| 2026 | GECCO | When Should Helper Functions Based on Fourier Transform be Terminated in Evolutionary Multi-Objective Optimization via Multi-objectivization? | Hiroto Imajo, Hitoshi Iba, Keiki Takadama |
| 2026 | GECCO | Simultaneous Approximation of Constraint-Specific Pareto Fronts by Constraint-Decomposed Push and Pull Search. | Ryo Takamiya, Minami Miyakawa, Keiki Takadama, Hiroyuki Sato |
| 2026 | HCI | Parkinson's Disease Detection Based on BCG Respiration Stability. | Ryuki Ishizawa, Naoki Takao, Makoto Shiraishi, Keiki Takadama |
| 2026 | ICAART | Meta Deep Reinforcement Learning Based on Supervised Learning of Correspondence between Model Parameters and Reward Functions as Externally Conditioned Queries. | Takumi Kuitani, Hiroyuki Sato, Keiki Takadama |
| 2025 | CEC | Limitation of Adapting to Continuously Changing Optimization Problem and Its Solution in Swarm Optimization. | Shoei Fujita, Ryuki Ishizawa, Hiroyuki Sato, Keiki Takadama |
| 2025 | CEC | Controlling an Exploration in Unbounded Search Space by Novelty-Based Multi-Objectivization. | Ryuki Ishizawa, Hiroyuki Sato, Keiki Takadama |
| 2025 | CEC | Multi-objective optimization of flight schedules considering Constraint Tolerance based on Local Search and Archives. | Tomoki Ishizuka, Hiroyuki Sato, Keiki Takadama |
| 2025 | CEC | Multitask Knapsack Problems with Scalable Objective and Constraint Similarities: Behavioral Analysis of Evolutionary Multi-Factorial Algorithms. | Shio Kawakami, Keiki Takadama, Hiroyuki Sato |
| 2025 | GECCO | Adaptive Multi-Population Dynamic Optimization for Multimodal Dynamic Function Optimization. | Shoei Fujita, Ryuki Ishizawa, Hiroyuki Sato, Keiki Takadama |
| 2025 | GECCO | Evolutionary Co-Optimization of Rule Shape and Fuzziness in Rule-Based Machine Learning. | Hiroki Shiraishi, Yohei Hayamizu, Tomonori Hashiyama, Keiki Takadama, Hisao Ishibuchi, Masaya Nakata |
| 2025 | HCI | Circadian Rhythm Estimation Based on Knowledge Distillation Without Daytime Heartrate Data. | Shunnan Man, Keiki Takadama |
| 2025 | ICAART | What Kind of Information Is Needed? Multi-Agent Reinforcement Learning that Selectively Shares Information from Other Agents. | Riku Sakagami, Keiki Takadama |
| 2025 | ICAART | Action-Based Intrinsic Reward Design for Cooperative Behavior Acquisition in Multi-Agent Reinforcement Learning. | Iori Takeuchi, Keiki Takadama |
| 2024 | CEC | From Multipoint Search to Multiarea Search: Novelty-Based Multi-Objectivization for Unbounded Search Space Optimization. | Ryuki Ishizawa, Hiroyuki Sato, Keiki Takadama |
| 2024 | CEC | Evolutionary Constrained Multi-Factorial Optimization Based on Task Similarity. | Shio Kawakami, Keiki Takadama, Hiroyuki Sato |
| 2024 | CEC | Design of Generalized and Specialized Helper Objectives for Multi-objective Continuous Optimization Problems. | Keigo Mochizuki, Tomoki Ishizuka, Naoya Yatsu, Hiroyuki Sato, Keiki Takadama |
| 2024 | CEC | Pareto Front Estimation Model Optimization for Aggregative Solution Set Representation. | Naru Okumura, Keiki Takadama, Hiroyuki Sato |
| 2024 | CEC | Should Multi-objective Evolutionary Algorithms Use Always Best Non-dominated Solutions as Parents? | Kazuma Sato, Naru Okumura, Keiki Takadama, Hiroyuki Sato |
| 2024 | CEC | Push and Pull Search with Directed Mating for Constrained Multi-objective Optimization. | Ryo Takamiya, Minami Miyakawa, Keiki Takadama, Hiroyuki Sato |
| 2024 | CEC | Designing Helper Objectives in Multi-Objectivization. | Shoichiro Tanaka, Arnaud Liefooghe, Keiki Takadama, Hiroyuki Sato |
