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HANDA Hisashi

    Department of Informatics　Professor
Last Updated :2023/12/05

Researcher Information

URL

J-Global ID

Research Interests

  • 分布推定アルゴリズム   協調分散型進化計算   測地距離   強化学習   強化学習問題   進化計算   知識抽出   State Space Construction   Reinforcement Learning   グラフカーネル   Anticipatory Behavior   Autonomous Robots   Growing Neural Gas Neural Networks   グラフマイニング   クラスタリング   状態分割法   レーザーレンジファインダ   3次元形状測定   鏡面物体   風土的人工物   不便益   ヒューマンインタフェース   システム設計   分節化   システムデザイン   光沢度測定   重層性   素材判別   超音波センサアレイ   人間-機械系   

Research Areas

  • Informatics / Sensitivity (kansei) informatics
  • Informatics / Soft computing
  • Informatics / Intelligent robotics
  • Informatics / Perceptual information processing
  • Manufacturing technology (mechanical, electrical/electronic, chemical engineering) / Measurement engineering
  • Manufacturing technology (mechanical, electrical/electronic, chemical engineering) / Control and systems engineering
  • Manufacturing technology (mechanical, electrical/electronic, chemical engineering) / Control and systems engineering
  • Informatics / Mechanics and mechatronics
  • Informatics / Robotics and intelligent systems

Academic & Professional Experience

  • 2019/04 - Today  Kindai UniversityFaculty of Science and Engineering Department of InformaticsProfessor
  • 2012/04 - 2019/03  Kindai UniversityFaculty of Science and Engineering, Department of Informatics准教授
  • 2007/04 - 2011/03  Okayama University大学院・自然科学研究科助教
  • 1998/04 - 2007/03  Okayama UniversityFaculty of Engineering助手

Association Memberships

  • ACM SIGEVO   THE JAPANESE SOCIETY FOR ARTIFICIAL INTELLIGENCE   IEEE   THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)   THE INSTITUTE OF ELECTRICAL ENGINEERS OF JAPAN   THE SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS   進化計算学会   

