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ORCIDPrashant Doshi
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- affiliation: University of Georgia, Athens, USA
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2020 – today
- 2024
[j31]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)- [c118]Aditya Shinde, Prashant Doshi:
Modeling Cognitive Biases in Decision-theoretic Planning for Active Cyber Deception. AAMAS 2024: 1718-1726 - [c117]Prasanth Sengadu Suresh, Siddarth Jain, Prashant Doshi, Diego Romeres:
Open Human-Robot Collaboration using Decentralized Inverse Reinforcement Learning. IROS 2024: 7092-7098 - [c116]Gauri Jain, Pradeep Varakantham, Haifeng Xu, Aparna Taneja, Prashant Doshi, Milind Tambe:
IRL for Restless Multi-armed Bandits with Applications in Maternal and Child Health. PRICAI (5) 2024: 165-178 - [c115]Keyang He, Prashant Doshi, Bikramjit Banerjee:
Robust Individualistic Learning in Many-Agent Systems. PRIMA 2024: 290-305 - [i33]Prasanth Sengadu Suresh, Siddarth Jain, Prashant Doshi, Diego Romeres:
Open Human-Robot Collaboration using Decentralized Inverse Reinforcement Learning. CoRR abs/2410.01790 (2024) - [i32]Tyson Jordan, Pranav Pandey, Prashant Doshi, Ramviyas Parasuraman, Adam Goodie:
Analyzing Human Perceptions of a MEDEVAC Robot in a Simulated Evacuation Scenario. CoRR abs/2410.19072 (2024) - [i31]Yanyu Liu, Yinghui Pan, Yifeng Zeng, Biyang Ma, Prashant Doshi:
Active Legibility in Multiagent Reinforcement Learning. CoRR abs/2410.20954 (2024) - [i30]Gauri Jain, Pradeep Varakantham, Haifeng Xu, Aparna Taneja, Prashant Doshi, Milind Tambe:
IRL for Restless Multi-Armed Bandits with Applications in Maternal and Child Health. CoRR abs/2412.08463 (2024) - [i29]Ehsan Asali, Prashant Doshi:
Visual IRL for Human-Like Robotic Manipulation. CoRR abs/2412.11360 (2024) - 2023
- [j30]Adam Eck
, Leen-Kiat Soh, Prashant Doshi:
Decision making in open agent systems. AI Mag. 44(4): 508-523 (2023) - [c114]Prasanth Sengadu Suresh, Yikang Gui, Prashant Doshi:
Dec-AIRL: Decentralized Adversarial IRL for Human-Robot Teaming. AAMAS 2023: 1116-1124 - [i28]Keyang He, Prashant Doshi, Bikramjit Banerjee:
Latent Interactive A2C for Improved RL in Open Many-Agent Systems. CoRR abs/2305.05159 (2023) - [i27]Yikang Gui, Prashant Doshi:
A Novel Variational Lower Bound for Inverse Reinforcement Learning. CoRR abs/2311.03698 (2023) - [i26]Ehsan Asali, Prashant Doshi, Jin Sun:
MVSA-Net: Multi-View State-Action Recognition for Robust and Deployable Trajectory Generation. CoRR abs/2311.08393 (2023) - 2022
- [c113]Kenneth D. Bogert, Prashant Doshi:
A Hierarchical Bayesian Process for Inverse RL in Partially-Controlled Environments. AAMAS 2022: 145-153 - [c112]Saurabh Arora, Prashant Doshi, Bikramjit Banerjee:
Online Inverse Reinforcement Learning with Learned Observation Model. CoRL 2022: 1468-1477 - [c111]Swaraj Pawar, Prashant Doshi:
Anytime Learning of Sum-Product and Sum-Product-Max Networks. PGM 2022: 49-60 - [c110]Keyang He, Prashant Doshi, Bikramjit Banerjee:
Reinforcement learning in many-agent settings under partial observability. UAI 2022: 780-789 - [c109]Anirudh Kakarlapudi, Gayathri Anil, Adam Eck, Prashant Doshi, Leen-Kiat Soh:
Decision-theoretic planning with communication in open multiagent systems. UAI 2022: 938-948 - [c108]Prasanth Sengadu Suresh, Prashant Doshi:
Marginal MAP estimation for inverse RL under occlusion with observer noise. UAI 2022: 1907-1916 - [i25]Gengyu Zhang, Prashant Doshi:
SIPOMDPLite-Net: Lightweight, Self-Interested Learning and Planning in POSGs with Sparse Interactions. CoRR abs/2202.11188 (2022) - [i24]Kenneth D. Bogert, Yikang Gui, Prashant Doshi:
IRL with Partial Observations using the Principle of Uncertain Maximum Entropy. CoRR abs/2208.06988 (2022) - 2021