| 2024 | CEC | Prototype Generation with the sUpervised Classifier System on kNN Matching. | Naoya Yatsu, Hiroki Shiraishi, Hiroyuki Sato, Keiki Takadama |
| 2024 | GECCO | Approximating Pareto Local Optimal Solution Networks. | Shoichiro Tanaka, Gabriela Ochoa, Arnaud Liefooghe, Keiki Takadama, Hiroyuki Sato |
| 2024 | GECCO | Generating High-Dimensional Prototypes with a Classifier System by Evolving in Latent Space. | Naoya Yatsu, Hiroki Shiraishi, Hiroyuki Sato, Keiki Takadama |
| 2024 | ICAART | Multi-Agent Archive-Based Inverse Reinforcement Learning by Improving Suboptimal Experts. | Shunsuke Ueki, Keiki Takadama |
| 2024 | IJCNN | Multi-layer Cortical Learning Algorithm for Forecasting Time-series Data with Smoothly Changing Variation Patterns. | Kazushi Fujino, Keiki Takadama, Hiroyuki Sato |
| 2023 | CEC | Toward Unbounded Search Space Exploration by Particle Swarm Optimization in Multi-Modal Optimization Problem. | Ryuki Ishizawa, Tomoya Kuga, Yusuke Maekawa, Hiroyuki Sato, Keiki Takadama |
| 2023 | EMO | Pareto Front Upconvert by Iterative Estimation Modeling and Solution Sampling. | Tomoaki Takagi, Keiki Takadama, Hiroyuki Sato |
| 2023 | GECCO | Exploring High-dimensional Rules Indirectly via Latent Space Through a Dimensionality Reduction for XCS. | Naoya Yatsu, Hiroki Shiraishi, Hiroyuki Sato, Keiki Takadama |
| 2023 | HCI | Personalized Sleep Stage Estimation Based on Time Series Probability of Estimation for Each Label with Wearable 3-Axis Accelerometer. | Iko Nakari, Masahiro Nakashima, Keiki Takadama |
| 2022 | CEC | Beta Distribution based XCS Classifier System. | Hiroki Shiraishi, Yohei Havamizu, Hiroyuki Sato, Keiki Takadama |
| 2022 | CEC | Supervised Multi-Objective Optimization Algorithm Using Estimation. | Tomoaki Takagi, Keiki Takadama, Hiroyuki Sato |
| 2022 | CEC | Impacts of Single-objective Landscapes on Multi-objective Optimization. | Shoichiro Tanaka, Keiki Takadama, Hiroyuki Sato |
| 2022 | CEC | XCSR with VAE using Gaussian Distribution Matching: From Point to Area Matching in Latent Space for Less-overlapped Rule Generation in Observation Space. | Naoya Yatsu, Hiroki Shiraishi, Hiroyuki Sato, Keiki Takadama |
| 2022 | GECCO | Absumption based on overgenerality and condition-clustering based specialization for XCS with continuous-valued inputs. | Hiroki Shiraishi, Yohei Hayamizu, Hiroyuki Sato, Keiki Takadama |
| 2022 | GECCO | Can the same rule representation change its matching area?: enhancing representation in XCS for continuous space by probability distribution in multiple dimension. | Hiroki Shiraishi, Yohei Hayamizu, Hiroyuki Sato, Keiki Takadama |
| 2022 | HCI | Design of Human-Agent-Group Interaction for Correct Opinion Sharing on Social Media. | Fumito Uwano, Daiki Yamane, Keiki Takadama |
| 2021 | CEC | Increasing Accuracy and Interpretability of High-Dimensional Rules for Learning Classifier System. | Hiroki Shiraishi, Masakazu Tadokoro, Yohei Hayamizu, Yukiko Fukumoto, Hiroyuki Sato, Keiki Takadama |
| 2021 | CEC | XCS with Weight-based Matching in VAE Latent Space and Additional Learning of High-Dimensional Data. | Masakazu Tadokoro, Hiroyuki Sato, Keiki Takadama |
| 2021 | CEC | Weight Vector Arrangement Using Virtual Objective Vectors in Decomposition-based MOEA. | Tomoaki Takagi, Keiki Takadama, Hiroyuki Sato |
| 2021 | EMO | Pareto Front Estimation Using Unit Hyperplane. | Tomoaki Takagi, Keiki Takadama, Hiroyuki Sato |