Published Papers

  • 最先端医療の今 CNNシステムによる大腸ポリープAI自動診断
    米田 頼晃; 半田 久志; 工藤 正俊
    Medical Science Digest (株)ニュー・サイエンス社 48 (3) 144 - 146 1347-4340 2022/03 
    畳み込みニューラルネットワーク(CNN:Convolutional Neural Networks)は、人工知能(AI:Artificial Intelligence)に基づく画像識別の分野で広く使用されている。この研究では、CNNによる画像鑑別する新技術の一つResidual Network(ResNet)を用いて大腸ポリープのコンピューター診断支援システム作成について行った。合計で127,610枚の画像(腺腫性ポリープを伴う62,510枚の画像、非腺腫性ポリープ(過形成ポリープなど)30,443枚の画像、および健康な大腸の正常粘膜を有する34,657枚)をAIに学習させてから10-fold cross validationによる12,761枚を使用して検証を行った。ResNetシステムの腺腫ポリープの診断をするための有効性について感度、特異度、PPV、NPV、正診率によって評価した。通常の白色光観察(WLI)において、感度、特異度、PPV、NPV、正診率はそれぞれ98.8%、94.3%、90.5%、87.4%、および92.8%であった。狭帯域光観察(NBI)あるいは色素内視鏡画像(CEI)ではNBI対CEI:感度94.9%対98.2%;特異性93.9%対85.8%;PPV92.5%対81.7%;NPV93.5%対99.9%;正診率91.5%対90.1%であった。ResNetシステムは、大腸ポリープのAI自動診断のため使用できる有力なツールであり臨床現場への導入が期待される。(著者抄録)
  • Hidekazu Tanaka; Ken Kamata; Rika Ishihara; Hisashi Handa; Yasuo Otsuka; Akihiro Yoshida; Tomoe Yoshikawa; Rei Ishikawa; Ayana Okamoto; Tomohiro Yamazaki; Atsushi Nakai; Shunsuke Omoto; Kosuke Minaga; Kentaro Yamao; Mamoru Takenaka; Tomohiro Watanabe; Naoshi Nishida; Masatoshi Kudo
    Journal of gastroenterology and hepatology 37 (5) 841 - 846 2022/01 
    BACKGROUND AND AIM: Contrast-enhanced harmonic endoscopic ultrasonography (CH-EUS) is useful for the diagnosis of lesions inside and outside the digestive tract. This study evaluated the value of artificial intelligence (AI) in the diagnosis of gastric submucosal tumors by CH-EUS. METHODS: This retrospective study included 53 patients with gastrointestinal stromal tumors (GISTs) and leiomyomas, all of whom underwent CH-EUS between June 2015 and February 2020. A novel technology, SiamMask, was used to track and trim the lesions in CH-EUS videos. CH-EUS was evaluated by AI using deep learning involving a residual neural network and leave-one-out cross-validation. The diagnostic accuracy of AI in discriminating between GISTs and leiomyomas was assessed and compared with that of blind reading by two expert endosonographers. RESULTS: Of the 53 patients, 42 had GISTs and 11 had leiomyomas. Mean tumor size was 26.4 mm. The consistency rate of the segment range of the tumor image extracted by SiamMask and marked by the endosonographer was 96% with a Dice coefficient. The sensitivity, specificity, and accuracy of AI in diagnosing GIST were 90.5%, 90.9%, and 90.6%, respectively, whereas those of blind reading were 90.5%, 81.8%, and 88.7%, respectively (P = 0.683). The κ coefficient between the two reviewers was 0.713. CONCLUSIONS: The diagnostic ability of CH-EUS results evaluated by AI to distinguish between GISTs and leiomyomas was comparable with that of blind reading by expert endosonographers.
  • Yoriaki Komeda; Hisashi Handa; Ryoma Matsui; Shohei Hatori; Riku Yamamoto; Toshiharu Sakurai; Mamoru Takenaka; Satoru Hagiwara; Naoshi Nishida; Hiroshi Kashida; Tomohiro Watanabe; Masatoshi Kudo
    PLOS ONE Public Library of Science (PLoS) 16 (6) e0253585 - e0253585 2021/06 
    Convolutional neural networks (CNNs) are widely used for artificial intelligence (AI)-based image classification. Residual network (ResNet) is a new technology that facilitates the accuracy of image classification by CNN-based AI. In this study, we developed a novel AI model combined with ResNet to diagnose colorectal polyps. In total, 127,610 images consisting of 62,510 images with adenomatous polyps, 30,443 with non-adenomatous hyperplastic polyps, and 34,657 with healthy colorectal normal mucosa were subjected to deep learning after annotation. Each validation process was performed using 12,761 stored images of colorectal polyps by a 10-fold cross validation. The efficacy of the ResNet system was evaluated by sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy. The sensitivity, specificity, PPV, NPV, and diagnostic accuracy for adenomatous polyps at WLIs were 98.8%, 94.3%, 90.5%, 87.4%, and 92.8%, respectively. Similar results were obtained for adenomatous polyps at narrow-band imagings (NBIs) and chromoendoscopy images (CEIs) (NBIs vs. CEIs: sensitivity, 94.9% vs. 98.2%; specificity, 93.9% vs. 85.8%; PPV, 92.5% vs. 81.7%; NPV, 93.5% vs. 99.9%; and overall accuracy, 91.5% vs. 90.1%). The ResNet model is a powerful tool that can be used for AI-based accurate diagnosis of colorectal polyps.
