj Dasgupta, Peter Stone:
Multirobot Systems. IEEE Intell. Syst. 32(6): 3-5 (2017) - [j13]Bikramjit Banerjee, Caleb E. Davis:
Multiagent Path Finding With Persistence Conflicts. IEEE Trans. Comput. Intell. AI Games 9(4): 402-409 (2017) - 2016
- [j12]Landon Kraemer, Bikramjit Banerjee:
Multi-agent reinforcement learning as a rehearsal for decentralized planning. Neurocomputing 190: 82-94 (2016) - [c34]Bikramjit Banerjee, Steven Loscalzo, Daniel Lucas Thompson:
Detection of Plan Deviation in Multi-Agent Systems. AAAI 2016: 2445-2451 - [c33]Roi Ceren, Prashant Doshi, Bikramjit Banerjee:
Reinforcement Learning in Partially Observable Multiagent Settings: Monte Carlo Exploring Policies with PAC Bounds. AAMAS 2016: 530-538 - 2015
- [j11]Bikramjit Banerjee, Jeremy Lyle, Landon Kraemer:
The complexity of multi-agent plan recognition. Auton. Agents Multi Agent Syst. 29(1): 40-72 (2015) - [j10]Bikramjit Banerjee, Landon Kraemer:
Stackelberg Surveillance. Informatica (Slovenia) 39(4) (2015) - 2014
- [j9]Landon Kraemer, Bikramjit Banerjee:
Reinforcement Learning of Informed Initial Policies for Decentralized Planning. ACM Trans. Auton. Adapt. Syst. 9(4): 18:1-18:32 (2014) - [c32]Todd W. Neller, Laura E. Brown, Roger L. West, James E. Heliotis, Sean Strout, Ivona Bezáková, Bikramjit Banerjee, Daniel Lucas Thompson:
Model AI Assignments 2014. AAAI 2014: 3054-3056 - 2013
- [c31]Bikramjit Banerjee:
Pruning for Monte Carlo Distributed Reinforcement Learning in Decentralized POMDPs. AAAI 2013: 88-94 - [c30]Landon Kraemer, Bikramjit Banerjee:
Concurrent reinforcement learning as a rehearsal for decentralized planning under uncertainty. AAMAS 2013: 1291-1292 - 2012
- [j8]Bikramjit Banerjee, Jing Peng:
Strategic best-response learning in multiagent systems. J. Exp. Theor. Artif. Intell. 24(2): 139-160 (2012) - [c29]Bikramjit Banerjee, Jeremy Lyle, Landon Kraemer, Rajesh Yellamraju:
Sample Bounded Distributed Reinforcement Learning for Decentralized POMDPs. AAAI 2012: 1256-1262 - [c28]Landon Kraemer, Bikramjit Banerjee:
Informed Initial Policies for Learning in Dec-POMDPs. AAAI 2012: 2433-2434 - [c27]Bikramjit Banerjee, Jeremy Lyle, Landon Kraemer:
Efficient context free parsing of multi-agent activities for team and plan recognition. AAMAS 2012: 1441-1442 - 2011
- [j7]Bikramjit Banerjee, Landon Kraemer:
Action Discovery for Single and Multi-Agent Reinforcement Learning. Adv. Complex Syst. 14(2): 279-305 (2011) - [c26]Bikramjit Banerjee, Landon Kraemer:
Branch and Price for Multi-Agent Plan Recognition. AAAI 2011: 601-607 - [c25]Prithviraj Dasgupta, Ke Cheng, Bikramjit Banerjee:
Adaptive Multi-robot Team Reconfiguration Using a Policy-Reuse Reinforcement Learning Approach. AAMAS Workshops 2011: 330-345 - 2010
- [j6]Kyle Walsh, Bikramjit Banerjee:
Fast a* with Iterative Resolution for Navigation. Int. J. Artif. Intell. Tools 19(1): 101-119 (2010) - [c24]Bikramjit Banerjee, Landon Kraemer:
Search Performance of Multi-Agent Plan Recognition in a General Model. Plan, Activity, and Intent Recognition 2010 - [c23]Bikramjit Banerjee, Landon Kraemer, Jeremy Lyle:
Multi-Agent Plan Recognition: Formalization and Algorithms. AAAI 2010: 1059-1064 - [c22]Bikramjit Banerjee, Landon Kraemer:
Evaluation and Comparison of Multi-agent Based Crowd Simulation Systems. AGS 2010: 53-66 - [c21]Bikramjit Banerjee, Landon Kraemer:
Coalition structure generation in multi-agent systems with mixed externalities. AAMAS 2010: 175-182 - [c20]Bikramjit Banerjee, Landon Kraemer:
Validation of agent based crowd egress simulation. AAMAS 2010: 1551-1552 - [c19]Bikramjit Banerjee, Landon Kraemer:
Action discovery for reinforcement learning. AAMAS 2010: 1585-1586
2000 – 2009
- 2009
- [j5]Bikramjit Banerjee, Ahmed Abukmail, Landon Kraemer:
Layered Intelligence for Agent-based Crowd Simulation. Simul. 85(10): 621-633 (2009) - 2008
- [c18]Bikramjit Banerjee, Matthew Bennett, Mike Johnson, Adel Ali:
Congestion Avoidance in Multi-Agent-based Egress Simulation. IC-AI 2008: 151-157 - [c17]Bikramjit Banerjee, Ahmed Abukmail, Landon Kraemer:
Advancing the Layered Approach to Agent-Based Crowd Simulation. PADS 2008: 185-192 - 2007
- [j4]Bikramjit Banerjee, Jing Peng:
Generalized multiagent learning with performance bound. Auton. Agents Multi Agent Syst. 15(3): 281-312 (2007) - [c16]Bikramjit Banerjee, Peter Stone:
General Game Learning Using Knowledge Transfer. IJCAI 2007: 672-677 - 2006
- [j3]Bikramjit Banerjee, Jing Peng:
Reactivity and Safe Learning in Multi-Agent Systems. Adapt. Behav. 14(4): 339-356 (2006) - [c15]Bikramjit Banerjee, Jing Peng:
RVsigma(t): a unifying approach to performance and convergence in online multiagent learning. AAMAS 2006: 798-800 - 2005
- [c14]Bikramjit Banerjee, Jing Peng:
Efficient No-Regret Multiagent Learning. AAAI 2005: 41-46 - [c13]Bikramjit Banerjee, Jing Peng:
Efficient learning of multi-step best response. AAMAS 2005: 60-66 - [c12]Bikramjit Banerjee:
On the performance of on-line concurrent reinforcement learners. AAMAS 2005: 1371 - [c11]Bikramjit Banerjee, Jing Peng:
Unifying Convergence and No-Regret in Multiagent Learning. LAMAS 2005: 100-114 - 2004
- [j2]Bikramjit Banerjee, Sandip Sen, Jing Peng:
On-policy concurrent reinforcement learning. J. Exp. Theor. Artif. Intell. 16(4): 245-260 (2004) - [c10]Bikramjit Banerjee, Jing Peng:
Performance Bounded Reinforcement Learning in Strategic Interactions. AAAI 2004: 2-7 - [c9]Bikramjit Banerjee, Jing Peng:
The Role of Reactivity in Multiagent Learning. AAMAS 2004: 538-545 - 2003
- [c8]Bikramjit Banerjee, Jing Peng:
Adaptive policy gradient in multiagent learning. AAMAS 2003: 686-692 - 2002
- [c7]Bikramjit Banerjee, Jing Peng:
Convergent Gradient Ascent in General-Sum Games. ECML 2002: 1-9 - [c6]Jing Peng, Bikramjit Banerjee, Douglas R. Heisterkamp:
Kernel Index for Relevance feedback Retrieval. FSKD 2002: 187-191 - 2001
- [c5]Bikramjit Banerjee, Sandip Sen, Jing Peng:
Fast Concurrent Reinforcement Learners. IJCAI 2001: 825-832 - [c4]Doug Warner, J. Neal Richter, Stephen D. Durbin, Bikramjit Banerjee:
Mining user session data to facilitate user interaction with a customer service knowledge base in RightNow Web. KDD 2001: 467-472 - 2000
- [j1]Bikramjit Banerjee, Anish Biswas, Manisha Mundhe, Sandip Debnath, Sandip Sen:
Using Bayesian Networks to Model Agent Relationships. Appl. Artif. Intell. 14(9): 867-879 (2000) - [c3]Rajatish Mukherjee, Bikramjit Banerjee, Sandip Sen:
Learning Mutual Trust. Trust in Cyber-societies 2000: 145-158 - [c2]Bikramjit Banerjee, Sandip Sen:
Selecting partners. Agents 2000: 261-262 - [c1]Bikramjit Banerjee, Sandip Debnath, Sandip Sen:
Combining Multiple Perspectives. ICML 2000: 33-40
Coauthor Index
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last updated on 2024-10-15 00:23 CEST by the dblp team
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