| 2021 | GECCO | Misclassification detection based on conditional VAE for rule evolution in learning classifier system. | Hiroki Shiraishi, Masakazu Tadokoro, Yohei Hayamizu, Yukiko Fukumoto, Hiroyuki Sato, Keiki Takadama |
| 2021 | HCI | Analyzing Early Stage of Forming a Consensus from Viewpoint of Majority/Minority Decision in Online-Barnga. | Yoshimiki Maekawa, Tomohiro Yamaguchi, Keiki Takadama |
| 2020 | CEC | Local Covering: Adaptive Rule Generation Method Using Existing Rules for XCS. | Masakazu Tadokoro, Satoshi Hasegawa, Takato Tatsumi, Hiroyuki Sato, Keiki Takadama |
| 2020 | CEC | Non-dominated Solution Sampling Using Environmental Selection in EMO algorithms. | Tomoaki Takagi, Keiki Takadama, Hiroyuki Sato |
| 2020 | GECCO | Preliminary study of adaptive grid-based decomposition on many-objective evolutionary optimization. | Kensuke Kano, Tomoaki Takagi, Keiki Takadama, Hiroyuki Sato |
| 2020 | GECCO | Incremental lattice design of weight vector set. | Tomoaki Takagi, Keiki Takadama, Hiroyuki Sato |
| 2020 | GECCO | Visual mapping of multi-objective optimization problems and evolutionary algorithms. | Kohei Yamamoto, Tomoaki Takagi, Keiki Takadama, Hiroyuki Sato |
| 2020 | HCI | How to Emote for Consensus Building in Virtual Communication. | Yoshimiki Maekawa, Fumito Uwano, Eiki Kitajima, Keiki Takadama |
| 2020 | HIS | Distance Minimization Problems for Multi-factorial Evolutionary Optimization Benchmarking. | Shio Kawakami, Tomoaki Takagi, Keiki Takadama, Hiroyuki Sato |
| 2019 | CEC | Niche Radius Adaptation in Bat Algorithm for Locating Multiple Optima in Multimodal Functions. | Takuya Iwase, Ryo Takano, Fumito Uwano, Hiroyuki Sato, Keiki Takadama |
| 2019 | CEC | Knowledge Extraction from XCSR Based on Dimensionality Reduction and Deep Generative Models. | Masakazu Tadokoro, Satoshi Hasegawa, Takato Tatsumi, Hiroyuki Sato, Keiki Takadama |
| 2019 | CEC | Complex-Valued-based Learning Classifier System for POMDP Environments. | Keiki Takadama, Daichi Yamazaki, Masaya Nakata, Hiroyuki Sato |
| 2019 | CEC | Comparison of Statistical Table- and Non-Statistical Table-based XCS in Noisy Environments. | Takato Tatsumi, Keiki Takadama |
| 2019 | EMO | Evolving Generalized Solutions for Robust Multi-objective Optimization: Transportation Analysis in Disaster. | Keiki Takadama, Keiji Sato, Hiroyuki Sato |
| 2019 | GECCO | XCS-CR for handling input, output, and reward noise. | Takato Tatsumi, Keiki Takadama |
| 2019 | HCI | Model-Based Multi-objective Reinforcement Learning with Unknown Weights. | Tomohiro Yamaguchi, Shota Nagahama, Yoshihiro Ichikawa, Keiki Takadama |
| 2019 | HCI | How to Design Adaptable Agents to Obtain a Consensus with Omoiyari. | Yoshimiki Maekawa, Fumito Uwano, Eiki Kitajima, Keiki Takadama |
| 2018 | GECCO | XCSR based on compressed input by deep neural network for high dimensional data. | Kazuma Matsumoto, Ryo Takano, Takato Tatsumi, Hiroyuki Sato, Tim Kovacs, Keiki Takadama |
| 2018 | GECCO | Artificial bee colony algorithm based on adaptive local information sharing: approach for several dynamic changes. | Ryo Takano, Hiroyuki Sato, Keiki Takadama |
| 2018 | GECCO | XCS-CR: determining accuracy of classifier by its collective reward in action set toward environment with action noise. | Takato Tatsumi, Tim Kovacs, Keiki Takadama |
| 2018 | GECCO | Multiple swarm intelligence methods based on multiple population with sharing best solution for drastic environmental change. | Yuta Umenai, Fumito Uwano, Hiroyuki Sato, Keiki Takadama |