  • Yoriaki Komeda; Hisashi Handa; Ryoma Matsui; Toshiharu Sakurai; Tomohiro Watanabe; Hiroshi Kashida; Masatoshi Kudo
    Gastrointestinal Endoscopy Elsevier BV 89 (6) AB631 - AB631 0016-5107 2019/06
  • Application of Evolutionary Multiobjective Optimization in L1-Regularization of CNN
    Misaki Kitahashi; Hisashi Handa
    2129 - 2135 2019
  • Solving Order/Degree Problems by Using EDA-GK with a Novel Sampling Method
    Ryoichi Hasegawa; Hisashi Handa
    22 (2) 236 - 241 2018
  • Estimating Classroom Situations by Using CNN with Environmental Sound Spectrograms
    Misaki Kitahashi; Hisashi Handa
    Journal of Advanced Computational Intelligence and Intelligent Informatics 22 (2) 242 - 248 2018
  • Yoriaki Komeda; Hisashi Handa; Tomohiro Watanabe; Takanobu Nomura; Misaki Kitahashi; Toshiharu Sakurai; Ayana Okamoto; Tomohiro Minami; Masashi Kono; Tadaaki Arizumi; Mamoru Takenaka; Satoru Hagiwara; Shigenaga Matsui; Naoshi Nishida; Hiroshi Kashida; Masatoshi Kudo
    Oncology S. Karger AG 93 (1) 30 - 34 0030-2414 2017
  • 半田久志; 前澤健太; 長谷川陵一
    知能と情報（日本知能情報ファジィ学会誌） 日本知能情報ファジィ学会 29 (1) 14 - 19 1347-7986 2017
  • Estimation of Distribution Algorithms with Graph Kernels for Graphs with Node Types
    Kenta Maezawa; Hisashi Handa
    Proc. 20th Asia Pacific Symposium on Intelligent and Evolutionary Systems 251 - 261 2016
  • Use of RBM for Identifying Linkage Structures of Genetic Algorithms
    Hisashi Handa
    Proc. Eighth International Conference on Future Computational Technologies and Applications, Rome, Italy 41 - 46 2016
  • グラフカーネルを用いた分布推定アルゴリズム
    前澤健太; 半田久志
    進化計算学会論文誌 7 (3) 56 - 64 2016
  • Hisashi Handa
    IEEJ Transactions on Electronics, Information and Systems The Institute of Electrical Engineers of Japan 134 (11) 1738 - 1745 0385-4221 2014 
    The use of the Deep Boltzmann Machine for Neuroevolution in the case of Mario AI is discussed in this paper. The scene information in the Mario AI is transformed into a feature space generated by using the Deep Boltzmann Machine. Experimental results show that the proposed method, i.e., Neuroevolution with the Deep Boltzmann Machine outperforms the one with shallow Boltzmann Machine and other methods.
  • Efficent Evolution of the Topology of Networks by the Estimation of Distribution Algorithms with Graph Kernels
    Hisashi Handa
    IEEE International Conference on Computational Intelligence and Communication Networks, Kolkata, India 2014
  • Memetic Algorithms of Graph-Based Estimation of Distribution Algorithms
    Kenta Maezawa; Hisashi Handa
    Proc. 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems 647 - 656 2014
  • Handa Hisashi
    2014 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) IEEE 36 - 41 2014 
    Deep Learning has attracted much attention recently since it can extract features taking account into the high-order knowledge. In this paper, we examine the Deep Boltzmann Machines for scene information of the Mario AI Championship. That is, the proposed method is composed of two parts: the DBM and a recurrent neural network. The DBM extracts features behind perceptual scene information, and it learns off-line. On the other hand, the recurrent neural network utilizes features to decide actions of the Mario AI agents, and it learns on-line by using Particle Swarm Optimization. Experimental results show the effectiveness of the proposed method.
  • Mario AIにおけるDeep Boltzmann Machineを併用した進化学習
    半田久志
    電気学会論文誌C（電子・情報・システム部門誌） 134 (11) 1738 - 1745 2014
  • HANDA Hisashi
    IEEJ Transactions on Sensors and Micromachines The Institute of Electrical Engineers of Japan 133 (6) 356 - 359 1340-5551 2013/06 
    This article has no abstract.
  • Masanobu Abe; Daisuke Fujioka; Hisashi Handa
    Proceedings - 2012 6th International Conference on Complex, Intelligent, and Software Intensive Systems, CISIS 2012 665 - 670 2012/07 [Refereed]
     