- [j29]Saurabh Arora, Prashant Doshi, Bikramjit Banerjee:
I2RL: online inverse reinforcement learning under occlusion. Auton. Agents Multi Agent Syst. 35(1): 4 (2021) - [j28]Saurabh Arora, Prashant Doshi:
A survey of inverse reinforcement learning: Challenges, methods and progress. Artif. Intell. 297: 103500 (2021) - [j27]Roi Ceren, Keyang He, Prashant Doshi, Bikramjit Banerjee:
PALO bounds for reinforcement learning in partially observable stochastic games. Neurocomputing 420: 36-56 (2021) - [j26]Omid Setayeshfar, Christian Adkins, Matthew Jones, Kyu Hyung Lee, Prashant Doshi:
GrAALF: Supporting graphical analysis of audit logs for forensics. Softw. Impacts 8: 100068 (2021) - [c107]Muhammed AbuOdeh, Christian Adkins, Omid Setayeshfar, Prashant Doshi, Kyu Hyung Lee:
A Novel AI-based Methodology for Identifying Cyber Attacks in Honey Pots. AAAI 2021: 15224-15231 - [c106]Hari Teja Tatavarti, Prashant Doshi, Layton Hayes:
Data-Driven Decision-Theoretic Planning using Recurrent Sum-Product-Max Networks. ICAPS 2021: 606-614 - [c105]Keyang He, Bikramjit Banerjee, Prashant Doshi:
Cooperative-Competitive Reinforcement Learning with History-Dependent Rewards. AAMAS 2021: 602-610 - [c104]Aditya Shinde, Prashant Doshi, Omid Setayeshfar:
Cyber Attack Intent Recognition and Active Deception using Factored Interactive POMDPs. AAMAS 2021: 1200-1208 - [c103]Saurabh Arora, Prashant Doshi, Bikramjit Banerjee:
Min-Max Entropy Inverse RL of Multiple Tasks. ICRA 2021: 12639-12645 - [c102]Layton Hayes, Prashant Doshi, Swaraj Pawar, Hari Teja Tatavarti:
State-Based Recurrent SPMNs for Decision-Theoretic Planning under Partial Observability. IJCAI 2021: 2526-2533 - [i23]Keyang He, Prashant Doshi, Bikramjit Banerjee:
Many Agent Reinforcement Learning Under Partial Observability. CoRR abs/2106.09825 (2021) - [i22]Kenneth D. Bogert, Prashant Doshi:
A Hierarchical Bayesian model for Inverse RL in Partially-Controlled Environments. CoRR abs/2107.05818 (2021) - [i21]Prasanth Sengadu Suresh, Prashant Doshi:
Marginal MAP Estimation for Inverse RL under Occlusion with Observer Noise. CoRR abs/2109.07788 (2021) - 2020
- [j25]Prashant Doshi, Piotr J. Gmytrasiewicz, Edmund H. Durfee:
Recursively modeling other agents for decision making: A research perspective. Artif. Intell. 279 (2020) - [c101]Adam Eck, Maulik Shah, Prashant Doshi, Leen-Kiat Soh:
Scalable Decision-Theoretic Planning in Open and Typed Multiagent Systems. AAAI 2020: 7127-7134 - [c100]Nihal Soans, Ehsan Asali, Yi Hong, Prashant Doshi:
SA-Net: Robust State-Action Recognition for Learning from Observations. ICRA 2020: 2153-2159 - [i20]Saurabh Arora, Bikramjit Banerjee, Prashant Doshi:
Maximum Entropy Multi-Task Inverse RL. CoRR abs/2004.12873 (2020) - [i19]Hari Teja Tatavarti, Prashant Doshi, Layton Hayes:
Recurrent Sum-Product-Max Networks for Decision Making in Perfectly-Observed Environments. CoRR abs/2006.07300 (2020) - [i18]Aditya Shinde, Prashant Doshi, Omid Setayeshfar:
Active Deception using Factored Interactive POMDPs to Recognize Cyber Attacker's Intent. CoRR abs/2007.09512 (2020) - [i17]Keyang He, Bikramjit Banerjee, Prashant Doshi:
Cooperative-Competitive Reinforcement Learning with History-Dependent Rewards. CoRR abs/2010.08030 (2020)
2010 – 2019
- 2019
- [j24]Tomoki Nishi, Prashant Doshi, Danil V. Prokhorov:
Merging in Congested Freeway Traffic Using Multipolicy Decision Making and Passive Actor-Critic Learning. IEEE Trans. Intell. Veh. 4(2): 287-297 (2019) - [c99]Vinamra Jain, Prashant Doshi, Bikramjit Banerjee:
Model-Free IRL Using Maximum Likelihood Estimation. AAAI 2019: 3951-3958 - [c98]Saurabh Arora, Prashant Doshi, Bikramjit Banerjee:
Online Inverse Reinforcement Learning Under Occlusion. AAMAS 2019: 1170-1178 - [c97]Adithya Raam Sankar, Prashant Doshi, Adam Goodie:
Evacuate or Not? A POMDP Model of the Decision Making of Individuals in Hurricane Evacuation Zones. UAI 2019: 669-678 - [i16]Nihal Soans, Yi Hong, Prashant Doshi:
SA-Net: Deep Neural Network for Robot Trajectory Recognition from RGB-D Streams. CoRR abs/1905.04380 (2019) - [i15]Omid Setayeshfar, Christian Adkins, Matthew Jones, Kyu Hyung Lee, Prashant Doshi:
GrAALF: Supporting Graphical Analysis of Audit Logs for Forensics. CoRR abs/1909.00902 (2019) - [i14]Adam Eck, Maulik Shah, Prashant Doshi, Leen-Kiat Soh:
Scalable Decision-Theoretic Planning in Open and Typed Multiagent Systems. CoRR abs/1911.08642 (2019) - 2018
- [j23]Kenneth D. Bogert, Prashant Doshi:
Multi-robot inverse reinforcement learning under occlusion with estimation of state transitions. Artif. Intell. 263: 46-73 (2018) - [c96]Maulesh Trivedi, Prashant Doshi:
Inverse Learning of Robot Behavior for Collaborative Planning. IROS 2018: 1-9 - [c95]Agastya Kalra, Abdullah Rashwan, Wei-Shou Hsu, Pascal Poupart, Prashant Doshi, Georgios Trimponias:
Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks. NeurIPS 2018: 6944-6954 - [i13]Saurabh Arora, Prashant Doshi, Bikramjit Banerjee:
A Framework and Method for Online Inverse Reinforcement Learning. CoRR abs/1805.07871 (2018) - [i12]Roi Ceren, Prashant Doshi, Keyang He:
Reinforcement Learning for Heterogeneous Teams with PALO Bounds. CoRR abs/1805.09267 (2018) - [i11]Saurabh Arora, Prashant Doshi:
A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress. CoRR abs/1806.06877 (2018) - 2017
- [j22]Muthukumaran Chandrasekaran, Prashant Doshi, Yifeng Zeng, Yingke Chen:
Can bounded and self-interested agents be teammates? Application to planning in ad hoc teams. Auton. Agents Multi Agent Syst. 31(4): 821-860 (2017) - [j21]Xia Qu, Prashant Doshi:
On the role of fairness and limited backward induction in sequential bargaining games - New behavioral models and analyses. Ann. Math. Artif. Intell. 79(1-3): 205-227 (2017) - [j20]Ekhlas Sonu, Yingke Chen, Prashant Doshi:
Decision-Theoretic Planning Under Anonymity in Agent Populations. J. Artif. Intell. Res. 59: 725-770 (2017) - [c94]Muthukumaran Chandrasekaran, Yingke Chen, Prashant Doshi:
On Markov Games Played by Bayesian and Boundedly-Rational Players. AAAI 2017: 437-443 - [c93]Kenneth D. Bogert, Prashant Doshi:
Scaling Expectation-Maximization for Inverse Reinforcement Learning to Multiple Robots under Occlusion. AAMAS 2017: 522-529 - [c92]Shervin Shahryari, Prashant Doshi:
Inverse Reinforcement Learning Under Noisy Observations. AAMAS 2017: 1733-1735 - [c91]Sina Solaimanpour, Prashant Doshi:
A layered HMM for predicting motion of a leader in multi-robot settings. ICRA 2017: 788-793 - [c90]Muthukumaran Chandrasekaran, Junhuan Zhang, Prashant Doshi, Yifeng Zeng:
Robust Model Equivalence using Stochastic Bisimulation for N-Agent Interactive DIDs. UAI 2017 - [i10]Tomoki Nishi, Prashant Doshi, Michael R. James, Danil V. Prokhorov:
Actor-Critic for Linearly-Solvable Continuous MDP with Partially Known Dynamics. CoRR abs/1706.01077 (2017) - [i9]Tomoki Nishi, Prashant Doshi, Danil V. Prokhorov:
Freeway Merging in Congested Traffic based on Multipolicy Decision Making with Passive Actor Critic. CoRR abs/1707.04489 (2017) - [i8]Shervin Shahryari, Prashant Doshi:
Inverse Reinforcement Learning Under Noisy Observations. CoRR abs/1710.10116 (2017) - 2016
- [j19]Yifeng Zeng, Prashant Doshi, Yingke Chen, Yinghui Pan, Hua Mao, Muthukumaran Chandrasekaran:
Approximating behavioral equivalence for scaling solutions of I-DIDs. Knowl. Inf. Syst. 49(2): 511-552 (2016) - [c89]Muthukumaran Chandrasekaran, Yingke Chen, Prashant Doshi:
Bayesian Markov Games with Explicit Finite-Level Types. AAAI 2016: 4198-4199 - [c88]Muthukumaran Chandrasekaran, Yingke Chen, Prashant Doshi:
Bayesian Markov Games with Explicit Finite-Level Types. AAAI Workshop: Multiagent Interaction without Prior Coordination 2016 - [c87]Mazen Melibari, Pascal Poupart, Prashant Doshi:
Decision Sum-Product-Max Networks. AAAI 2016: 4234-4235 - [c86]Roi Ceren, Prashant Doshi, Bikramjit Banerjee:
Reinforcement Learning in Partially Observable Multiagent Settings: Monte Carlo Exploring Policies with PAC Bounds. AAMAS 2016: 530-538 - [c85]Kenneth D. Bogert, Jonathan Feng-Shun Lin, Prashant Doshi, Dana Kulic:
Expectation-Maximization for Inverse Reinforcement Learning with Hidden Data. AAMAS 2016: 1034-1042 - [c84]Mazen A. Melibari, Pascal Poupart, Prashant Doshi:
Sum-Product-Max Networks for Tractable Decision Making: (Extended Abstract). AAMAS 2016: 1419-1420 - [c83]Mazen Melibari, Pascal Poupart, Prashant Doshi:
Sum-Product-Max Networks for Tractable Decision Making. IJCAI 2016: 1846-1852 - [c82]Mazen Melibari, Pascal Poupart, Prashant Doshi, George Trimponias:
Dynamic Sum Product Networks for Tractable Inference on Sequence Data. Probabilistic Graphical Models 2016: 345-355 - [c81]Muthukumaran Chandrasekaran, Adam Eck, Prashant Doshi, Leenkiat Soh:
Individual Planning in Open and Typed Agent Systems. UAI 2016 - 2015
- [j18]Ekhlas Sonu, Prashant Doshi:
Scalable solutions of interactive POMDPs using generalized and bounded policy iteration. Auton. Agents Multi Agent Syst. 29(3): 455-494 (2015) - [j17]Amir H. Asiaee, Todd Minning, Prashant Doshi, Rick L. Tarleton:
A framework for ontology-based question answering with application to parasite immunology. J. Biomed. Semant. 6: 31 (2015) - [j16]ChanMin Kim, Dongho Kim, Jiangmei Yuan, Roger B. Hill, Prashant Doshi, Chi N. Thai:
Robotics to promote elementary education pre-service teachers' STEM engagement, learning, and teaching. Comput. Educ. 91: 14-31 (2015) - [c80]Ekhlas Sonu, Yingke Chen, Prashant Doshi:
Individual Planning in Agent Populations: Exploiting Anonymity and Frame-Action Hypergraphs. ICAPS 2015: 202-210 - [c79]Yingke Chen, Prashant Doshi, Yifeng Zeng:
Iterative Online Planning in Multiagent Settings with Limited Model Spaces and PAC Guarantees. AAMAS 2015: 1161-1169 - [c78]Kenneth D. Bogert, Prashant Doshi:
Multi-Robot Inverse Reinforcement Learning Under Occlusion with State Transition Estimation. AAMAS 2015: 1837-1838 - [c77]Xia Qu, Prashant Doshi:
Improved Planning for Infinite-Horizon Interactive POMDPs using Probabilistic Inference (Extended Abstract). AAMAS 2015: 1839-1840 - [c76]Kenneth D. Bogert, Sina Solaimanpour, Prashant Doshi:
Aerial Robotic Simulations for Evaluation of Multi-Agent Planning in GaTAC. AAMAS 2015: 1919-1920 - [c75]Fadel Adoe, Yingke Chen, Prashant Doshi:
Fast Solving of Influence Diagrams for Multiagent Planning on GPU-enabled Architectures. ICAART (2) 2015: 183-195 - [c74]Fadel Adoe, Yingke Chen, Prashant Doshi:
Speeding up Planning in Multiagent Settings Using CPU-GPU Architectures. ICAART (Revised Selected Papers) 2015: 262-283 - [c73]Kenneth D. Bogert, Prashant Doshi:
Toward Estimating Others' Transition Models Under Occlusion for Multi-Robot IRL. IJCAI 2015: 1867-1873 - [c72]Kedar Marathe, Prashant Doshi:
Localization and tracking under extreme and persistent sensory occlusion. IROS 2015: 2550-2555 - [c71]Xia Qu, Prashant Doshi:
Individual Planning in Infinite-Horizon Multiagent Settings: Inference, Structure and Scalability. NIPS 2015: 478-486 - [i7]Ekhlas Sonu, Yingke Chen, Prashant Doshi:
Individual Planning in Agent Populations: Exploiting Anonymity and Frame-Action Hypergraphs. CoRR abs/1503.07220 (2015) - [i6]Mazen Melibari, Pascal Poupart, Prashant Doshi:
Dynamic Sum Product Networks for Tractable Inference on Sequence Data. CoRR abs/1511.04412 (2015) - 2014
- [j15]Uthayasanker Thayasivam, Prashant Doshi:
Speeding Up Iterative Ontology Alignment using Block-Coordinate Descent. J. Artif. Intell. Res. 50: 805-845 (2014) - [c70]Kenneth D. Bogert, Prashant Doshi:
Multi-robot inverse reinforcement learning under occlusion with interactions. AAMAS 2014: 173-180 - [c69]Muthukumaran Chandrasekaran, Prashant Doshi, Yifeng Zeng, Yingke Chen:
Team behavior in interactive dynamic influence diagrams with applications to ad hoc teams. AAMAS 2014: 1559-1560 - [c68]ChanMin Kim, Prashant Doshi, Chi N. Thai, Dongho Kim, Jiangmei Yuan:
A Portal Designed to Learn about Educational Robotics. CogSci 2014 - [c67]Xia Qu, Prashant Doshi:
Behavioral Modeling of Sequential Bargaining Games: Fairness and Limited Backward Induction. ISAIM 2014 - [i5]Prashant Doshi, Piotr J. Gmytrasiewicz:
Monte Carlo Sampling Methods for Approximating Interactive POMDPs. CoRR abs/1401.3455 (2014) - [i4]Yifeng Zeng, Prashant Doshi:
Exploiting Model Equivalences for Solving Interactive Dynamic Influence Diagrams. CoRR abs/1401.4600 (2014) - [i3]Muthukumaran Chandrasekaran, Prashant Doshi, Yifeng Zeng, Yingke Chen:
Team Behavior in Interactive Dynamic Influence Diagrams with Applications to Ad Hoc Teams. CoRR abs/1409.0302 (2014) - 2013
- [c66]Amir H. Asiaee, Prashant Doshi, Todd Minning, Satya Sanket Sahoo, Priti Parikh, Amit P. Sheth, Rick L. Tarleton:
From Questions to Effective Answers: On the Utility of Knowledge-Driven Querying Systems for Life Sciences Data. DILS 2013: 38-45 - [c65]Ekhlas Sonu, Prashant Doshi:
Bimodal Switching for Online Planning in Multiagent Settings. IJCAI 2013: 360-366 - [c64]Uthayasanker Thayasivam, Prashant Doshi:
Speeding Up Batch Alignment of Large Ontologies Using MapReduce. ICSC 2013: 110-113 - [c63]Roi Ceren, Prashant Doshi, Matthew Meisel, Adam Goodie, Dan Hall:
On Modeling Human Learning in Sequential Games with Delayed Reinforcements. SMC 2013: 3108-3113 - [c62]Tejas Chaudhari, Uthayasanker Thayasivam, Prashant Doshi:
Canonical Forms and Similarity of Complex Concepts for Improved Ontology Alignment. Web Intelligence 2013: 193-198 - 2012
- [j14]Noa Agmon, Vikas Agrawal, David W. Aha, Yiannis Aloimonos, Donagh Buckley, Prashant Doshi, Christopher W. Geib, Floriana Grasso, Nancy L. Green, Benjamin Johnston, Burt Kaliski, Christopher Kiekintveld, Edith Law, Henry Lieberman, Ole J. Mengshoel, Ted Metzler, Joseph Modayil, Douglas W. Oard, Nilufer Onder, Barry O'Sullivan, Katerina Pastra, Doina Precup, Sowmya Ramachandran, Chris Reed, Sanem Sariel Talay, Ted Selker, Lokendra Shastri, Stephen F. Smith, Satinder Singh, Siddharth Srivastava, Gita Sukthankar, David C. Uthus, Mary-Anne Williams:
Reports of the AAAI 2011 Conference Workshops. AI Mag. 33(1): 57-70 (2012) - [j13]Prashant Doshi:
Decision Making in Complex Multiagent Contexts: A Tale of Two Frameworks. AI Mag. 33(4): 82-95 (2012) - [j12]Yifeng Zeng, Prashant Doshi:
Exploiting Model Equivalences for Solving Interactive Dynamic Influence Diagrams. J. Artif. Intell. Res. 43: 211-255 (2012) - [j11]Prashant Doshi, Xia Qu, Adam Goodie, Diana L. Young:
Modeling Human Recursive Reasoning Using Empirically Informed Interactive Partially Observable Markov Decision Processes. IEEE Trans. Syst. Man Cybern. Part A 42(6): 1529-1542 (2012) - [c61]Uthayasanker Thayasivam, Prashant Doshi:
Improved Convergence of Iterative Ontology Alignment using Block-Coordinate Descent. AAAI 2012: 150-156 - [c60]Ekhlas Sonu, Prashant Doshi:
Generalized and bounded policy iteration for finitely-nested interactive POMDPs: scaling up. AAMAS 2012: 1039-1048 - [c59]Xia Qu, Prashant Doshi, Adam Goodie:
Modeling deep strategic reasoning by humans in competitive games. AAMAS 2012: 1243-1244 - [c58]Ekhlas Sonu, Prashant Doshi:
GaTAC: a scalable and realistic testbed for multiagent decision making (demonstration). AAMAS 2012: 1507-1508 - [c57]Yifeng Zeng, Hua Mao, Prashant Doshi, Yinghui Pan, Jian Luo:
Learning Communication in Interactive Dynamic Influence Diagrams. IAT 2012: 243-250 - [c56]Xia Qu, Prashant Doshi, Adam Goodie:
Modeling Deep Strategic Reasoning by Humans in Competitive Games. ISAIM 2012 - [c55]Ekhlas Sonu, Prashant Doshi:
Generalized and Bounded Policy Iteration for Interactive POMDPs. ISAIM 2012 - [c54]Uthayasanker Thayasivam, Tejas Chaudhari, Prashant Doshi:
Optima+ results for OAEI 2012. OM 2012 - [i2]Amir H. Asiaee, Prashant Doshi, Todd Minning, Satya Sanket Sahoo, Priti Parikh, Amit P. Sheth, Rick L. Tarleton:
From Questions to Effective Answers: On the Utility of Knowledge-Driven Querying Systems for Life Sciences Data. CoRR abs/1210.0595 (2012) - 2011
- [c53]Yifeng Zeng, Prashant Doshi, Yinghui Pan, Hua Mao, Muthukumaran Chandrasekaran, Jian Luo:
Utilizing Partial Policies for Identifying Equivalence of Behavioral Models. AAAI 2011: 1083-1088 - [c52]Anousha Mesbah, Prashant Doshi:
Individual Localization and Tracking in Multi-robot Settings with Dynamic Landmarks - (Extended Abstract). AAMAS Workshops 2011: 277-280 - [c51]Yifeng Zeng, Yingke Chen, Prashant Doshi:
Approximating behavioral equivalence of models using top-k policy paths. AAMAS 2011: 1229-1230 - [c50]Ekhlas Sonu, Prashant Doshi:
Identifying and exploiting weak-information inducing actions in solving POMDPs. AAMAS 2011: 1259-1260 - [c49]Yifeng Zeng, Yingke Chen, Prashant Doshi:
Approximating Model Equivalence in Interactive Dynamic Influence Diagrams Using Top K Policy Paths. IAT 2011: 208-211 - [c48]Uthayasanker Thayasivam, Prashant Doshi:
On the Utility of WordNet for Ontology Alignment: Is it Really Worth it? ICSC 2011: 267-274 - [c47]Uthayasanker Thayasivam, Prashant Doshi:
Optima results for OAEI 2011. OM 2011 - [i1]Prashant Doshi, Piotr J. Gmytrasiewicz:
A Framework for Sequential Planning in Multi-Agent Settings. CoRR abs/1109.2135 (2011) - 2010
- [j10]David W. Aha, Mark S. Boddy, Vadim Bulitko, Artur S. d'Avila Garcez, Prashant Doshi, Stefan Edelkamp, Christopher W. Geib, Piotr J. Gmytrasiewicz, Robert P. Goldman, Pascal Hitzler, Charles L. Isbell Jr., Darsana P. Josyula, Leslie Pack Kaelbling, Kristian Kersting, Maithilee Kunda, Luís C. Lamb, Bhaskara Marthi, Keith McGreggor, Vivi Nastase, Gregory M. Provan, Anita Raja, Ashwin Ram, Mark O. Riedl, Stuart Russell, Ashish Sabharwal, Jan-Georg Smaus, Gita Sukthankar, Karl Tuyls, Ron van der Meyden, Alon Y. Halevy, Lilyana Mihalkova, Sriraam Natarajan:
Reports of the AAAI 2010 Conference Workshops. AI Mag. 31(4): 95-108 (2010) - [j9]Yifeng Zeng, Prashant Doshi:
Model identification in interactive influence diagrams using mutual information. Web Intell. Agent Syst. 8(3): 313-327 (2010) - [c46]Prashant Doshi, Xia Qu, Adam Goodie, Diana L. Young:
Modeling recursive reasoning by humans using empirically informed interactive POMDPs. AAMAS 2010: 1223-1230 - [c45]Prashant Doshi, Muthukumaran Chandrasekaran, Yifeng Zeng:
Epsilon-Subjective Equivalence of Models for Interactive Dynamic Influence Diagrams. IAT 2010: 165-172 - [c44]John Harney, Prashant Doshi:
Risk Sensitive Value of Changed Information for Selective Querying of Web Services. ICSOC 2010: 77-91 - [c43]Muthukumaran Chandrasekaran, Prashant Doshi, Yifeng Zeng:
Approximate solutions of interactive dynamic influence diagrams using ε-behavioral equivalence. ISAIM 2010
2000 – 2009
- 2009
- [j8]Prashant Doshi, Yifeng Zeng, Qiongyu Chen:
Graphical models for interactive POMDPs: representations and solutions. Auton. Agents Multi Agent Syst. 18(3): 376-416 (2009) - [j7]Prashant Doshi, Piotr J. Gmytrasiewicz:
Monte Carlo Sampling Methods for Approximating Interactive POMDPs. J. Artif. Intell. Res. 34: 297-337 (2009) - [j6]Haibo Zhao, Prashant Doshi:
A hierarchical framework for logical composition of web services. Serv. Oriented Comput. Appl. 3(4): 285-306 (2009) - [j5]Prashant Doshi, Ravikanth Kolli, Christopher Thomas:
Inexact matching of ontology graphs using expectation-maximization. J. Web Semant. 7(2): 90-106 (2009) - [c42]Sharon Paradesi, Prashant Doshi:
Toward Integrating Social Trust into Web Service Compositions. AAAI Spring Symposium: Social Semantic Web: Where Web 2.0 Meets Web 3.0 2009: 65-66 - [c41]Prashant Doshi, Yifeng Zeng:
Improved approximation of interactive dynamic influence diagrams using discriminative model updates. AAMAS (2) 2009: 907-914 - [c40]Prashant Doshi:
Compact approximations of mixture distributions for state estimation in multiagent settings. AAMAS (2) 2009: 1207-1208 - [c39]John Harney, Prashant Doshi:
Selective Querying for Adapting Hierarchical Web Service Compositions Using Aggregate Volatility. ICWS 2009: 43-50 - [c38]Haibo Zhao, Prashant Doshi:
Towards Automated RESTful Web Service Composition. ICWS 2009: 189-196 - [c37]Sharon Paradesi, Prashant Doshi, Sonu Swaika:
Integrating Behavioral Trust in Web Service Compositions. ICWS 2009: 453-460 - [c36]Yifeng Zeng, Prashant Doshi:
Speeding Up Exact Solutions of Interactive Dynamic Influence Diagrams Using Action Equivalence. IJCAI 2009: 1996-2001 - 2008
- [j4]John Harney, Prashant Doshi:
Selective Querying for Adapting Web Service Compositions Using the Value of Changed Information. IEEE Trans. Serv. Comput. 1(3): 169-185 (2008) - [c35]Yunzhou Wu, Prashant Doshi:
Making BPEL Flexible - Adapting in the Context of Coordination Constraints Using WS-BPEL. IEEE SCC (1) 2008: 423-430 - [c34]Prashant Doshi, Dennis Perez:
Generalized Point Based Value Iteration for Interactive POMDPs. AAAI 2008: 63-68 - [c33]Yifeng Zeng, Prashant Doshi:
An Information-Theoretic Approach to Model Identification in Interactive Influence Diagrams. IAT 2008: 224-230 - [c32]Prashant Doshi, Toshihiro Matsui, Marius Silaghi, Makoto Yokoo, Markus Zanker:
Distributed Private Constraint Optimization. IAT 2008: 277-281 - [c31]Boseon Byeon, Khaled Rasheed, Prashant Doshi:
Enhancing the Quality of Noisy Training Data Using a Genetic Algorithm and Prototype Selection. IC-AI 2008: 821-827 - [c30]Dennis Perez, Prashant Doshi:
Approximate Solutions of Interactive POMDPs Using Point Based Value Iteration. ISAIM 2008 - [c29]Ravikanth Kolli, Prashant Doshi:
OPTIMA: Tool for Ontology Alignment with Application to Semantic Reconciliation of Sensor Metadata for Publication in SensorMap. ICSC 2008: 484-485 - [c28]John Harney, Prashant Doshi:
Speeding up web service composition with volatile external information. CSSSIA 2008: 4 - [c27]Yunzhou Wu, Prashant Doshi:
Making BPEL flexible: adapting in the context of coordination constraints using WS-BPEL. WWW 2008: 1199-1200 - [c26]John Harney, Prashant Doshi:
Speeding up web service composition with volatile information. WWW 2008: 1201-1202 - 2007
- [c25]Yunzhou Wu, Prashant Doshi:
Regret-Based Decentralized Adaptation ofWeb Processes with Coordination Constraints. IEEE SCC 2007: 262-269 - [c24]Prashant Doshi:
Improved State Estimation in Multiagent Settings with Continuous or Large Discrete State Spaces. AAAI 2007: 712-717 - [c23]Yifeng Zeng, Prashant Doshi, Qiongyu Chen:
Approximate Solutions of Interactive Dynamic Influence Diagrams Using Model Clustering. AAAI 2007: 782-787 - [c22]Prashant Doshi, Yifeng Zeng, Qiongyu Chen:
Graphical Models for Online Solutions to Interactive POMDPs. AAAI Spring Symposium: Game Theoretic and Decision Theoretic Agents 2007: 8-16 - [c21]Prashant Doshi:
On the Role of Interactive Epistemology in Multiagent Planning. Artificial Intelligence and Pattern Recognition 2007: 208-213 - [c20]Prashant Doshi:
Approximate state estimation in multiagent settings with continuous or large discrete state spaces. AAMAS 2007: 13 - [c19]Prashant Doshi, Yifeng Zeng, Qiongyu Chen:
Graphical models for online solutions to interactive POMDPs. AAMAS 2007: 217 - [c18]Haibo Zhao, Prashant Doshi:
Haley: A Hierarchical Framework for Logical Composition ofWeb Services. ICWS 2007: 312-319 - [c17]Girish Chafle, Prashant Doshi, John Harney, Sumit Mittal, Biplav Srivastava:
Improved Adaptation of Web Service Compositions Using Value of Changed Information. ICWS 2007: 784-791 - [c16]John Harney, Prashant Doshi:
Speeding up adaptation of web service compositions using expiration times. WWW 2007: 1023-1032 - 2006
- [j3]Wolfgang Achtner, Esma Aïmeur, Sarabjot Singh Anand, Douglas E. Appelt, Naveen Ashish, Tiffany Barnes, Joseph E. Beck, M. Bernardine Dias, Prashant Doshi, Chris Drummond, William Elazmeh, Ariel Felner, Dayne Freitag, Hector Geffner, Christopher W. Geib, Richard Goodwin, Robert C. Holte, Frank Hutter, Fair Isaac, Nathalie Japkowicz, Gal A. Kaminka, Sven Koenig, Michail G. Lagoudakis, David B. Leake, Lundy Lewis, Hugo Liu, Ted Metzler, Rada Mihalcea, Bamshad Mobasher, Pascal Poupart, David V. Pynadath, Thomas Roth-Berghofer, Wheeler Ruml, Stefan Schulz, Sven Schwarz, Stephanie Seneff, Amit P. Sheth, Ron Sun, Michael Thielscher, Afzal Upal, Jason D. Williams, Steve J. Young, Dmitry Zelenko:
Reports on the Twenty-First National Conference on Artificial Intelligence (AAAI-06) Workshop Program. AI Mag. 27(4): 92-102 (2006) - [c15]Prashant Doshi, Piotr J. Gmytrasiewicz:
On the Difficulty of Achieving Equilibrium in Interactive POMDPs. AAAI 2006: 1131-1136 - [c14]Prashant Doshi, Christopher Thomas:
Inexact Matching of Ontology Graphs Using Expectation-Maximization. AAAI 2006: 1277-1282 - [c13]Bharaneedharan Rathnasabapathy, Prashant Doshi, Piotr J. Gmytrasiewicz:
Exact solutions of interactive POMDPs using behavioral equivalence. AAMAS 2006: 1025-1032 - [c12]Haibo Zhao, Prashant Doshi:
A Hierarchical Framework for Composing Nested Web Processes. ICSOC 2006: 116-128 - [c11]John Harney, Prashant Doshi:
Adaptive Web Processes Using Value of Changed Information. ICSOC 2006: 179-190 - [c10]Kunal Verma, Prashant Doshi, Karthik Gomadam, John A. Miller, Amit P. Sheth:
Optimal Adaptation in Web Processes with Coordination Constraints. ICWS 2006: 257-264 - [c9]Prashant Doshi, Piotr J. Gmytrasiewicz:
On the Difficulty of Achieving Equilibrium in Interactive POMDPs. AI&M 2006 - 2005
- [j2]Piotr J. Gmytrasiewicz, Prashant Doshi:
A Framework for Sequential Planning in Multi-Agent Settings. J. Artif. Intell. Res. 24: 49-79 (2005) - [j1]Prashant Doshi, Richard Goodwin, Rama Akkiraju, Kunal Verma:
Dynamic Workflow Composition: Using Markov Decision Processes. Int. J. Web Serv. Res. 2(1): 1-17 (2005) - [c8]Prashant Doshi, Piotr J. Gmytrasiewicz:
A Particle Filtering Based Approach to Approximating Interactive POMDPs. AAAI 2005: 969-974 - [c7]Prashant Doshi, Piotr J. Gmytrasiewicz:
Approximating state estimation in multiagent settings using particle filters. AAMAS 2005: 320-327 - 2004
- [c6]Prashant Doshi:
A Framework for Optimal Sequential Planning in Multiagent Settings. AAAI 2004: 985-986 - [c5]Piotr J. Gmytrasiewicz, Prashant Doshi:
Interactive POMDPs: Properties and Preliminary Results. AAMAS 2004: 1374-1375 - [c4]Prashant Doshi, Richard Goodwin, Rama Akkiraju, Kunal Verma:
Dynamic Workflow Composition using Markov Decision Processes. ICWS 2004: 576-582 - [c3]Piotr J. Gmytrasiewicz, Prashant Doshi:
A Framework for Sequential Planning in Multi-Agent Settings. AI&M 2004 - 2003
- [c2]Prashant Doshi, Lloyd G. Greenwald, John R. Clarke:
Using Bayesian Networks for Cleansing Trauma Data. FLAIRS 2003: 72-76 - [c1]Rama Akkiraju, Richard Goodwin, Prashant Doshi, Sascha Roeder:
A Method for Semantically Enhancing the Service Discovery Capabilities of UDDI. IIWeb 2003: 87-92
Coauthor Index
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