| 2018 | GECCO | Generalizing rules by random forest-based learning classifier systems for high-dimensional data mining. | Fumito Uwano, Koji Dobashi, Keiki Takadama, Tim Kovacs |
| 2018 | GECCO | Classifier generalization for comprehensive classifiers subsumption in XCS. | Caili Zhang, Takato Tatsumi, Hiyoyuki Sato, Tim Kovacs, Keiki Takadama |
| 2018 | HCI | Correcting Wrongly Determined Opinions of Agents in Opinion Sharing Model. | Eiki Kitajima, Caili Zhang, Haruyuki Ishii, Fumito Uwano, Keiki Takadama |
| 2018 | HCI | Generating Learning Environments Derived from Found Solutions by Adding Sub-goals Toward the Creative Learning Support. | Takato Okudo, Tomohiro Yamaguchi, Keiki Takadama |
| 2018 | PRIMA | Strategy for Learning Cooperative Behavior with Local Information for Multi-agent Systems. | Fumito Uwano, Keiki Takadama |
| 2017 | CEC | Performance comparison of parallel asynchronous multi-objective evolutionary algorithm with different asynchrony. | Tomohiro Harada, Keiki Takadama |
| 2017 | CEC | Applying variance-based Learning Classifier System without Convergence of Reward Estimation into various Reward distribution. | Takato Tatsumi, Hiroyuki Sato, Tim Kovacs, Keiki Takadama |
| 2017 | GECCO | A study of self-adaptive semi-asynchronous evolutionary algorithm on multi-objective optimization problem. | Tomohiro Harada, Keiki Takadama |
| 2017 | GECCO | Theoretical XCS parameter settings of learning accurate classifiers. | Masaya Nakata, Will N. Browne, Tomoki Hamagami, Keiki Takadama |
| 2017 | GECCO | An improved MOEA/D utilizing variation angles for multi-objective optimization. | Hiroyuki Sato, Minami Miyakawa, Keiki Takadama |
| 2017 | GECCO | Automatic adjustment of selection pressure based on range of reward in learning classifier system. | Takato Tatsumi, Hiroyuki Sato, Keiki Takadama |
| 2017 | HCI | Towards Adaptive Aircraft Landing Order with Aircraft Routes Partially Fixed by Air Traffic Controllers as Human Intervention. | Akinori Murata, Hiroyuki Sato, Keiki Takadama |
| 2017 | HCI | Designing the Learning Goal Space for Human Toward Acquiring a Creative Learning Skill. | Takato Okudo, Keiki Takadama, Tomohiro Yamaguchi |
| 2016 | CEC | XCS-DH: Minimal default hierarchies in XCS. | Tim Kovacs, Simon Rawles, Larry Bull, Masaya Nakata, Keiki Takadama |
| 2016 | CEC | Learning classifier system with deep autoencoder. | Kazuma Matsumoto, Yusuke Tajima, Rei Saito, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, Keiki Takadama |
| 2016 | CEC | Enhanced decomposition-based many-objective optimization using supplemental weight vectors. | Hiroyuki Sato, Satoshi Nakagawa, Minami Miyakawa, Keiki Takadama |
| 2016 | CEC | A modified cuckoo search algorithm for dynamic optimization problems. | Yuta Umenai, Fumito Uwano, Yusuke Tajima, Masaya Nakata, Hiroyuki Sato, Keiki Takadama |
| 2016 | GECCO | Variance-based Learning Classifier System without Convergence of Reward Estimation. | Takato Tatsumi, Takahiro Komine, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, Keiki Takadama |
| 2016 | HCI | Preventing Incorrect Opinion Sharing with Weighted Relationship Among Agents. | Rei Saito, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, Keiki Takadama |
| 2016 | HCI | Personalized Real-Time Sleep Stage from Past Sleep Data to Today's Sleep Estimation. | Yusuke Tajima, Tomohiro Harada, Hiroyuki Sato, Keiki Takadama |
| 2015 | CEC | Directed mating using inverted PBI function for constrained multi-objective optimization. | Minami Miyakawa, Keiki Takadama, Hiroyuki Sato |