    In this paper, we propose a system to collect human behavior in detail with higher-level tags such as attitude, accompanying-person, expenditure, tasks and so on. The system makes it possible to construct database to model higher-level human behaviors that are utilized in context-aware services. The system has two input methods i.e., on-the-fly by a smartphone and post-processing by a PC browser. On the PC, users can interactively know where and when they were, which results in easily and accurately constructing human behavior database. To decrease numbers of operations, users' repetitions and similarity of among users are used. According to experiment results, the system successfully decreases operation time, higher-level tags are properly collected and behavior tendencies of the users are clearly captured. © 2012 Crown Copyright.
  • Hisashi Handa
    6TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS, AND THE 13TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS IEEE 290 - 295 2012 [Refereed]
     
    We have shown that the Isomap, one of the most famous Manifold Learning method, is suitable for Neuroevolution of mobile robots with redundant inputs. In the proposed method, a large number of high dimensional inputs are collected in advance. The Manifold Learning method yields the low dimensional space. Evolutionary Learning is carried out with the low dimensional inputs, instead of the original high dimensional inputs. In this paper, the Isomap and Manifold Sculpting are compared by using Mario AI Championship.
  • Norio Baba; Yuta Arase; Masaki Takeda; Hisashi Handa
    ADVANCES IN KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS IOS PRESS 243 1991 - 1998 0922-6389 2012 [Refereed]
     
    Recently, we suggested that soft computing techniques such as NNs, MOEA-II, and FEP could be utilized for making educational games much more exciting. In this paper, we present game playing results of the original COMMONS GAME and two kinds of the modified COMMONS GAME having been carried out in several universities in Kansai area in Japan. These gaming results suggest that the modified COMMONS GAMES utilizing soft computing techniques such as NNs, MOEA-II, and FEP is by far the best in order to be utilized for letting people consider seriously about the "COMMONS" issue which is quite important not only for human beings but also all of the creatures in this planet.
  • Hisashi Handa
    2011 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) IEEE 1515 - 1520 2011 [Refereed]
     
    Mario AI is one of competitions on Computational Intelligence. In the case of video games, agents have to cope with a large number of input information in order to decide their actions at every time step. We have proposed the use of Isomap, a famous Manifold Learning, to reduce the dimensionality of inputs. Especially, we have applied it into scene information. In this paper, we newly extend to enemy information, where the number of enemies is not fixed. Hence, we introduce the proximity metrics in terms of enemies. The generated low-dimensional data is used for input values of Neural Networks. That is, at every time step, transferred data by using a map from raw inputs into the low-dimensional data are presented to Neural Networks. Experimental results on Mario AI environment show the effectiveness of the proposed approach.
  • Norio Baba; Hisashi Handa; Mariko Kusaka; Masaki Takeda; Yuriko Yoshihara; Keisuke Kogawa
    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT III SPRINGER-VERLAG BERLIN 6278 555 - + 0302-9743 2010 [Refereed]
     
    In this paper, we suggest that soft computing techniques such as NNs, MOEA-II, and FEP could be utilized for making the original COMMONS GAME much more exciting. Several game playing results by our students confirm the effectiveness of our approach.
  • Hisashi Handa; Hiroshi Kawakami
    2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) IEEE 2010 [Refereed]
     
