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Sachiyo Arai
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2020 – today
- 2024
- [j13]Akinori Tamura
, Sachiyo Arai
Learning and Selection of Pareto Optimal Policies Matching User Preferences. IEEE Access 12: 97280-97297 (2024) - [j12]Daiko Kishikawa
Gamma-Regression-Based Inverse Reinforcement Learning From Suboptimal Demonstrations. IEEE Access 12: 128519-128524 (2024) - [j11]Dan Zhou
Neuroevolutionary diversity policy search for multi-objective reinforcement learning. Inf. Sci. 657: 119932 (2024) - [j10]Akiko Ikenaga, Sachiyo Arai
Estimating Objective Weights of Pareto-Optimal Policies for Multi-Objective Sequential Decision-Making. J. Adv. Comput. Intell. Intell. Informatics 28(2): 393-402 (2024) - [j9]Masaharu Saito, Sachiyo Arai
Estimation of Different Reward Functions Latent in Trajectory Data. J. Adv. Comput. Intell. Intell. Informatics 28(2): 403-412 (2024) - 2023
- [j8]Dan Zhou
Efficient Elitist Cooperative Evolutionary Algorithm for Multi-Objective Reinforcement Learning. IEEE Access 11: 43128-43139 (2023) - [j7]Naoya Takayama
Objective Weight Interval Estimation Using Adversarial Inverse Reinforcement Learning. IEEE Access 11: 58532-58538 (2023) - [j6]Takumi Saiki
Flexible Traffic Signal Control via Multi-Objective Reinforcement Learning. IEEE Access 11: 75875-75883 (2023) - [j5]Dan Zhou
Efficient search of decision makers' region of interest by using preference directions in multi-objective coevolutionary algorithm. Swarm Evol. Comput. 81: 101349 (2023) - [e3]Yasufumi Takama
New Frontiers in Artificial Intelligence - JSAI-isAI 2022 Workshop, JURISIN 2022, and JSAI 2022 International Session, Kyoto, Japan, June 12-17, 2022, Revised Selected Papers. Lecture Notes in Computer Science 13859, Springer 2023, ISBN 978-3-031-29167-8 [contents] - 2022
- [j4]Naoya Takayama, Sachiyo Arai:
Multi-objective deep inverse reinforcement learning for weight estimation of objectives. Artif. Life Robotics 27(3): 594-602 (2022) - [c25]Daiko Kishikawa, Sachiyo Arai:
Multi-Objective Deep Inverse Reinforcement Learning through Direct Weights and Rewards Estimation. SICE 2022: 122-127 - [c24]Takumi Saiki, Sachiyo Arai:
Switching Policies based on Multi-Objective Reinforcement Learning for Adaptive Traffic Signal Control. SICE 2022: 488-493 - 2021
- [j3]Daiko Kishikawa
Estimation of personal driving style via deep inverse reinforcement learning. Artif. Life Robotics 26(3): 338-346 (2021) - 2020
- [c23]Yusuke Nakata, Sachiyo Arai:
Mini-batch Bayesian Inverse Reinforcement Learning for Multiple Dynamics. AAMAS 2020: 1949-1950
2010 – 2019
- 2019
- [c22]Daiko Kishikawa
Comfortable Driving by using Deep Inverse Reinforcement Learning. ICA 2019: 38-43 - [c21]Kousuke Nishi, Sachiyo Arai:
Modeling multi-objectivization mechanism in multi-agent domain. ICA 2019: 61-64 - [c20]Toya Kamatani, Yusuke Nakata, Sachiyo Arai:
Dynamic pricing method to maximize utilization of one-way car sharing service. ICA 2019: 65-68 - 2018
- [c19]Keiichi Namikoshi, Sachiyo Arai:
Estimation of the heterogeneous strategies from action log. GECCO 2018: 1310-1317 - [c18]Yasuhiro Yoshida, Sachiyo Arai:
Charge Control of Regenerative Power for Energy Saving in Railway Systems. ICA 2018: 69-74 - [c17]Yusuke Nakata, Yuki Kitazato, Sachiyo Arai:
Detection of Features Affording a Certain Action via Analysis of CNN. ICA 2018: 105-108 - [c16]Akiko Ikenaga, Sachiyo Arai:
Inverse Reinforcement Learning Approach for Elicitation of Preferences in Multi-objective Sequential Optimization. ICA 2018: 117-118 - [c15]Yuki Kitazato, Sachiyo Arai:
Estimation of Reward Function Maximizing Learning Efficiency in Inverse Reinforcement Learning. ICAART (2) 2018: 276-283 - [c14]Shota Ishikawa, Sachiyo Arai:
Learning Cooperative Policy among Self-Driving Vehicles to Relieve Traffic Jams. ATT@IJCAI 2018: 63-69 - [e2]Sachiyo Arai, Kazuhiro Kojima, Koji Mineshima, Daisuke Bekki, Ken Satoh, Yuiko Ohta:
New Frontiers in Artificial Intelligence - JSAI-isAI Workshops, JURISIN, SKL, AI-Biz, LENLS, AAA, SCIDOCA, kNeXI, Tsukuba, Tokyo, Japan, November 13-15, 2017, Revised Selected Papers. Lecture Notes in Computer Science 10838, Springer 2018, ISBN 978-3-319-93793-9 [contents] - 2017
- [e1]Setsuya Kurahashi, Yuiko Ohta, Sachiyo Arai, Ken Satoh, Daisuke Bekki:
New Frontiers in Artificial Intelligence - JSAI-isAI 2016 Workshops, LENLS, HAT-MASH, AI-Biz, JURISIN and SKL, Kanagawa, Japan, November 14-16, 2016, Revised Selected Papers. Lecture Notes in Computer Science 10247, Springer 2017, ISBN 978-3-319-61571-4 [contents] - 2016
- [c13]Shota Ishikawa, Sachiyo Arai:
Cooperative Learning of a Driving Strategy to Suppress Phantom Traffic Jams. ICA 2016: 90-93 - [c12]Ryosuke Saitake, Sachiyo Arai:
Parameter Estimation of Multi-objective Reinforcement Learning to Reach Arbitrary Pareto Solution. ICA 2016: 110-111 - [c11]Sachiyo Arai, Haichi Xu:
Faster Convergence to Cooperative Policy by Autonomous Detection of Interference States in Multiagent Reinforcement Learning. PRICAI 2016: 16-29 - 2015
- [c10]Shota Ishikawa, Sachiyo Arai:
Evaluating advantage of sharing information among vehicles toward avoiding phantom traffic jam. WSC 2015: 300-311 - 2014
- [j2]Sachiyo Arai, Kanako Suzuki:
Encouragement of Right Social Norms by Inverse Reinforcement Learning. J. Inf. Process. 22(2): 299-306 (2014)
2000 – 2009
- 2009
- [j1]Sachiyo Arai, Yoshihisa Ishigaki:
Information Theoretic Approach for Measuring Interaction in Multiagent Domain. J. Adv. Comput. Intell. Intell. Informatics 13(6): 649-657 (2009) - 2008
- [c9]Sachiyo Arai, Yoshihisa Ishigaki, Hironori Hirata:
Measurement of Underlying Cooperation in Multiagent Reinforcement Learning. PRIMA 2008: 34-41 - 2006
- [c8]Nobuyuki Tanaka, Sachiyo Arai:
Teamwork Formation for Keepaway in Robotics Soccer (Reinforcement Learning Approach). PRIMA 2006: 279-292 - 2005
- [c7]Nima Asgharbeygi, Negin Nejati, Pat Langley, Sachiyo Arai:
Guiding Inference Through Relational Reinforcement Learning. ILP 2005: 20-37 - 2003
- [c6]Sachiyo Arai, Yohei Murakami
Semantic Web Service Architecture Using Multi-agent Scenario Description. PRIMA 2003: 98-109 - 2000
- [c5]Sachiyo Arai, Katia P. Sycara:
Multi-agent reinforcement learning for planning and conflict resolution in a dynamic domain. Agents 2000: 104-105 - [c4]Sachiyo Arai, Katia P. Sycara, Terry R. Payne
Multi-Agent Reinforcement Learning for Planning and Scheduling Multiple Goals. ICMAS 2000: 359-360 - [c3]Sachiyo Arai, Katia P. Sycara, Terry R. Payne
Experience-Based Reinforcement Learning to Acquire Effective Behavior in a Multi-agent Domain. PRICAI 2000: 125-135
1990 – 1999
- 1999
- [c2]Sachiyo Arai, Kazuteru Miyazaki, Shigenobu Kobayashi:
Multi-agent Reinforcement Learning for Crane Control Problem: Designing Rewards for Conflict Resolution. ISADS 1999: 310-319 - 1995
- [c1]Sachiyo Arai:
Rational Balancing of Planning and Communication in the Dynamic Multi-Agents World. ICMAS 1995: 436
Coauthor Index
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last updated on 2024-10-04 20:04 CEST by the dblp team
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