| 2015 | CEC | How should Learning Classifier Systems cover a state-action space? | Masaya Nakata, Pier Luca Lanzi, Tim Kovacs, Will Neil Browne, Keiki Takadama |
| 2015 | CEC | Detecting shoplifting from customer behavior data by extended XCS-SL: Towards feature extraction on class-imbalanced sequence data. | Minato Sato, Kotaro Usui, Masaya Nakata, Keiki Takadama |
| 2015 | CEC | Extracting both generalized and specialized knowledge by XCS using Attribute Tracking and Feedback. | Keiki Takadama, Masaya Nakata |
| 2015 | CEC | Ship route evolutionary optimization of multiple ship companies for distributed coordination of resources. | Keiki Takadama, Hiroyuki Sato, Daisuke Watanabe, Eriko Azuma, Takahiro Majima, Mitujiro Katuhara |
| 2015 | CEC | Toward robustness against environmental change speed by artificial bee colony algorithm based on local information sharing. | Ryo Takano, Hiroyuki Sato, Tomohiro Harada, Keiki Takadama |
| 2015 | CEC | Handling different level of unstable reward environment through an estimation of reward distribution in XCS. | Takato Tatsumi, Takahiro Komine, Hiroyuki Sato, Keiki Takadama |
| 2015 | GECCO | Control of Crossed Genes Ratio for Directed Mating in Evolutionary Constrained Multi-Objective Optimization. | Minami Miyakawa, Keiki Takadama, Hiroyuki Sato |
| 2015 | GECCO | A Potential of Evolutionary Rule-based Machine Learning for Real World Applications. | Keiki Takadama |
| 2015 | IECON | Analyzing human's continuous learning ability with the reflection cost. | Tomohiro Yamaguchi, Yuki Tamai, Keiki Takadama |
| 2014 | EUROGP | Asynchronous Evolution by Reference-Based Evaluation: Tertiary Parent Selection and Its Archive. | Tomohiro Harada, Keiki Takadama |
| 2014 | GECCO | Asynchronously evolving solutions with excessively different evaluation time by reference-based evaluation. | Tomohiro Harada, Keiki Takadama |
| 2014 | GECCO | Controlling selection area of useful infeasible solutions and their archive for directed mating in evolutionary constrained multiobjective optimization. | Minami Miyakawa, Keiki Takadama, Hiroyuki Sato |
| 2014 | GECCO | A modified XCS classifier system for sequence labeling. | Masaya Nakata, Tim Kovacs, Keiki Takadama |
| 2014 | GECCO | Complete action map or best action map in accuracy-based reinforcement learning classifier systems. | Masaya Nakata, Pier Luca Lanzi, Tim Kovacs, Keiki Takadama |
| 2014 | HCI | Favor Information Presentation and Its Effect for Collective-Adaptive Situation. | Asami Mori, Tomohiro Harada, Yoshihiro Ichikawa, Keiki Takadama |
| 2014 | HCI | Visualizing Mental Learning Processes with Invisible Mazes for Continuous Learning. | Tomohiro Yamaguchi, Kouki Takemori, Keiki Takadama |
| 2014 | PPSN | Messy Coding in the XCS Classifier System for Sequence Labeling. | Masaya Nakata, Tim Kovacs, Keiki Takadama |
| 2014 | SMC | Multiagent-based ABC algorithm for Autonomous Rescue Agent Cooperation. | Rya Takano, D. Yamazaki, Yoshihiro Ichikawa, Kiyohiko Hattori, Keiki Takadama |
| 2013 | CEC | Simple compact genetic algorithm for XCS. | Masaya Nakata, Pier Luca Lanzi, Keiki Takadama |
| 2013 | EUROGP | Asynchronous Evaluation Based Genetic Programming: Comparison of Asynchronous and Synchronous Evaluation and Its Analysis. | Tomohiro Harada, Keiki Takadama |
| 2013 | GECCO | Two-stage non-dominated sorting and directed mating for solving problems with multi-objectives and constraints. | Minami Miyakawa, Keiki Takadama, Hiroyuki Sato |
| 2013 | GECCO | Selection strategy for XCS with adaptive action mapping. | Masaya Nakata, Pier Luca Lanzi, Keiki Takadama |