    The optimization of the number and the alignment of sensors is quite important task for designing intelligent agents/robotics. Even though we could use excellent learning algorithms, it will not work well if the alignment of sensors is wrong or the number of sensors is not enough. In addition, if a large number of sensors are available, it will cause the delay of learning. In this paper, we propose the use of Manifold Learning for Evolutionary Learning with redundant sensory inputs in order to avoid the difficulty of designing the allocation of sensors. The proposed method is composed of two stages: The first stage is to generate a mapping from higher dimensional sensory inputs to lower dimensional space, by using Manifold Learning. The second stage is using Evolutionary Learning to learn control scheme. The input data for Evolutionary Learning is generated by translating sensory inputs into lower dimensional data by using the mapping.
  • SUTO Hidetsugu; KAWAKAMI Hiroshi; HANDA Hisashi
    Transactions of Japan Society of Kansei Engineering Japan Society of Kansei Engineering 9 (1) 11 - 18 2009 
    The accessibility of information on web-space is considered to be focused on the characteristics of hyperlinks. Although the reasons to put a hyperlink are multifarious, only one kind of link exists in HTML structure. As a result of this, users have to search for links by using syntactically techniques so as to retrieve information in web-space. In this paper, four kinds of hyper links are introduced into HTML, in order to improve the accessibility of information in web-space: Outbound links, Internal links, Navigation links and Intra-unit links. Designers and users can share the image of the link target page by using these kinds of links. Furthermore, the information flow between designers and users is described using mathematical framework, and experiments are carried out in order to verify the scheme.
  • Hisashi Handa
    2009 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS IEEE 426 - 431 2164-7143 2009 [Refereed]
     
    EDA-RL, Estimation of Distribution Algorithms for Reinforcement Learning Problems, have been proposed by us recently. The EDA-RL can improve policies by EDA scheme: First, select better episodes. Secondly, estimate probabilistic models, i.e., policies, and finally, interact with the environment for generating new episodes. In this paper, the EDA-RL is extended for Multi-Objective Reinforcement Learning Problems, where reward is given by several criteria. By incorporating the notions in Evolutionary Multi-Objective Optimization, the proposed method is enable to acquire various strategies by a single run.
  • Hisashi Handa
    2009 IEEE INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL, VOLS 1 AND 2 IEEE 118 - 123 1810-7869 2009 [Refereed]
     
    We previously proposed evolutionary fuzzy systems of playing Ms.PacMan for the competitions. As a consequence of the evolution, reflective action rules such that PacMan tries to eat pills effectively until ghosts come close to PacMan are acquired. Such rules works well. However, sometimes it is too reflective so that PacMan go toward ghosts by herself in longer corridors. In this paper, a critical situation learning module is combined with the evolved fuzzy systems, i.e., reflective action module. The critical situation learning module is composed of Q-learning with CMAC. Location information of surrounding ghosts and the existence of power-pills are given to PacMan as state. This module punishes if PacMan is caught by ghosts. Therefore, this module learning which pairs of (state, action) cause her death. By using learnt Q-value, PacMan tries to survive much longer. Experimental results on Ms.PacMan elucidate the proposed method is promising since it can capture critical situations well. However, as a consequence of the large amount of memory required by CMAC, real time responses tend to be lost.
  • Norio Baba; Kenta Nagasawa; Hisashi Handa
    KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 2, PROCEEDINGS SPRINGER-VERLAG BERLIN 5178 411 - + 0302-9743 2008 [Refereed]
     
    We have suggested that utilization of soft computing techniques such as GAs and EAs could contribute a lot for making the original COMMONS GAME much more exciting. In this paper, we try to find an answer concerning to the question "Which game is the best for letting people consider seriously about the commons among the three games (the original COMMONS GAME, the modified COMMONS GAME utilizing GAs & NNs, and the modified COMMONS GAME utilizing EAs & NNs) ?". Several game playing by our Students confirm that the modified COMMONS GAME utilizing EAs & NNs can provide the best chance for letting players consider seriously about the commons.
  • Norio Baba; Hisashi Handa
    Studies in Computational Intelligence 71 1 - 16 1860-949X 2007 [Refereed]
     
    In this paper, we suggest that Evolutionary Algorithms could be utilized in order to let the COMMONS GAME, one of the most popular environmental games, become much more exciting. In order to attain this objective, we utilize Multi-Objective Evolutionary Algorithms to generate various skilled players. Further, we suggest that Evolutionary Programming could be utilized to find out an appropriate point of each card at the COMMONS GAME. Several game playings utilizing the new rule of the COMMONS GAME confirm the effectiveness of our approach. © 2007 Springer-Verlag Berlin Heidelberg.
  • Hisashi Handa; Norio Baba
    2007 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND GAMES IEEE 334 - 339 2007 [Refereed]
     
