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Monte Carlo tree search (MCTS) is one of the most capable online search algorithms for sequential planning tasks, with significant applications in areas such as resource allocation and transit planning.
Semantical considerations on modal logic
S. A. Kripke · 1963
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Design and synthesis of synchronization skeletons using branching time temporal logic
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Model-checking in dense real-time
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S. Sreedharan, U. Soni, M. Verma, S. Srivastava, and S. Kambhampati · 2002
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Bandit based monte-carlo planning
L. Kocsis and C. Szepesvári · 2006
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Explainable agency in human-robot interaction
P. Langley · 2016
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”why should i trust you?” explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
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Timed temporal logics
P. Bouyer, F. Laroussinie, N. Markey, J. Ouaknine, and J. Worrell · 2017
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Plan explanations as model reconciliation: Moving beyond explanation as soliloquy
T. Chakraborti, S. Sreedharan, Y. Zhang, and S. Kambhampati · 2017
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Decentralised online planning for multi-robot warehouse commissioning
D. Claes, F. Oliehoek, H. Baier, and K. Tuyls · 2017
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M. Fox, D. Long, and D. Magazzeni · 2017
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A unified approach to interpreting model predictions
S. M. Lundberg and S.-I. Lee · 2017
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Planning and learning for decentralized mdps with event driven rewards
T. Gupta, A. Kumar, and P. Paruchuri · 2018
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Metrics for explainable ai: Challenges and prospects
R. R. Hoffman, S. T. Mueller, G. Klein, and J. Litman · 2018
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A symbolic approach to explaining bayesian network classifiers
A. Shih, A. Choi, and A. Darwiche · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, et al · 2018
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Mastering atari, go, chess and shogi by planning with a learned model
J. Schrittwieser, I. Antonoglou, T. Hubert, K. Simonyan, L. Sifre, S. Schmitt, A. Guez, E. Lockhart, D. Hassabis, T. Graepel, et al · 2020
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Neural approximate dynamic programming for on-demand ride-pooling
S. Shah, M. Lowalekar, and P. Varakantham · 2020
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Explainable artificial intelligence: an analytical review
P. P. Angelov, E. A. Soares, R. Jiang, N. I. Arnold, and P. M. Atkinson · 2021
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Towards explainable mcts
H. Baier and M. Kaisers · 2021
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Toward policy explanations for multi-agent reinforcement learning
K. Boggess, S. Kraus, and L. Feng · 2022
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On the computation of necessary and sufficient explanations, 2022
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An online decision-theoretic pipeline for responder dispatch
A. Mukhopadhyay, G. Pettet, C. Samal, A. Dubey, and Y. Vorobeychik · 2019
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Towards explainable artificial intelligence
W. Samek and K.-R. Müller · 2019
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Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai
A. B. Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. García, S. Gil-López, D. Molina, R. Benjamins, et al · 2020
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Explainable search
H. Baier and M. Kaisers · 2020
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Opportunities and challenges in explainable artificial intelligence (xai): A survey
A. Das and P. Rad · 2020
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Deep reinforcement learning approach to solve dynamic vehicle routing problem with stochastic customers
W. Joe and H. C. Lau · 2020
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The emerging landscape of explainable automated planning & decision making
S. Sreedharan, T. Chakraborti, and S. Kambhampati
Cited in the paper.
A. Darwiche and C. Ji · 2022
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Algorithmic concept-based explainable reasoning
D. Georgiev, P. Barbiero, D. Kazhdan, P. Veličković, and P. Liò · 2022
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An online approach to solve the dynamic vehicle routing problem with stochastic trip requests for paratransit services
M. Wilbur, S. U. Kadir, Y. Kim, G. Pettet, A. Mukhopadhyay, P. Pugliese, S. Samaranayake, A. Laszka, and A. Dubey · 2022
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Executive order on the safe, secure, and trustworthy development and use of artificial intelligence, 2023
J. Biden · 2023
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The role of explainable ai in the context of the ai act
C. Panigutti, R. Hamon, I. Hupont, D. Fernandez Llorca, D. Fano Yela, H. Junklewitz, S. Scalzo, G. Mazzini, I. Sanchez, J. Soler Garrido, et al · 2023
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