| 2013 | HCI | Towards Understanding of Relationship among Pareto Optimal Solutions in Multi-dimensional Space via Interactive System. | Keiki Takadama, Yuya Sawadaishi, Tomohiro Harada, Yoshihiro Ichikawa, Keiji Sato, Kiyohiko Hattori, Hiroyuki Sato, Tomohiro Yamaguchi |
| 2013 | HCI | Modeling a Human's Learning Processes to Support Continuous Learning on Human Computer Interaction. | Kouki Takemori, Tomohiro Yamaguchi, Kazuki Sasaji, Keiki Takadama |
| 2012 | PPSN | Enhancing Learning Capabilities by XCS with Best Action Mapping. | Masaya Nakata, Pier Luca Lanzi, Keiki Takadama |
| 2011 | HCI | What Kinds of Human Negotiation Skill Can Be Acquired by Changing Negotiation Order of Bargaining Agents? | Keiki Takadama, Atsushi Otaki, Keiji Sato, Hiroyasu Matsushima, Masayuki Otani, Yoshihiro Ichikawa, Kiyohiko Hattori, Hiroyuki Sato |
| 2011 | ICMLA | The Biased Multi-objective Optimization Using the Reference Point: Toward the Industrial Logistics Network. | Eriko Azuma, Tomohiro Shimada, Keiki Takadama, Hiroyuki Sato, Kiyohiko Hattori |
| 2010 | CEC | Dynamic matching range in Exemplar-based Learning Classifier System. | Hiroyasu Matsushima, Kiyohiko Hattori, Hiroyuki Sato, Keiki Takadama |
| 2010 | PPSN | Hybrid Directional-Biased Evolutionary Algorithm for Multi-Objective Optimization. | Tomohiro Shimada, Masayuki Otani, Hiroyasu Matsushima, Hiroyuki Sato, Kiyohiko Hattori, Keiki Takadama |
| 2007 | CEC | Hierarchical importance sampling instead of annealing. | Takayuki Higo, Keiki Takadama |
| 2007 | IDEAL | Exploring Quantitative Evaluation Criteria for Service and Potentials of New Service in Transportation: Analyzing Transport Networks of Railway, Subway, and Waterbus. | Keiki Takadama, Takahiro Majima, Daisuke Watanabe, Mitsujiro Katsuhara |
| 2006 | MABS | Can Agents Acquire Human-Like Behaviors in a Sequential Bargaining Game? - Comparison of Roth's and Q-Learning Agents -. | Keiki Takadama, Tetsuro Kawai, Yuhsuke Koyama |
| 2005 | GECCO | Exploring XCS in multiagent environments. | Hiroyasu Inoue, Keiki Takadama, Katsunori Shimohara |
| 2005 | GECCO | Learning classifier system equivalent with reinforcement learning with function approximation. | Atsushi Wada, Keiki Takadama, Katsunori Shimohara |
| 2005 | GECCO | Counter example for Q-bucket-brigade under prediction problem. | Atsushi Wada, Keiki Takadama, Katsunori Shimohara |
| 2004 | MABS | Toward Guidelines for Modeling Learning Agents in Multiagent-Based Simulation: Implications from Q-Learning and Sarsa Agents. | Keiki Takadama, Hironori Fujita |
| 2003 | MABS | Towards Verification and Validation in Multiagent-Based Systems and Simulations: Analyzing Different Learning Bargaining Agents. | Keiki Takadama, Yutaka L. Suematsu, Norikazu Sugimoto, Norberto Eiji Nawa, Katsunori Shimohara |
| 2002 | GECCO | Cross-validation In Multiagent-based Simulation: Analyzing Evolutionary Bargaining Agents. | Keiki Takadama, Yutaka L. Suematsu, Norberto Eiji Nawa, Katsunori Shimohara |
| 1999 | PRICAI | How to Design Good Results for Multiple Learning Agents in Scheduling Problems? | Keiki Takadama, Masakazu Watabe, Katsunori Shimohara, Shinichi Nakasuka |
| 1998 | PRICAI | Amalyzing the Roles of Problem Solving and Learning in Organizational-Learning Oriented Classifier System. | Keiki Takadama, Shinichi Nakasuka, Takao Terano |
| 1998 | SMC | Fault tolerance in a multiple robots organization based on an organizational learning model. | Hitomi Kasahara, Keiki Takadama, Shinichi Nakasuka, Katsunori Shimohara |