    In this paper, we focus on game rule design by using two Evolutionary Computations. The first EC is a Multi-Objective Evolutionary Algorithm in order to generate various skilled players. By using acquired skilled players, i.e., Pareto individuals in MOEA, another EC (Evolutionary Programming) adjusts game rule parameters he, an appropriate point of each card in the COMMONS GAME.
  • Norio Baba; Hisashi Handa
    2007 INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS, VOLS 1-6 IEEE 876 - 879 2007 [Refereed]
     
    In this paper, we suggest that EAs could be utilized for making the Environmental Game become much more exciting. In particular, in order attain this objective, we try to utilize Evolutionary Algorithms in the following steps: (1) First, we consider a new way for producing a return for each player. (2) Second, we utilize Multi-Objective Evolutionary Algorithms (MOEAs) to generate various skilled players whose decision making concerning the investment to the chemical goods (and consideration toward environmental protection) can strongly affect the environmental state of the sea. (3) Further, we utilize Evolutionary Programming (EP) to derive appropriate rules which could be used to help players fully enjoy game playing. Several game playing (utilizing the new rule) by our students suggest the effectiveness of our approach.
  • Hidetsugu Suto; Hiroshi Kawakami; Hisashi Handa
    HUMAN INTERFACE AND THE MANAGEMENT OF INFORMATION: METHODS, TECHNIQUES AND TOOLS IN INFORMATION DESIGN, PT 1, PROCEEDINGS SPRINGER-VERLAG BERLIN 4557 189 - 198 0302-9743 2007 [Refereed]
     
    The accessibility of information on web-space is considered to be focused on the properties of hyperlinks. Although the reasons to put in a hyperlink are multifarious, only one kind of link exists in HTML structure. As a result of this, users have to search for links by using syntactically techniques so as to retrieve information in web-space. In this paper, four kinds of hyper links are introduced into HTML, in order to improve the accessibility of information in web-space: External links, Internal links, Navigation links and Intra-unit links. Designers and users can share the image of the link target page by using these kinds of links.
  • H Handa; O Katai
    PROCEEDINGS OF 2003 INTERNATIONAL CONFERENCE ON NEURAL NETWORKS & SIGNAL PROCESSING, PROCEEDINGS, VOLS 1 AND 2 IEEE 436 - 439 2003 [Refereed]
     
    Estimation of Bayesian Network Algorithms which adopt Bayesian Networks as the probabilistic model were one of the most sophisticated algorithms in the Estimation of Distribution Algorithms. However the estimation of Bayesian Network is key topic of this algorithm, conventional EBNAs adopt greedy. searches to search for better network structures. In this paper, we propose a new EBNA which adopts Genetic Algorithm to search the structure of Bayesian Network. In order to reduce the computational complexity of estimating better network structures, we elaborates the fitness function of the GA module, based upon the synchronicity of specific pattern in the selected individuals. Several computational simulations on multidimensional knapsack problems show us the effectiveness of the proposed method.
  • Mitsuru Baba; Tadataka Konishi; Hisashi Handa
    Systems and Computers in Japan 33 (4) 50 - 60 0882-1666 2002/04 [Refereed]
     
    In this paper, a method for measuring the shape of a columnar object with specular surfaces by a slit ray projection method is proposed. Although the slit ray projection method is effective for measuring the shape of an object having diffuse reflective characteristics, applying the method to an object with specular surfaces has hitherto been difficult. In this paper, a triangulation equation for a columnar object with specular surfaces is derived and used as the basis of a method for measuring the shape of an object having specular surfaces using applicable slit rays. The basic principle is that the angle of incidence of a slit ray reflected from the measured object into an image sensor is restricted specifically by the special design of the optical system. In addition to the theoretical study of the proposed principle, a system for measuring the shape of a columnar specular object and a diffuse object was created and its effectiveness was verified. © 2002 Wiley Periodicals, Inc. Syst. Comp. Jpn.
  • H Handa; A Ninomiya; T Horiuchi; T Konishi; M Baba
    2001 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-5 IEEE 1436 - 1441 1062-922X 2002 [Refereed]
     
