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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)- [c115]Aditya Shinde, Prashant Doshi:
Modeling Cognitive Biases in Decision-theoretic Planning for Active Cyber Deception. AAMAS 2024: 1718-1726 - 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]