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2020 – today
- 2024
[j69]Iko Nakari
, Keiki Takadama:
Explainable Non-Contact Sleep Apnea Syndrome Detection Based on Comparison of Random Forests. IEEE Access 12: 12001-12009 (2024)- [j68]Kazuteru Miyazaki, Keiki Takadama:
Editorial: Cutting Edge of Reinforcement Learning and its Hybrid Methods. J. Adv. Comput. Intell. Intell. Informatics 28(2): 379 (2024) - [j67]Fumito Uwano, Satoshi Hasegawa, Keiki Takadama:
Inverse Reinforcement Learning with Agents' Biased Exploration Based on Sub-Optimal Sequential Action Data. J. Adv. Comput. Intell. Intell. Informatics 28(2): 380-392 (2024) - [j66]Iko Nakari, Keiki Takadama:
Non-Contact Sleep Stage Estimation by Updating its Prediction Probabilities According to Ultradian Rhythm. J. Adv. Comput. Intell. Intell. Informatics 28(2): 444-453 (2024) - [c214]Takashi Kido, Keiki Takadama:
The Challenges for GenAI in Social and Individual Well-Being. AAAI Spring Symposia 2024: 365-367 - [c213]Iko Nakari, Keiki Takadama:
Sleep Stage Estimation by Introduction of Sleep Domain Knowledge to AI: Towards Personalized Sleep Counseling System with GenAI. AAAI Spring Symposia 2024: 368-373 - [c212]Daiki Shintani, Iko Nakari, Satomi Washizaki, Keiki Takadama:
NREM3 Sleep Stage Estimation Based on Accelerometer by Body Movement Count and Biological Rhythms. AAAI Spring Symposia 2024: 405-411 - [c211]Keiki Takadama:
What Is a Correct Output by Generative AI From the Viewpoint of Well-Being? - Perspective From Sleep Stage Estimation -. AAAI Spring Symposia 2024: 434-439 - [c210]Ryuki Ishizawa, Hiroyuki Sato, Keiki Takadama:
From Multipoint Search to Multiarea Search: Novelty-Based Multi-Objectivization for Unbounded Search Space Optimization. CEC 2024: 1-8 - [c209]Shio Kawakami, Keiki Takadama, Hiroyuki Sato:
Evolutionary Constrained Multi-Factorial Optimization Based on Task Similarity. CEC 2024: 1-8 - [c208]Keigo Mochizuki, Tomoki Ishizuka, Naoya Yatsu, Hiroyuki Sato, Keiki Takadama:
Design of Generalized and Specialized Helper Objectives for Multi-objective Continuous Optimization Problems. CEC 2024: 1-8 - [c207]Naru Okumura, Keiki Takadama, Hiroyuki Sato:
Pareto Front Estimation Model Optimization for Aggregative Solution Set Representation. CEC 2024: 1-8 - [c206]Kazuma Sato, Naru Okumura, Keiki Takadama, Hiroyuki Sato:
Should Multi-objective Evolutionary Algorithms Use Always Best Non-dominated Solutions as Parents? CEC 2024: 1-8 - [c205]Ryo Takamiya, Minami Miyakawa, Keiki Takadama, Hiroyuki Sato:
Push and Pull Search with Directed Mating for Constrained Multi-objective Optimization. CEC 2024: 1-8 - [c204]Shoichiro Tanaka, Arnaud Liefooghe, Keiki Takadama, Hiroyuki Sato:
Designing Helper Objectives in Multi-Objectivization. CEC 2024: 1-8 - [c203]Naoya Yatsu, Hiroki Shiraishi, Hiroyuki Sato, Keiki Takadama:
Prototype Generation with the sUpervised Classifier System on kNN Matching. CEC 2024: 1-8 - [c202]Shoichiro Tanaka, Gabriela Ochoa, Arnaud Liefooghe, Keiki Takadama, Hiroyuki Sato:
Approximating Pareto Local Optimal Solution Networks. GECCO 2024 - [c201]Naoya Yatsu, Hiroki Shiraishi, Hiroyuki Sato, Keiki Takadama:
Generating High-Dimensional Prototypes with a Classifier System by Evolving in Latent Space. GECCO Companion 2024: 2127-2130 - [c200]Shunsuke Ueki, Keiki Takadama:
Multi-Agent Archive-Based Inverse Reinforcement Learning by Improving Suboptimal Experts. ICAART (3) 2024: 1362-1369 - [c199]Kazushi Fujino, Keiki Takadama, Hiroyuki Sato:
Multi-layer Cortical Learning Algorithm for Forecasting Time-series Data with Smoothly Changing Variation Patterns. IJCNN 2024: 1-8 - 2023
- [j65]Tomoaki Takagi, Keiki Takadama, Hiroyuki Sato:
Directional Pareto Front and Its Estimation to Encourage Multi-Objective Decision-Making. IEEE Access 11: 20619-20634 (2023) - [j64]Takashi Kido, Keiki Takadama:
AAAI 23 Spring Symposium Report on "Socially Responsible AI for Well-Bing". AI Mag. 44(2): 211-212 (2023) - [j63]Kazushi Fujino, Takeru Aoki, Keiki Takadama, Hiroyuki Sato:
Adaptive action-prediction cortical learning algorithm under uncertain environments. Int. J. Hybrid Intell. Syst. 19(3): 225-245 (2023) - [c198]Takashi Kido, Keiki Takadama:
The Challenges for Socially Responsible AI for Well-being. AAAI Spring Symposium: SRAI 2023: 1-3 - [c197]Keiki Takadama:
How to Handle Wellbeing in Socially Responsible AI? - Findings from Sleep Perspective -. AAAI Spring Symposium: SRAI 2023: 4-8 - [c196]Iko Nakari, Keiki Takadama:
Sleep Stage Estimation based on The Estimated Probability of each Sleep Stage by Learning with Specialized Models. AAAI Spring Symposium: SRAI 2023: 103-110 - [c195]Miki Nakai, Tomoyoshi Ashikaga, Junichi Shimizu, Keiki Takadama:
Thermal environment evaluation considering nap start time. AAAI Spring Symposium: SRAI 2023: 121-127 - [c194]Ryuki Ishizawa, Tomoya Kuga, Yusuke Maekawa, Hiroyuki Sato, Keiki Takadama:
Toward Unbounded Search Space Exploration by Particle Swarm Optimization in Multi-Modal Optimization Problem. CEC 2023: 1-8 - [c193]Naoya Matsuda, Iko Nakari, Keiki Takadama, Kohta Katayama, Makoto Shiraishi, Yoshiyuki Ohira:
Alzheimer Dementia Detection Based on Time-series Instability of Heart Rate. EMBC 2023: 1-4 - [c192]Iko Nakari, Keiki Takadama:
Personalized Non-contact Sleep Stage Estimation with Weighted Probability Estimation by Ultradian Rhythm. EMBC 2023: 1-4 - [c191]Tomoaki Takagi, Keiki Takadama, Hiroyuki Sato:
Pareto Front Upconvert by Iterative Estimation Modeling and Solution Sampling. EMO 2023: 218-230 - [c190]Shoichiro Tanaka, Keiki Takadama, Hiroyuki Sato:
Multi-objectivization Relaxes Multi-funnel Structures in Single-objective NK-landscapes. EvoCOP 2023: 195-210 - [c189]Fumito Uwano, Keiki Takadama:
Reinforcement Learning in Cyclic Environmental Changes for Agents in Non-Communicative Environments: A Theoretical Approach. EXTRAAMAS 2023: 143-159 - [c188]Naoya Yatsu, Hiroki Shiraishi, Hiroyuki Sato, Keiki Takadama:
Exploring High-dimensional Rules Indirectly via Latent Space Through a Dimensionality Reduction for XCS. GECCO 2023: 606-614 - [c187]Iko Nakari, Masahiro Nakashima, Keiki Takadama:
Personalized Sleep Stage Estimation Based on Time Series Probability of Estimation for Each Label with Wearable 3-Axis Accelerometer. HCI (5) 2023: 531-542 - [e10]Takashi Kido, Keiki Takadama:
Post-event Proceedings of the AAAI Spring Symposium: Socially Responsible AI for Well-being (AAAI-SRAI 2023) co-located with Association for the Advancement of Artificial Intelligence 2023 Spring Symposium (AAAI-Spring Symposium 2023), Hyatt Regency San Francisco Airport (Hybrid), March 27-29, 2023. CEUR Workshop Proceedings 3527, CEUR-WS.org 2023 [contents] - 2022
- [c186]Takashi Kido, Keiki Takadama:
The Challenges for Fairness and Well-being. AAAI Spring Symposium: HFIF 2022: 1-3 - [c185]Keiki Takadama:
How to Cope with Bias in Well-being AI? AAAI Spring Symposium: HFIF 2022: 4-7 - [c184]Iko Nakari, Naoya Matsuda, Keiki Takadama:
REM Estimation Based on Combination of Multi-Timescale Estimations and Automatic Adjustment of Personal Bio-vibration Data of Mattress Sensor. AAAI Spring Symposium: HFIF 2022: 74-80 - [c183]Naoya Matsuda, Taiki Senju, Iko Nakari, Keiki Takadama:
Analysis of Circadian Rhythm Estimation Process for Improving the Accuracy of Alzheimer Dementia Detection. AAAI Spring Symposium: HFIF 2022: 81-87 - [c182]Miki Nakai, Tomoyoshi Ashikaga, Takahiro Ohga, Keiki Takadama:
A Thermal Environment that Promotes Efficient Napping. AAAI Spring Symposium: HFIF 2022: 88-93 - [c181]Hiroki Shiraishi, Yohei Havamizu, Hiroyuki Sato, Keiki Takadama:
Beta Distribution based XCS Classifier System. CEC 2022: 1-8 - [c180]Tomoaki Takagi, Keiki Takadama, Hiroyuki Sato:
Supervised Multi-Objective Optimization Algorithm Using Estimation. CEC 2022: 1-8 - [c179]Shoichiro Tanaka, Keiki Takadama, Hiroyuki Sato:
Impacts of Single-objective Landscapes on Multi-objective Optimization. CEC 2022: 1-8 - [c178]Naoya Yatsu, Hiroki Shiraishi, Hiroyuki Sato, Keiki Takadama:
XCSR with VAE using Gaussian Distribution Matching: From Point to Area Matching in Latent Space for Less-overlapped Rule Generation in Observation Space. CEC 2022: 1-8 - [c177]Iko Nakari, Naoya Matsuda, Keiki Takadama:
Non-Contact REM Sleep Estimation Correction by Time-Series Confidence of Predictions: From Binary to Continuous Prediction in Machine Learning for Biological Data. EMBC 2022: 1008-1011 - [c176]Naoya Matsuda, Iko Nakari, Keiki Takadama:
Unstable Circadian Rhythm of Heart Rate of Alzheimer Dementia Based on Biological Data of Mattress Sensor. EMBC 2022: 1129-1132 - [c175]Hiroki Shiraishi, Yohei Hayamizu, Iko Nakari, Hiroyuki Sato, Keiki Takadama:
Inheritance vs. Expansion: Generalization Degree of Nearest Neighbor Rule in Continuous Space as Covering Operator of XCS. EvoApplications 2022: 352-368 - [c174]Hiroki Shiraishi, Yohei Hayamizu, Hiroyuki Sato, Keiki Takadama:
Absumption based on overgenerality and condition-clustering based specialization for XCS with continuous-valued inputs. GECCO 2022: 422-430 - [c173]Hiroki Shiraishi, Yohei Hayamizu, Hiroyuki Sato, Keiki Takadama:
Can the same rule representation change its matching area?: enhancing representation in XCS for continuous space by probability distribution in multiple dimension. GECCO 2022: 431-439 - [c172]Fumito Uwano, Daiki Yamane, Keiki Takadama:
Design of Human-Agent-Group Interaction for Correct Opinion Sharing on Social Media. HCI (4) 2022: 146-165 - [c171]Takeru Aoki, Keiki Takadama, Hiroyuki Sato:
Synergistic Effect of Adaptive Synapse Arrangement and Column-based Decoder in Cortical Learning Algorithm. SCIS/ISIS 2022: 1-6 - [c170]Kazushi Fujino, Takeru Aoki, Keiki Takadama, Hiroyuki Sato:
Adaptive Synapse Adjustment for Multivariate Cortical Learning Algorithm. SCIS/ISIS 2022: 1-8 - [e9]Takashi Kido, Keiki Takadama:
Proceedings of the Symposium How Fair is Fair? Achieving Wellbeing AI co-located with Association for the Advancement of Artificial Intelligence 2022 Spring Symposium (AAAI-Spring Symposium 2022), Stanford, CA, March 21-23, 2022. CEUR Workshop Proceedings 3276, CEUR-WS.org 2022 [contents] - 2021
- [j62]Takeru Aoki, Keiki Takadama, Hiroyuki Sato:
Adaptive Synapse Arrangement in Cortical Learning Algorithm. J. Adv. Comput. Intell. Intell. Informatics 25(4): 450-466 (2021) - [c169]Yohei Hayamizu, Saeid Amiri, Kishan Chandan, Keiki Takadama, Shiqi Zhang:
Guiding Robot Exploration in Reinforcement Learning via Automated Planning. ICAPS 2021: 625-633 - [c168]Takeru Aoki, Keiki Takadama, Hiroyuki Sato:
Double-Layered Cortical Learning Algorithm for Time-Series Prediction. BICT 2021: 33-44 - [c167]Shio Kawakami, Keiki Takadama, Hiroyuki Sato:
Multi-factorial Evolutionary Algorithm Using Objective Similarity Based Parent Selection. BICT 2021: 45-60 - [c166]Masakazu Tadokoro, Hiroyuki Sato, Keiki Takadama:
XCS with Weight-based Matching in VAE Latent Space and Additional Learning of High-Dimensional Data. CEC 2021: 304-310 - [c165]Hiroki Shiraishi, Masakazu Tadokoro, Yohei Hayamizu, Yukiko Fukumoto, Hiroyuki Sato, Keiki Takadama:
Increasing Accuracy and Interpretability of High-Dimensional Rules for Learning Classifier System. CEC 2021: 311-318 - [c164]Tomoaki Takagi, Keiki Takadama, Hiroyuki Sato:
Weight Vector Arrangement Using Virtual Objective Vectors in Decomposition-based MOEA. CEC 2021: 1462-1469 - [c163]Iko Nakari, Keiki Takadama:
Sleep Apnea Syndrome Detection Based on Degree of Convexity of Logarithmic Spectrum Calculated from Overnight Bio-vibration Data of Mattress Sensor. EMBC 2021: 2270-2273 - [c162]Naoya Matsuda, Iko Nakari, Keiki Takadama:
Alzheimer Dementia Detection Based on Unstable Circadian Rhythm Waves Extracted from Heartrate. EMBC 2021: 4473-4476 - [c161]Tomoaki Takagi, Keiki Takadama, Hiroyuki Sato:
Pareto Front Estimation Using Unit Hyperplane. EMO 2021: 126-138 - [c160]Sho Kajihara, Hiroyuki Sato, Keiki Takadama:
Generating Duplex Routes for Robust Bus Transport Network by Improved Multi-objective Evolutionary Algorithm Based on Decomposition. EvoApplications 2021: 65-80 - [c159]Hiroki Shiraishi, Masakazu Tadokoro, Yohei Hayamizu, Yukiko Fukumoto, Hiroyuki Sato, Keiki Takadama:
Misclassification detection based on conditional VAE for rule evolution in learning classifier system. GECCO Companion 2021: 169-170 - [c158]Yoshimiki Maekawa, Tomohiro Yamaguchi, Keiki Takadama:
Analyzing Early Stage of Forming a Consensus from Viewpoint of Majority/Minority Decision in Online-Barnga. HCI (5) 2021: 269-285 - [c157]Naoya Matsuda, Iko Nakari, Ryotaro Arai, Hiroyuki Sato, Keiki Takadama, Masanori Hirose, Hiroshi Hasegawa, Makoto Shiraishi, Takahide Matsuda:
Alzheimer Dementia Detection based on Circadian Rhythm Disorder of Heartrate. LifeTech 2021: 360-364 - [i2]Fumito Uwano, Keiki Takadama:
Directionality Reinforcement Learning to Operate Multi-Agent System without Communication. CoRR abs/2110.05773 (2021) - 2020
- [j61]Sotetsu Suzugamine, Takeru Aoki, Keiki Takadama, Hiroyuki Sato:
Self-Structured Cortical Learning Algorithm by Dynamically Adjusting Columns and Cells. J. Adv. Comput. Intell. Intell. Informatics 24(2): 185-198 (2020) - [j60]Fumito Uwano, Keiki Takadama:
Reward Value-Based Goal Selection for Agents' Cooperative Route Learning Without Communication in Reward and Goal Dynamism. SN Comput. Sci. 1(3): 182 (2020) - [j59]Tomohiro Harada, Keiki Takadama:
Analysis of semi-asynchronous multi-objective evolutionary algorithm with different asynchronies. Soft Comput. 24(4): 2917-2939 (2020) - [c156]Masakazu Tadokoro, Satoshi Hasegawa, Takato Tatsumi, Hiroyuki Sato, Keiki Takadama:
Local Covering: Adaptive Rule Generation Method Using Existing Rules for XCS. CEC 2020: 1-8 - [c155]Tomoaki Takagi, Keiki Takadama, Hiroyuki Sato:
Non-dominated Solution Sampling Using Environmental Selection in EMO algorithms. CEC 2020: 1-9 - [c154]Kensuke Kano, Tomoaki Takagi, Keiki Takadama, Hiroyuki Sato:
Preliminary study of adaptive grid-based decomposition on many-objective evolutionary optimization. GECCO Companion 2020: 1373-1380 - [c153]Tomoaki Takagi, Keiki Takadama, Hiroyuki Sato:
Incremental lattice design of weight vector set. GECCO Companion 2020: 1486-1494 - [c152]Kohei Yamamoto, Tomoaki Takagi, Keiki Takadama, Hiroyuki Sato:
Visual mapping of multi-objective optimization problems and evolutionary algorithms. GECCO Companion 2020: 1872-1879 - [c151]Yoshimiki Maekawa, Fumito Uwano, Eiki Kitajima, Keiki Takadama:
How to Emote for Consensus Building in Virtual Communication. HCI (5) 2020: 194-205 - [c150]Shio Kawakami, Tomoaki Takagi, Keiki Takadama, Hiroyuki Sato:
Distance Minimization Problems for Multi-factorial Evolutionary Optimization Benchmarking. HIS 2020: 710-719 - [c149]Iko Nakari, Eiki Kitajima, Yusuke Tajima, Keiki Takadama:
Non-contact Sleep Apnea Syndrome Detection Based on What Random Forests Learned. LifeTech 2020: 240-244 - [c148]Takeru Aoki, Keiki Takadama, Hiroyuki Sato:
Column-based Decoder of Internal Prediction Representation in Cortical Learning Algorithms. SCIS/ISIS 2020: 1-7 - [c147]Yukiko Fukumoto, Masakazu Tadokoro, Keiki Takadama:
Cooperative Multi-agent Inverse Reinforcement Learning Based on Selfish Expert and its Behavior Archives. SSCI 2020: 2202-2209 - [i1]Yohei Hayamizu, Saeid Amiri, Kishan Chandan, Shiqi Zhang, Keiki Takadama:
Guided Dyna-Q for Mobile Robot Exploration and Navigation. CoRR abs/2004.11456 (2020)
2010 – 2019
- 2019
- [j58]Ioana Baldini, Clark W. Barrett, Antonio Chella, Carlos Cinelli, David Gamez, Leilani H. Gilpin, Knut Hinkelmann, Dylan Holmes, Takashi Kido, Murat Kocaoglu, William F. Lawless, Alessio Lomuscio, Jamie C. Macbeth, Andreas Martin, Ranjeev Mittu, Evan Patterson, Donald Sofge, Prasad Tadepalli, Keiki Takadama, Shomir Wilson:
Reports of the AAAI 2019 Spring Symposium Series. AI Mag. 40(3): 59-66 (2019) - [c146]Takashi Kido, Keiki Takadama:
The Challenges for Interpretable AI for Well-being. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - [c145]Iko Nakari, Yusuke Tajima, Akari Tobaru, Keiki Takadama:
WAKE Detection During Sleep using Random Forest for Apnea Syndrome Patients. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - [c144]Keiki Takadama:
What Makes It Difficult To Apply AI Into Well-being and Its Solution: An Example of Sleep Apnea Syndrome. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - [c143]Ryo Takano, Sho Kajihara, Satoshi Hasegawa, Eiki Kitajima, Keiki Takadama, Toru Shimuta, Toru Yabe, Hideo Matsumoto:
Toward Good Circadian Rhythm through an valuate of Stress Condition. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - [c142]Ryo Takano, Akari Tobaru, Iko Nakari, Keiki Takadama:
Sleep Stage Estimation Through Mattress Sensor. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - [c141]Akari Tobaru, Yusuke Tajima, Keiki Takadama:
Sleep Stage Estimation using Heart Rate Variability divided by Sleep Cycle with Relative Evaluation. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - [c140]Tomoaki Takagi, Keiki Takadama, Hiroyuki Sato:
A Distribution Control of Weight Vector Set for Multi-objective Evolutionary Algorithms. BICT 2019: 70-80 - [c139]Takuya Iwase, Ryo Takano, Fumito Uwano, Hiroyuki Sato, Keiki Takadama:
Niche Radius Adaptation in Bat Algorithm for Locating Multiple Optima in Multimodal Functions. CEC 2019: 800-807 - [c138]Keiki Takadama, Daichi Yamazaki, Masaya Nakata, Hiroyuki Sato:
Complex-Valued-based Learning Classifier System for POMDP Environments. CEC 2019: 1852-1859 - [c137]Takato Tatsumi, Keiki Takadama:
Comparison of Statistical Table- and Non-Statistical Table-based XCS in Noisy Environments. CEC 2019: 1875-1882 - [c136]Masakazu Tadokoro, Satoshi Hasegawa, Takato Tatsumi, Hiroyuki Sato, Keiki Takadama:
Knowledge Extraction from XCSR Based on Dimensionality Reduction and Deep Generative Models. CEC 2019: 1883-1890 - [c135]Keiki Takadama, Keiji Sato, Hiroyuki Sato:
Evolving Generalized Solutions for Robust Multi-objective Optimization: Transportation Analysis in Disaster. EMO 2019: 491-503 - [c134]Takato Tatsumi, Keiki Takadama:
XCS-CR for handling input, output, and reward noise. GECCO (Companion) 2019: 1303-1311 - [c133]Tomohiro Yamaguchi, Shota Nagahama, Yoshihiro Ichikawa, Keiki Takadama:
Model-Based Multi-objective Reinforcement Learning with Unknown Weights. HCI (5) 2019: 311-321 - [c132]Yoshimiki Maekawa, Fumito Uwano, Eiki Kitajima, Keiki Takadama:
How to Design Adaptable Agents to Obtain a Consensus with Omoiyari. HCI (4) 2019: 462-475 - [c131]Ryota Kobayashi, Ryo Takano, Hiroyuki Sato, Keiki Takadama:
Simultaneous Local Adaptation for Different Local Properties. IES 2019: 216-227 - [c130]Takahiro Majima, Keiki Takadama, Daisuke Watanabe, Taro Aratani, Keiji Sato:
Application of Multi Agent System and Transition Matrix Analysis to Logistics System for Equal Distribution under Disaster Situation. SICE 2019: 108-114 - [c129]Iko Nakari, Akinori Murata, Eiki Kitajima, Hiroyuki Sato, Keiki Takadama:
Sleep Apnea Syndrome Detection based on Biological Vibration Data from Mattress Sensor. SSCI 2019: 550-556 - [e8]Takashi Kido, Keiki Takadama:
Proceedings of the Symposium Interpretable AI for Well-being: Understanding Cognitive Bias and Social Embeddedness co-located with Association for the Advancement of Artificial Intelligence 2019 Spring Symposium (AAAI-Spring Symposium 2019), Stanford, CA, March 25-27, 2019. CEUR Workshop Proceedings 2448, CEUR-WS.org 2019 [contents] - 2018
- [j57]Christopher Amato, Haitham Bou-Ammar, Elizabeth F. Churchill, Erez Karpas, Takashi Kido, Mike Kuniavsky, William F. Lawless, Francesca Rossi, Frans A. Oliehoek, Stephen Russell, Keiki Takadama, Siddharth Srivastava, Karl Tuyls, Philip van Allen, Kristen Brent Venable, Peter Vrancx, Shiqi Zhang:
Reports on the 2018 AAAI Spring Symposium Series. AI Mag. 39(4): 29-35 (2018) - [j56]Takahiro Majima, Keiki Takadama, Daisuke Watanabe, Taro Aratani, Keiji Sato:
Transportation simulator for disaster circumstance and bottleneck analysis. Artif. Life Robotics 23(4): 593-599 (2018) - [c128]Takashi Kido, Keiki Takadama:
The Challenges for Understanding Cognitive Bias and Humanity for Well-Being AI - Beyond Machine Intelligence. AAAI Spring Symposia 2018 - [c127]Yusuke Tajima, Akinori Murata, Tomohiro Harada, Keiki Takadama:
Sleep Stage Re-Estimation Method According To Sleep Cycle Change. AAAI Spring Symposia 2018 - [c126]Keiki Takadama:
Can Machine Learning Correct Commonly Accepted Knowledge and Provide Understandable Knowledge in Care Support Domain? Tackling Cognitive Bias and Humanity from Machine Learning Perspective. AAAI Spring Symposia 2018 - [c125]Ryo Takano, Satoshi Hasegawa, Yuta Umenai, Takato Tatsumi, Keiki Takadama, Toru Shimuta, Toru Yabe, Hideo Matsumoto:
Study of Analytical Methods on the Relationship between Sleep Quality and Stress with a focus on Human Circadian Rhythm. AAAI Spring Symposia 2018 - [c124]Akari Tobaru, Fumito Uwano, Takuya Iwase, Kazuma Matsumoto, Ryo Takano, Yusuke Tajima, Yuta Umenai, Keiki Takadama:
Improving Sleep Stage Estimation Accuracy by Circadian Rhythm Extracted from a Low Frequency Component of Heart Rate. AAAI Spring Symposia 2018 - [c123]Fumito Uwano, Keiki Takadama:
Ensemble Heart Rate Extraction Method for Biological Data from Water Pressure Sensor. AAAI Spring Symposia 2018 - [c122]Ryo Takano, Hiroyuki Sato, Keiki Takadama:
Artificial bee colony algorithm based on adaptive local information sharing: approach for several dynamic changes. GECCO (Companion) 2018: 95-96 - [c121]Yuta Umenai, Fumito Uwano, Hiroyuki Sato, Keiki Takadama:
Multiple swarm intelligence methods based on multiple population with sharing best solution for drastic environmental change. GECCO (Companion) 2018: 97-98 - [c120]Kazuma Matsumoto, Ryo Takano, Takato Tatsumi, Hiroyuki Sato, Tim Kovacs, Keiki Takadama:
XCSR based on compressed input by deep neural network for high dimensional data. GECCO (Companion) 2018: 1418-1425 - [c119]Takato Tatsumi, Tim Kovacs, Keiki Takadama:
XCS-CR: determining accuracy of classifier by its collective reward in action set toward environment with action noise. GECCO (Companion) 2018: 1457-1464 - [c118]Fumito Uwano, Koji Dobashi, Keiki Takadama, Tim Kovacs:
Generalizing rules by random forest-based learning classifier systems for high-dimensional data mining. GECCO (Companion) 2018: 1465-1472 - [c117]Caili Zhang, Takato Tatsumi, Hiyoyuki Sato, Tim Kovacs, Keiki Takadama:
Classifier generalization for comprehensive classifiers subsumption in XCS. GECCO (Companion) 2018: 1854-1861 - [c116]Takato Okudo, Tomohiro Yamaguchi, Keiki Takadama:
Generating Learning Environments Derived from Found Solutions by Adding Sub-goals Toward the Creative Learning Support. HCI (5) 2018: 313-330 - [c115]Eiki Kitajima, Caili Zhang, Haruyuki Ishii, Fumito Uwano, Keiki Takadama:
Correcting Wrongly Determined Opinions of Agents in Opinion Sharing Model. HCI (4) 2018: 658-676 - [c114]Fumito Uwano, Keiki Takadama:
Strategy for Learning Cooperative Behavior with Local Information for Multi-agent Systems. PRIMA 2018: 663-670 - [c113]Sotetsu Suzugamine, Takeru Aoki, Keiki Takadama, Hiroyuki Sato:
A Study on a Cortical Learning Algorithm Dynamically Adjusting Columns and Cells. SCIS&ISIS 2018: 267-274 - [c112]Takato Tatsumi, Keiki Takadama:
XCS for Missing Attributes in Data. SCIS&ISIS 2018: 329-334 - [e7]Hernán E. Aguirre, Keiki Takadama:
Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. ACM 2018, ISBN 978-1-4503-5618-3 [contents] - [e6]Hernán E. Aguirre, Keiki Takadama:
Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2018, Kyoto, Japan, July 15-19, 2018. ACM 2018, ISBN 978-1-4503-5764-7 [contents] - 2017
- [j55]Jeannette Bohg, Xavier Boix, Nancy Chang, Elizabeth F. Churchill, Vivian Chu, Fei Fang, Jerome Feldman, Avelino J. Gonzalez, Takashi Kido, William F. Lawless, José L. Montaña, Santiago Ontañón, Jivko Sinapov, Donald A. Sofge, Luc Steels, Molly Wright Steenson, Keiki Takadama, Amulya Yadav:
Reports on the 2017 AAAI Spring Symposium Series. AI Mag. 38(4): 99-106 (2017) - [j54]Keiki Takadama, Kazuteru Miyazaki:
Editorial: Cutting Edge of Reinforcement Learning and its Hybrid Methods. J. Adv. Comput. Intell. Intell. Informatics 21(5): 833 (2017) - [j53]Kazuma Matsumoto, Takato Tatsumi, Hiroyuki Sato, Tim Kovacs, Keiki Takadama:
XCSR Learning from Compressed Data Acquired by Deep Neural Network. J. Adv. Comput. Intell. Intell. Informatics 21(5): 856-867 (2017) - [j52]Hiroyasu Matsushima, Keiki Takadama:
Exemplar-Based Learning Classifier System with Dynamic Matching Range for Imbalanced Data. J. Adv. Comput. Intell. Intell. Informatics 21(5): 868-875 (2017) - [j51]Caili Zhang, Takato Tatsumi, Masaya Nakata, Keiki Takadama:
Approach to Clustering with Variance-Based XCS. J. Adv. Comput. Intell. Intell. Informatics 21(5): 885-894 (2017) - [j50]Takato Tatsumi, Hiroyuki Sato, Keiki Takadama:
Learning Classifier System Based on Mean of Reward. J. Adv. Comput. Intell. Intell. Informatics 21(5): 895-906 (2017) - [j49]Takato Okudo, Tomohiro Yamaguchi, Akinori Murata, Takato Tatsumi, Fumito Uwano, Keiki Takadama:
Supporting the Exploration of the Learning Goals for a Continuous Learner Toward Creative Learning. J. Adv. Comput. Intell. Intell. Informatics 21(5): 907-916 (2017) - [j48]Fumito Uwano, Keiki Takadama:
Comparison Between Reinforcement Learning Methods with Different Goal Selections in Multi-Agent Cooperation. J. Adv. Comput. Intell. Intell. Informatics 21(5): 917-929 (2017) - [j47]Tomohiro Harada, Keiki Takadama:
Machine-Code Program Evolution by Genetic Programming Using Asynchronous Reference-Based Evaluation Through Single-Event Upset in On-Board Computer. J. Robotics Mechatronics 29(5): 808-818 (2017) - [j46]Fumito Uwano, Yusuke Tajima, Akinori Murata, Keiki Takadama:
Recovery System Based on Exploration-Biased Genetic Algorithm for Stuck Rover in Planetary Exploration. J. Robotics Mechatronics 29(5): 877-886 (2017) - [c111]Tomohiro Harada, Takahiro Kawashima, Morito Morishima, Keiki Takadama:
Improving Accuracy of Real-Time Sleep Stage Estimation by Considering Personal Sleep Feature and Rapid Change of Sleep Behavior. AAAI Spring Symposia 2017 - [c110]Masafumi Ishii, Jinhwan Kwon, Keiki Takadama, Maki Sakamoto:
Visual Impression Generation System Based on Boids Algorithm. AAAI Spring Symposia 2017 - [c109]Takashi Kido, Keiki Takadama:
Wellbeing AI Invited Speaker Abstracts. AAAI Spring Symposia 2017 - [c108]Yusuke Tajima, Tomohiro Harada, Keiki Takadama:
Sleep Stage Estimation Based on Appriximate Heartrate Calculated from Other Persons. AAAI Spring Symposia 2017 - [c107]Keiki Takadama:
Towards Guideline for Applying Machine Learning into Care Support Systems. AAAI Spring Symposia 2017 - [c106]Tomohiro Harada, Keiki Takadama:
Performance comparison of parallel asynchronous multi-objective evolutionary algorithm with different asynchrony. CEC 2017: 1215-1222 - [c105]Takato Tatsumi, Hiroyuki Sato, Tim Kovacs, Keiki Takadama:
Applying variance-based Learning Classifier System without Convergence of Reward Estimation into various Reward distribution. CEC 2017: 2630-2637 - [c104]Hiroyuki Sato, Minami Miyakawa, Keiki Takadama:
An improved MOEA/D utilizing variation angles for multi-objective optimization. GECCO (Companion) 2017: 163-164 - [c103]Masaya Nakata, Will N. Browne, Tomoki Hamagami, Keiki Takadama:
Theoretical XCS parameter settings of learning accurate classifiers. GECCO 2017: 473-480 - [c102]Takato Tatsumi, Hiroyuki Sato, Keiki Takadama:
Automatic adjustment of selection pressure based on range of reward in learning classifier system. GECCO 2017: 505-512 - [c101]Tomohiro Harada, Keiki Takadama:
A study of self-adaptive semi-asynchronous evolutionary algorithm on multi-objective optimization problem. GECCO (Companion) 2017: 1812-1819 - [c100]Takato Okudo, Keiki Takadama, Tomohiro Yamaguchi:
Designing the Learning Goal Space for Human Toward Acquiring a Creative Learning Skill. HCI (4) 2017: 62-73 - [c99]Akinori Murata, Hiroyuki Sato, Keiki Takadama:
Towards Adaptive Aircraft Landing Order with Aircraft Routes Partially Fixed by Air Traffic Controllers as Human Intervention. HCI (4) 2017: 422-433 - [c98]Motoaki Kakuguchi, Minami Miyakawa, Keiki Takadama, Hiroyuki Sato:
Multi-objetive optimization problem mapping based on algorithmic parameter rankings. SSCI 2017: 1-8 - [c97]Yuta Umenai, Fumito Uwano, Hiroyuki Sato, Keiki Takadama:
Strategies to Improve Cuckoo Search Toward Adapting Randomly Changing Environment. ICSI (1) 2017: 573-582 - 2016
- [j45]Christopher Amato, Ofra Amir, Joanna Bryson, Barbara J. Grosz, Bipin Indurkhya, Emre Kiciman, Takashi Kido, William F. Lawless, Miao Liu, Braden McDorman, Ross Mead, Frans A. Oliehoek, Andrew Specian, Georgi Stojanov, Keiki Takadama:
Reports of the AAAI 2016 Spring Symposium Series. AI Mag. 37(4): 83-88 (2016) - [j44]Kiyohiko Hattori, Naoki Tatebe, Toshinori Kagawa, Yasunori Owada, Lin Shan, Katsuhiro Temma, Kiyoshi Hamaguchi, Keiki Takadama:
Deployment of wireless mesh network using RSSI-based swarm robots. Artif. Life Robotics 21(4): 434-442 (2016) - [j43]Minami Miyakawa, Keiki Takadama, Hiroyuki Sato:
Controlling selection areas of useful infeasible solutions for directed mating in evolutionary constrained multi-objective optimization. Ann. Math. Artif. Intell. 76(1-2): 25-46 (2016) - [j42]Fumito Uwano, Naoki Tatebe, Masaya Nakata, Keiki Takadama, Tim Kovacs:
Reinforcement Learning with Internal Reward for Multi-Agent Cooperation: A Theoretical Approach. EAI Endorsed Trans. Collab. Comput. 2(8): e2 (2016) - [j41]Tomohiro Yamaguchi, Takuma Nishimura, Keiki Takadama:
Awareness Based Recommendation: Passively Interactive Learning System. Int. J. Robotics Appl. Technol. 4(1): 83-99 (2016) - [j40]Takahiro Majima, Keiki Takadama, Daisuke Watanabe, Mitujiro Katuhara:
Characteristic and application of network evolution model for public transport network. Multiagent Grid Syst. 12(1): 1-11 (2016) - [j39]Akinori Murata, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, Keiki Takadama:
Optimization of Aircraft Landing Route and Order: An approach of Hierarchical Evolutionary Computation. EAI Endorsed Trans. Self Adapt. Syst. 2(6): e5 (2016) - [c96]Tomohiro Harada, Fumito Uwano, Takahiro Komine, Yusuke Tajima, Takahiro Kawashima, Morito Morishima, Keiki Takadama:
Real-Time Sleep Stage Estimation from Biological Data with Trigonometric Function Regression Model. AAAI Spring Symposia 2016 - [c95]Takahiro Komine, Keiki Takadama, Seiji Nishino:
Toward the Next-Generation Sleep Monitoring / Evaluation by Human Body Vibration Analysis. AAAI Spring Symposia 2016 - [c94]Morito Morishima, Yusuke Sugino, Yuki Ueya, Hiroshi Kadotani, Keiki Takadama:
Effects on Sleep by "Cradle Sound" Adjusted to Heartbeat and Respiration. AAAI Spring Symposia 2016 - [c93]Keiki Takadama:
Well-Being Computing Towards Health and Happiness Improvement: From Sleep Perspective. AAAI Spring Symposia 2016 - [c92]Hiroyuki Sato, Satoshi Nakagawa, Minami Miyakawa, Keiki Takadama:
Enhanced decomposition-based many-objective optimization using supplemental weight vectors. CEC 2016: 1626-1633 - [c91]Yuta Umenai, Fumito Uwano, Yusuke Tajima, Masaya Nakata, Hiroyuki Sato, Keiki Takadama:
A modified cuckoo search algorithm for dynamic optimization problems. CEC 2016: 1757-1764 - [c90]Kazuma Matsumoto, Yusuke Tajima, Rei Saito, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, Keiki Takadama:
Learning classifier system with deep autoencoder. CEC 2016: 4739-4746 - [c89]Tim Kovacs, Simon Rawles, Larry Bull, Masaya Nakata, Keiki Takadama:
XCS-DH: Minimal default hierarchies in XCS. CEC 2016: 4747-4754 - [c88]Takato Tatsumi, Takahiro Komine, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, Keiki Takadama:
Variance-based Learning Classifier System without Convergence of Reward Estimation. GECCO (Companion) 2016: 67-68 - [c87]Rei Saito, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, Keiki Takadama:
Preventing Incorrect Opinion Sharing with Weighted Relationship Among Agents. HCI (5) 2016: 50-62 - [c86]Yusuke Tajima, Tomohiro Harada, Hiroyuki Sato, Keiki Takadama:
Personalized Real-Time Sleep Stage from Past Sleep Data to Today's Sleep Estimation. HCI (5) 2016: 501-510 - [c85]Takahiro Majima, Keiki Takadama, Daisuke Watanabe, Mitujiro Katuhara:
Proceedings in Adaptation, Learning and Optimization. IES 2016: 263-275 - [c84]Akinori Murata, Hiroyuki Sato, Keiki Takadama:
Proceedings in Adaptation, Learning and Optimization. IES 2016: 291-304 - [c83]Fumito Uwano, Keiki Takadama:
Proceedings in Adaptation, Learning and Optimization. IES 2016: 453-467 - [c82]Motoaki Kakuguchi, Minami Miyakawa, Keiki Takadama, Hiroyuki Sato:
Evolutionary Algorithmic Parameter Optimization of MOEAs for Multiple Multi-objective Problems. SCIS&ISIS 2016: 30-35 - [c81]Caili Zhang, Takato Tatsumi, Masaya Nakata, Keiki Takadama, Hiroyuki Sato, Tim Kovacs:
Extracting Different Abstracted Level Rule with Variance-Based LCS. SCIS&ISIS 2016: 160-165 - 2015
- [j38]Nitin Agarwal, Sean Andrist, Dan Bohus, Fei Fang, Laurie Fenstermacher, Lalana Kagal, Takashi Kido, Christopher Kiekintveld, William F. Lawless, Huan Liu, Andrew McCallum, Hemant Purohit, Oshani Seneviratne, Keiki Takadama, Gavin Taylor:
Reports on the 2015 AAAI Spring Symposium Series. AI Mag. 36(3): 113-119 (2015) - [j37]Masaya Nakata, Pier Luca Lanzi, Keiki Takadama:
Rule reduction by selection strategy in XCS with adaptive action map. Evol. Intell. 8(2-3): 71-87 (2015) - [j36]Masaya Nakata, Tim Kovacs, Keiki Takadama:
XCS-SL: a rule-based genetic learning system for sequence labeling. Evol. Intell. 8(2-3): 133-148 (2015) - [j35]Takahiro Jinba, Hiroto Kitagawa, Eriko Azuma, Keiji Sato, Hiroyuki Sato, Kiyohiko Hattori, Keiki Takadama:
Multi-objective Optimization for Common and Special Components: First Step Toward Network Optimization of Regular and Non-Regular Flights. New Math. Nat. Comput. 11(2): 183-199 (2015) - [j34]Yusuke Tajima, Masaya Nakata, Hiroyasu Matsushima, Yoshihiro Ichikawa, Hiroyuki Sato, Kiyohiko Hattori, Keiki Takadama:
Evolutionary Algorithm for Uncertain Evaluation Function. New Math. Nat. Comput. 11(2): 201-215 (2015) - [c80]Takahiro Komine, Keiki Takadama:
Detecting Aged Person's Sliding Feet from Time Series Data of Foot Pressure. AAAI Spring Symposia 2015 - [c79]Keiki Takadama, Yusuke Tajima, Tomohiro Harada, Atsushi Ishihara, Morito Morishima:
Towards Ambient Intelligence System for Good Sleep By Sound Adjusted to Heartbeat and Respiration. AAAI Spring Symposia 2015 - [c78]Takuma Fujitsuka, Tomohiro Harada, Hiroyuki Sato, Keiki Takadama, Tomohiro Yamaguchi:
Sightseeing plan recommendation system using sequential pattern mining based on adjacent activities. ASCC 2015: 1-6 - [c77]Tomohiro Yamaguchi, Kouki Takemori, Yuki Tamai, Keiki Takadama:
Analyzing human's continuous learning processes with the reflection sub task. ASCC 2015: 1-6 - [c76]Fumito Uwano, Naoki Tatebe, Masaya Nakata, Keiki Takadama, Tim Kovacs:
Reinforcement Learning with Internal Reward for Multi-Agent Cooperation: A Theoretical Approach. BICT 2015: 332-339 - [c75]Akinori Murata, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, Keiki Takadama:
Optimization of Aircraft Landing Route and Order: An approach of Hierarchical Evolutionary Computation. BICT 2015: 340-347 - [c74]Ryo Takano, Hiroyuki Sato, Tomohiro Harada, Keiki Takadama:
Toward robustness against environmental change speed by artificial bee colony algorithm based on local information sharing. CEC 2015: 1424-1431 - [c73]Keiki Takadama, Hiroyuki Sato, Daisuke Watanabe, Eriko Azuma, Takahiro Majima, Mitujiro Katuhara:
Ship route evolutionary optimization of multiple ship companies for distributed coordination of resources. CEC 2015: 1450-1457 - [c72]Minami Miyakawa, Keiki Takadama, Hiroyuki Sato:
Directed mating using inverted PBI function for constrained multi-objective optimization. CEC 2015: 2929-2936 - [c71]Takato Tatsumi, Takahiro Komine, Hiroyuki Sato, Keiki Takadama:
Handling different level of unstable reward environment through an estimation of reward distribution in XCS. CEC 2015: 2973-2980 - [c70]Minato Sato, Kotaro Usui, Masaya Nakata, Keiki Takadama:
Detecting shoplifting from customer behavior data by extended XCS-SL: Towards feature extraction on class-imbalanced sequence data. CEC 2015: 2981-2988 - [c69]Masaya Nakata, Pier Luca Lanzi, Tim Kovacs, Will Neil Browne, Keiki Takadama:
How should Learning Classifier Systems cover a state-action space? CEC 2015: 3012-3019 - [c68]Keiki Takadama, Masaya Nakata:
Extracting both generalized and specialized knowledge by XCS using Attribute Tracking and Feedback. CEC 2015: 3034-3041 - [c67]Keiki Takadama:
A Potential of Evolutionary Rule-based Machine Learning for Real World Applications. GECCO (Companion) 2015: 1039-1040 - [c66]Minami Miyakawa, Keiki Takadama, Hiroyuki Sato:
Control of Crossed Genes Ratio for Directed Mating in Evolutionary Constrained Multi-Objective Optimization. GECCO (Companion) 2015: 1201-1204 - [c65]Tomohiro Yamaguchi, Yuki Tamai, Keiki Takadama:
Analyzing human's continuous learning ability with the reflection cost. IECON 2015: 2920-2925 - [c64]Shingo Tomura, Tomohiro Harada, Hiroyuki Sato, Keiki Takadama, Makoto Aoki:
Estimating surrounding symptom level of dementia patient by sleep stage. ISMICT 2015: 190-194 - 2014
- [j33]Manish Jain, Albert Xin Jiang, Takashi Kido, Keiki Takadama, Eric G. Mercer, Neha Rungta, Mark Waser, Alan Wagner, Jennifer L. Burke, Donald A. Sofge, William F. Lawless, Mohan Sridharan, Nick Hawes, Tim Hwang:
Reports of the 2014 AAAI Spring Symposium Series. AI Mag. 35(3): 70-76 (2014) - [j32]Minami Miyakawa, Keiki Takadama, Hiroyuki Sato:
Archive of Useful Solutions for Directed Mating in Evolutionary Constrained Multiobjective Optimization. J. Adv. Comput. Intell. Intell. Informatics 18(2): 221-231 (2014) - [j31]Hiroto Kitagawa, Keiji Sato, Keiki Takadama:
Multiagent-based Sustainable Bus Route Optimization in Disaster. J. Inf. Process. 22(2): 235-242 (2014) - [c63]Yusuke Tajima, Masaya Nakata, Tomohiro Harada, Keiji Sato, Keiki Takadama:
Sleep Stage Estimation Using Synthesized Data of Heart Rate and Body Movement. AAAI Spring Symposia 2014 - [c62]Keiki Takadama:
Concierge-Based Care Support System for Designing Your Own Lifestyle. AAAI Spring Symposia 2014 - [c61]Takahiro Majima, Keiki Takadama, Daisuke Watanabe, Mitujiro Katuhara:
Application of Community Detection Method to Generating Public Transport Network. BICT 2014 - [c60]Tomohiro Harada, Keiki Takadama:
Asynchronous Evolution by Reference-Based Evaluation: Tertiary Parent Selection and Its Archive. EuroGP 2014: 198-209 - [c59]Masaya Nakata, Pier Luca Lanzi, Tim Kovacs, Keiki Takadama:
Complete action map or best action map in accuracy-based reinforcement learning classifier systems. GECCO 2014: 557-564 - [c58]Masaya Nakata, Tim Kovacs, Keiki Takadama:
A modified XCS classifier system for sequence labeling. GECCO 2014: 565-572 - [c57]Minami Miyakawa, Keiki Takadama, Hiroyuki Sato:
Controlling selection area of useful infeasible solutions and their archive for directed mating in evolutionary constrained multiobjective optimization. GECCO 2014: 629-636 - [c56]Tomohiro Harada, Keiki Takadama:
Asynchronously evolving solutions with excessively different evaluation time by reference-based evaluation. GECCO 2014: 911-918 - [c55]Tomohiro Yamaguchi, Kouki Takemori, Keiki Takadama:
Visualizing Mental Learning Processes with Invisible Mazes for Continuous Learning. HCI (13) 2014: 137-148 - [c54]Asami Mori, Tomohiro Harada, Yoshihiro Ichikawa, Keiki Takadama:
Favor Information Presentation and Its Effect for Collective-Adaptive Situation. HCI (13) 2014: 455-466 - [c53]Yusuke Tajima, Masaya Nakata, Keiki Takadama:
Personalized real-time sleep stage remote monitoring system. ISMICT 2014: 1-5 - [c52]Minami Miyakawa, Keiki Takadama, Hiroyuki Sato:
Controlling Selection Area of Useful Infeasible Solutions in Directed Mating for Evolutionary Constrained Multiobjective Optimization. LION 2014: 137-152 - [c51]Keiki Takadama, Tomohiro Harada, Hiroyuki Sato, Kiyohiko Hattori:
What is Needed to Promote an Asynchronous Program Evolution in Genetic Programing? LION 2014: 227-241 - [c50]Takahiro Komine, Masaya Nakata, Keiki Takadama:
Archives-holding XCS Classifier System: A preliminary study. NaBIC 2014: 53-58 - [c49]Kotaro Usui, Masaya Nakata, Keiki Takadama:
Reusable knowledge by linkage-classifier in Accuracy-based Learning Classifier System. NaBIC 2014: 312-317 - [c48]Masaya Nakata, Tim Kovacs, Keiki Takadama:
Messy Coding in the XCS Classifier System for Sequence Labeling. PPSN 2014: 191-200 - [c47]Rya Takano, D. Yamazaki, Yoshihiro Ichikawa, Kiyohiko Hattori, Keiki Takadama:
Multiagent-based ABC algorithm for Autonomous Rescue Agent Cooperation. SMC 2014: 585-590 - 2013
- [j30]Vita Markman, Georgi Stojanov, Bipin Indurkhya, Takashi Kido, Keiki Takadama, George Dimitri Konidaris, Eric Eaton, Naohiro Matsumura, Renate Fruchter, Donald A. Sofge, William F. Lawless, Omid Madani, Rahul Sukthankar:
Reports of the 2013 AAAI Spring Symposium Series. AI Mag. 34(3): 93-98 (2013) - [j29]Yoshihiro Ichikawa, Keiki Takadama:
Designing Internal Reward of Reinforcement Learning Agents in Multi-Step Dilemma Problem. J. Adv. Comput. Intell. Intell. Informatics 17(6): 926-931 (2013) - [j28]Tomohiro Nakada, Keiki Takadama, Shigeyoshi Watanabe:
Analysis of emission right prices in greenhouse gas emission trading via agent-based model. Multiagent Grid Syst. 9(3): 227-246 (2013) - [c46]Takashi Kido, Keiki Takadama:
Preface. AAAI Spring Symposium: Data Driven Wellness 2013 - [c45]Keiki Takadama:
Towards a Care Support System that Can Guess The Way Aged Persons Feel. AAAI Spring Symposium: Data Driven Wellness 2013 - [c44]Masaya Nakata, Pier Luca Lanzi, Keiki Takadama:
Simple compact genetic algorithm for XCS. IEEE Congress on Evolutionary Computation 2013: 1718-1723 - [c43]Tomohiro Nakada, Keiki Takadama:
Analysis on the number of XCS agents in agent-based computational finance. CIFEr 2013: 8-13 - [c42]Tomohiro Harada, Keiki Takadama:
Analyzing Program Evolution in Genetic Programming using Asynchronous Evaluation. ECAL 2013: 713-720 - [c41]Tomohiro Harada, Keiki Takadama:
Asynchronous Evaluation Based Genetic Programming: Comparison of Asynchronous and Synchronous Evaluation and Its Analysis. EuroGP 2013: 241-252 - [c40]Minami Miyakawa, Keiki Takadama, Hiroyuki Sato:
Two-stage non-dominated sorting and directed mating for solving problems with multi-objectives and constraints. GECCO 2013: 647-654 - [c39]Masaya Nakata, Pier Luca Lanzi, Keiki Takadama:
Selection strategy for XCS with adaptive action mapping. GECCO 2013: 1085-1092 - [c38]Keiki Takadama, Yuya Sawadaishi, Tomohiro Harada, Yoshihiro Ichikawa, Keiji Sato, Kiyohiko Hattori, Hiroyuki Sato, Tomohiro Yamaguchi:
Towards Understanding of Relationship among Pareto Optimal Solutions in Multi-dimensional Space via Interactive System. HCI (15) 2013: 137-146 - [c37]Kouki Takemori, Tomohiro Yamaguchi, Kazuki Sasaji, Keiki Takadama:
Modeling a Human's Learning Processes to Support Continuous Learning on Human Computer Interaction. HCI (13) 2013: 555-564 - 2012
- [j27]Harith Alani, Bo An, Manish Jain, Takashi Kido, George Dimitri Konidaris, William F. Lawless, David L. Martin, Caroline Pantofaru, Donald A. Sofge, Keiki Takadama, Milind Tambe, Tomas Vitvar:
Reports of the AAAI 2012 Spring Symposia. AI Mag. 33(3): 109-114 (2012) - [c36]Takashi Kido, Keiki Takadama:
Preface. AAAI Spring Symposium: Self-Tracking and Collective Intelligence for Personal Wellness 2012 - [c35]Takashi Kido, Keiki Takadama:
Invited Speaker and Special Presentation Abstracts. AAAI Spring Symposium: Self-Tracking and Collective Intelligence for Personal Wellness 2012 - [c34]Hiroyasu Matsushima, Shogo Minami, Keiki Takadama:
Age-Based Sleep Stage Estimation by Evolutionary Algorithm. AAAI Spring Symposium: Self-Tracking and Collective Intelligence for Personal Wellness 2012 - [c33]Keiki Takadama:
Exploring Individual Care Plan for a Good Sleep. AAAI Spring Symposium: Self-Tracking and Collective Intelligence for Personal Wellness 2012 - [c32]Masaya Nakata, Pier Luca Lanzi, Keiki Takadama:
Enhancing Learning Capabilities by XCS with Best Action Mapping. PPSN (1) 2012: 256-265 - [c31]Tomohiro Harada, Yoshihiro Ichikawa, Keiki Takadama:
Evolving conditional branch programs in Tierra-based Asynchronous Genetic Programming. SCIS&ISIS 2012: 1023-1028 - [c30]Masaya Nakata, Pier Luca Lanzi, Keiki Takadama:
XCS with Adaptive Action Mapping. SEAL 2012: 138-147 - 2011
- [j26]Keiji Sato, Keiki Takadama:
Pittsburgh-style learning classifier system for multiple environments: towards robust waterbus route for several situations. Int. J. Bio Inspired Comput. 3(6): 370-383 (2011) - [j25]Kiyoshi Izumi, Keiki Takadama, Hiromitsu Hattori, Nariaki Nishino, Itsuki Noda:
Social and Group Simulation Based on Real Data Analysis. J. Adv. Comput. Intell. Intell. Informatics 15(2): 166-172 (2011) - [j24]Tomohiro Harada, Masayuki Otani, Yoshihiro Ichikawa, Kiyohiko Hattori, Hiroyuki Sato, Keiki Takadama:
Robustness to Bit Inversion in Registers and Acceleration of Program Evolution in On-Board Computer. J. Adv. Comput. Intell. Intell. Informatics 15(8): 1175-1185 (2011) - [j23]Masayuki Otani, Kiyohiko Hattori, Hiroyuki Sato, Keiki Takadama:
Improving Recovery Capability of Multiple Robots in Different Scale Structure Assembly. J. Adv. Comput. Intell. Intell. Informatics 15(8): 1186-1196 (2011) - [c29]Keiki Takadama, Atsushi Otaki, Keiji Sato, Hiroyasu Matsushima, Masayuki Otani, Yoshihiro Ichikawa, Kiyohiko Hattori, Hiroyuki Sato:
What Kinds of Human Negotiation Skill Can Be Acquired by Changing Negotiation Order of Bargaining Agents? HCI (12) 2011: 335-344 - [c28]Eriko Azuma, Tomohiro Shimada, Keiki Takadama, Hiroyuki Sato, Kiyohiko Hattori:
The Biased Multi-objective Optimization Using the Reference Point: Toward the Industrial Logistics Network. ICMLA (2) 2011: 27-30 - [c27]Masaya Nakata, Fumiaki Sato, Keiki Takadama:
Towards generalization by identification-based XCS in multi-steps problem. NaBIC 2011: 389-394 - [c26]Tomohiro Harada, Keiki Takadama:
Adaptive mutation depending on program size in asynchronous program evolution. NaBIC 2011: 433-438 - 2010
- [j22]Keiki Takadama, Kazuyuki Hirose, Hiroyasu Matsushima, Kiyohiko Hattori, Nobuo Nakajima:
Learning Multiple Band-Pass Filters for Sleep Stage Estimation: Towards Care Support for Aged Persons. IEICE Trans. Commun. 93-B(4): 811-818 (2010) - [j21]Atsushi Otaki, Kiyohiko Hattori, Keiki Takadama:
Toward Strategic Human Skill Development Through Human and Agent Interaction: Improving Negotiation Skill by Interacting with Bargaining Agent. J. Adv. Comput. Intell. Intell. Informatics 14(7): 831-839 (2010) - [c25]Hiroyasu Matsushima, Kiyohiko Hattori, Hiroyuki Sato, Keiki Takadama:
Dynamic matching range in Exemplar-based Learning Classifier System. IEEE Congress on Evolutionary Computation 2010: 1-8 - [c24]Saori Iseya, Keiki Takadama:
Evolutionary optimization for hub and spoke network based on demand and operation. NaBIC 2010: 678-683 - [c23]Tomohiro Shimada, Masayuki Otani, Hiroyasu Matsushima, Hiroyuki Sato, Kiyohiko Hattori, Keiki Takadama:
Hybrid Directional-Biased Evolutionary Algorithm for Multi-Objective Optimization. PPSN (2) 2010: 121-130 - [e5]Keiki Takadama, Claudio Cioffi-Revilla, Guillaume Deffuant:
Simulating Interacting Agents and Social Phenomena, The Second World Congress [Post-Conference Proceedings of the World Congress on Social Simulation, WCSS 2008, George Mason University, Fairfax, VA, USA, 14-17 July 2008]. Agent-Based Social Systems 7, Springer 2010, ISBN 978-4-431-99780-1 [contents]
2000 – 2009
- 2009
- [j20]Atsushi Wada, Keiki Takadama:
Analyzing Strength-Based Classifier System from Reinforcement Learning Perspective. J. Adv. Comput. Intell. Intell. Informatics 13(6): 631-639 (2009) - [j19]Atsushi Wada, Keiki Takadama:
Is Gradient Descent Update Consistent with Accuracy-Based Learning Classifier System?. J. Adv. Comput. Intell. Intell. Informatics 13(6): 640-648 (2009) - [j18]Yasuyo Hatcho, Kiyohiko Hattori, Keiki Takadama:
Time Horizon Generalization in Reinforcement Learning: Generalizing Multiple Q-Tables in Q-Learning Agents. J. Adv. Comput. Intell. Intell. Informatics 13(6): 667-674 (2009) - [j17]Hiroyasu Matsushima, Kiyohiko Hattori, Keiki Takadama:
Exemplar Generalization in Reinforcement Learning: Improving Performance with Fewer Exemplars. J. Adv. Comput. Intell. Intell. Informatics 13(6): 683-690 (2009) - [c22]Yuya Ushida, Kiyohiko Hattori, Keiki Takadama:
From My Agent to Our Agent: Exploring Collective Adaptive Agent via Barnga. Australasian Conference on Artificial Intelligence 2009: 41-51 - 2008
- [j16]Takayuki Higo, Keiki Takadama:
Maintaining Multiple Populations with Different Diversities for Evolutionary Optimization Based on Probability Models. Inf. Media Technol. 3(2): 362-374 (2008) - [j15]Juliette Rouchier, Claudio Cioffi-Revilla, Gary Polhill, Keiki Takadama:
Progress in Model-To-Model Analysis. J. Artif. Soc. Soc. Simul. 11(2) (2008) - [j14]Keiki Takadama, Tetsuro Kawai, Yuhsuke Koyama:
Micro- and Macro-Level Validation in Agent-Based Simulation: Reproduction of Human-Like Behaviors and Thinking in a Sequential Bargaining Game. J. Artif. Soc. Soc. Simul. 11(2) (2008) - [e4]Jaume Bacardit, Ester Bernadó-Mansilla, Martin V. Butz, Tim Kovacs, Xavier Llorà, Keiki Takadama:
Learning Classifier Systems, 10th International Workshop, IWLCS 2006, Seattle, MA, USA, July 8, 2006 and 11th International Workshop, IWLCS 2007, London, UK, July 8, 2007, Revised Selected Papers. Lecture Notes in Computer Science 4998, Springer 2008, ISBN 978-3-540-88137-7 [contents] - 2007
- [c21]Takayuki Higo, Keiki Takadama:
Hierarchical importance sampling instead of annealing. IEEE Congress on Evolutionary Computation 2007: 134-141 - [c20]Keiki Takadama, Takahiro Majima, Daisuke Watanabe, Mitsujiro Katsuhara:
Exploring Quantitative Evaluation Criteria for Service and Potentials of New Service in Transportation: Analyzing Transport Networks of Railway, Subway, and Waterbus. IDEAL 2007: 1122-1130 - [p1]Takahiro Majima, Mitujiro Katuhara, Keiki Takadama:
Analysis on Transport Networks of Railway, Subway and Waterbus in Japan. Emergent Intelligence of Networked Agents 2007: 99-113 - [e3]Tim Kovacs, Xavier Llorà, Keiki Takadama, Pier Luca Lanzi, Wolfgang Stolzmann, Stewart W. Wilson:
Learning Classifier Systems, International Workshops, IWLCS 2003-2005, Revised Selected Papers. Lecture Notes in Computer Science 4399, Springer 2007, ISBN 978-3-540-71230-5 [contents] - [e2]Luis Antunes, Keiki Takadama:
Multi-Agent-Based Simulation VII, International Workshop, MABS 2006, Hakodate, Japan, May 8, 2006, Revised and Invited Papers. Lecture Notes in Computer Science 4442, Springer 2007, ISBN 978-3-540-76536-3 [contents] - 2006
- [c19]Keiki Takadama, Tetsuro Kawai, Yuhsuke Koyama:
Can Agents Acquire Human-Like Behaviors in a Sequential Bargaining Game? - Comparison of Roth's and Q-Learning Agents -. MABS 2006: 156-171 - 2005
- [c18]Atsushi Wada, Keiki Takadama, Katsunori Shimohara:
Learning classifier system equivalent with reinforcement learning with function approximation. GECCO Workshops 2005: 92-93 - [c17]Atsushi Wada, Keiki Takadama, Katsunori Shimohara:
Counter example for Q-bucket-brigade under prediction problem. GECCO Workshops 2005: 94-99 - [c16]Hiroyasu Inoue, Keiki Takadama, Katsunori Shimohara:
Exploring XCS in multiagent environments. GECCO Workshops 2005: 109-111 - [c15]Atsushi Wada, Keiki Takadama, Katsunori Shimohara, Osamu Katai:
Analyzing Parameter Sensitivity and Classifier Representations for Real-Valued XCS. IWLCS 2005: 1-16 - [c14]Atsushi Wada, Keiki Takadama, Katsunori Shimohara:
Counter Example for Q-Bucket-Brigade Under Prediction Problem. IWLCS 2005: 128-143 - [e1]Paul Davidsson, Brian Logan, Keiki Takadama:
Multi-Agent and Multi-Agent-Based Simulation, Joint Workshop MABS 2004, New York, NY, USA, July 19, 2004, Revised Selected Papers. Lecture Notes in Computer Science 3415, Springer 2005, ISBN 3-540-25262-2 [contents] - 2004
- [c13]Kikuo Yuta, Yoshi Fujiwara, Wataru Souma, Keiki Takadama, Katsunori Shimohara, Osamu Katai:
A Partitioned Random Network Agent Model for Organizational Sectionalism Studies. JSAI Workshops 2004: 114-125 - [c12]Keiki Takadama, Hironori Fujita:
Toward Guidelines for Modeling Learning Agents in Multiagent-Based Simulation: Implications from Q-Learning and Sarsa Agents. MABS 2004: 159-172 - 2003
- [j13]Yutaka I. Leon-Suematsu, Keiki Takadama, Norberto Eiji Nawa, Katsunori Shimohara, Osamu Katai:
Analyzing the Agent-Based Model and its Implications. Adv. Complex Syst. 6(3): 331-348 (2003) - [j12]Keiki Takadama, Shuichi Matsumoto, Shinichi Nakasuka, Katsunori Shimohara:
A reinforcement learning approach to fail-safe design for multiple space robots--cooperation mechanism without communication and negotiation schemes. Adv. Robotics 17(1): 21-39 (2003) - [j11]Keiki Takadama, Takao Terano, Katsunori Shimohara:
Interpretation by Implementation for Understanding a Multiagent Organization. Comput. Math. Organ. Theory 9(1): 19-35 (2003) - [j10]Keiki Takadama, Yutaka L. Suematsu, Norikazu Sugimoto, Norberto Eiji Nawa, Katsunori Shimohara:
Cross-Element Validation in Multiagent-based Simulation: Switching Learning Mechanisms in Agents. J. Artif. Soc. Soc. Simul. 6(4) (2003) - [c11]Hiroyasu Inoue, Katsunori Shimohara, Keiki Takadama, Osamu Katai:
Acquisition of a specialty in multi-agent learning: approach from learning classifier system. CIRA 2003: 1090-1095 - [c10]Keiki Takadama, Yutaka L. Suematsu, Norikazu Sugimoto, Norberto Eiji Nawa, Katsunori Shimohara:
Towards Verification and Validation in Multiagent-Based Systems and Simulations: Analyzing Different Learning Bargaining Agents. MABS 2003: 26-42 - 2002
- [j9]Keiki Takadama, Shinichi Nakasuka, Katsunori Shimohara:
Robustness in organizational-learning oriented classifier system. Soft Comput. 6(3-4): 229-239 (2002) - [c9]Keiki Takadama, Yutaka L. Suematsu, Norberto Eiji Nawa, Katsunori Shimohara:
Cross-validation In Multiagent-based Simulation: Analyzing Evolutionary Bargaining Agents. GECCO 2002: 121-128 - [c8]Atsushi Wada, Keiki Takadama, Katsunori Shimohara, Osamu Katai:
Autonomous symbol Acquisition through Agent Communication. SEAL 2002: 711-728 - 2001
- [j8]Keiki Takadama, Katsunori Shimohara:
Interactive self-reflection and its architecture based on cellular automata. Artif. Life Robotics 5(2): 97-102 (2001) - [j7]Keiki Takadama, Hiroyasu Inoue, Katsunori Shimohara, Michio Okada, Osamu Katai:
Agent architecture based on an interactive self-reflection classifier system. Artif. Life Robotics 5(2): 103-108 (2001) - [j6]Keiki Takadama, Takao Terano, Katsunori Shimohara:
Nongovernance rather than governance in a multiagent economic society. IEEE Trans. Evol. Comput. 5(5): 535-545 (2001) - [c7]Keiki Takadama, Katsunori Shimohara:
Toward Cumulative Progress in Agent-Based Simulation. JSAI Workshops 2001: 99-109 - 2000
- [c6]Keiki Takadama, Takao Terano, Katsunori Shimohara:
Learning Classifier Systems Meet Multiagent Environments. IWLCS 2000: 192-212
1990 – 1999
- 1999
- [j5]Keiki Takadama, Takao Terano, Katsunori Shimohara, Koichi Hori, Shinichi Nakasuka:
Making Organizational Learning Operational: Implications from Learning Classifier Systems. Comput. Math. Organ. Theory 5(3): 229-252 (1999) - [j4]Keiki Takadama, Shinichi Nakasuka, Takao Terano:
An approach to printed circuit board design with organizational learning agents. Syst. Comput. Jpn. 30(11): 33-42 (1999) - [c5]Keiki Takadama, Koichiro Hajiri, Tatsuya Nomura, Katsunori Shimohara, Shinichi Nakasuka:
Grammatical Learning Model for Adaptive Collective Behaviors in Multiple Robots. Grammatical Models of Multi-Agent Systems 1999: 343-355 - [c4]Keiki Takadama, Masakazu Watabe, Katsunori Shimohara, Shinichi Nakasuka:
How to Design Good Results for Multiple Learning Agents in Scheduling Problems? PRIMA 1999: 126-140 - 1998
- [j3]Keiki Takadama, Koichiro Hajiri, Tatsuya Nomura, Katsunori Shimohara, Michio Okada, Shinichi Nakasuka:
Learning model for adaptive behaviors as an organized group of swarm robots. Artif. Life Robotics 2(3): 123-128 (1998) - [j2]Keiki Takadama, Shinichi Nakasuka, Takao Terano:
Printed Circuit Board Design via Organizational-Learning Agents. Appl. Intell. 9(1): 25-37 (1998) - [c3]Keiki Takadama, Shinichi Nakasuka, Takao Terano:
Amalyzing the Roles of Problem Solving and Learning in Organizational-Learning Oriented Classifier System. PRICAI 1998: 71-82 - [c2]Hitomi Kasahara, Keiki Takadama, Shinichi Nakasuka, Katsunori Shimohara:
Fault tolerance in a multiple robots organization based on an organizational learning model. SMC 1998: 2261-2266 - 1997
- [j1]Keiki Takadama, Koichiro Hajiri, Tatsuya Nomura, Katsunori Shimohara, Shinichi Nakasuka:
Organizational learning model for adaptive collective behaviors in multiple robots. Adv. Robotics 12(3): 243-269 (1997) - 1996
- [c1]Keiki Takadama, Shinichi Nakasuka:
Self Organization of Individual Intelligence and Emergence of Communication in an Artificial Organism Population. German Conference on Bioinformatics 1996: 322-324
Coauthor Index
[c210] [c209] [c208] [c207] [c206] [c205] [c204] [c203] [c202] [c201] [c199] [j65] [j63] [c194] [c191] [c190] [c188] [c181] [c180] [c179] [c178] [c175] [c174] [c173] [c171] [c170] [j62] [c168] [c167] [c166] [c165] [c164] [c161] [c160] [c159] [c157] [j61] [c156] [c155] [c154] [c153] [c152] [c150] [c148] [c140] [c139] [c138] [c136] [c135] [c131] [c129] [c122] [c121] [c120] [c113] [j53] [j50] [c105] [c104] [c102] [c99] [c98] [c97] [j43] [j39] [c92] [c91] [c90] [c88] [c87] [c86] [c84] [c82] [c81] [j35] [j34] [c78] [c75] [c74] [c73] [c72] [c71] [c66] [c64] [j32] [c57] [c52] [c51] [c40] [c38] [j24] [j23] [c29] [c28] [c25] [c23]
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