    This paper applies our state construction method by ART Neural Network to Robot Navigation Problems. Agents in this paper consist of ART Neural Network and Contradiction Resolution Mechanism. The ART Neural Network serves as a mean of state recognition which maps stimulus inputs to a certain state and state construction which creates a new state when a current stimulus input cannot be categorized into any known states. On the other hand, the Contradiction Resolution Mechanism (CRM) uses agents' state transition table to detect inconsistency among constructed states. In the proposed method, two kinds of inconsistency for the CRM are introduced: "Different results caused by the same states and the same actions" and "Contradiction due to ambiguous states." The simulation results on the robot navigation problems confirm us the effectiveness of the proposed method.
  • H Handa; T Horiuchi
    SICE 2002: PROCEEDINGS OF THE 41ST SICE ANNUAL CONFERENCE, VOLS 1-5 IEEE 1609 - 1612 2002 [Refereed]
     
    In this paper, we propose a constitution method of game player agent that adopts a neural network as a state evaluation function for the game player, and evolves its weights and structure by Evolutionary Strategy (ES). In this method, we attempt to acquire better state evaluation function by evolving weights and structure simultaneously.
  • Hisashi Handa; Akira Ninomiya; Tadashi Horiuchi; Tadataka Konishi; Mitsuru Baba
    Proceedings of the IEEE International Conference on Systems, Man and Cybernetics 3 1436 - 1441 0884-3627 2001 [Refereed]
     
    This paper applies our state construction method by ART Neural Network to Robot Navigation Problems. Agents in this paper consist of ART Neural Network and Contradiction Resolution Mechanism. The ART Neural Network serves as a mean of state recognition which maps stimulus inputs to a certain state and State construction which creates a new state when a current stimulus input cannot be categorized into any known states. On the other hand, the Contradiction Resolution Mechanism (CRM) uses agents’ state transition table to detect inconsistency among constructed states. In the proposed method, two kinds of inconsistency for the CRM are introduced: “Different results caused by the same states and the same actions" and “Contradiction due to ambiguous states.” The simulation results on the robot navigation problems confirm us the effectiveness of the proposed method.
  • H Handa; T Noda; T Konishi; M Baba; O Katai
    JOINT 9TH IFSA WORLD CONGRESS AND 20TH NAFIPS INTERNATIONAL CONFERENCE, PROCEEDINGS, VOLS. 1-5 IEEE 2405 - 2410 2001 [Refereed]
     
    Recently, many researchers have studied for applying Fuzzy Classifier System (FCS) to control mobile robots, since the FCS can easily treat continuous inputs, such like sensors and images by using fuzzy number. By using the FCS, however, only reflective rules are acquired. Thus, in proposed approach, an additional Genetic Algorithm in order to search for strategic knowledge, i.e., the sequence of effective activated rules in the FCS, is incorporated. That is, proposed method consists of two modules: an ordinal FCS and the Genetic Algorithm. Computational experiments based on WEBOTS, one of Khepera robots' simulators, are confirmed us the effectiveness of the proposed method.
  • H Handa; T Horiuchi; O Katai; T Kaneko; T Konishi; M Baba
    PROCEEDINGS OF THE 2001 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2 IEEE 1213 - 1219 2001 [Refereed]
     
    In this paper, we will introduce a new Coevolutionary Genetic Algorithm with schema extraction by machine learning techniques. Our CGA consists of two GA populations: the first GA (H-GA) searches for the solutions in the given problems and the second GA (P-GA) searches for effective schemata of the H-GA. We aim to improve the search ability of our CGA by extracting more efficiently useful schemata from the H-GA population, and then incorporating those extracted schemata in natural manner into the P-GA. Several computational simulations on multidimensional knapsack problems confirm us the effectiveness of the proposed method.
  • H Handa; T Horiuchi; T Konishi; M Baba
    KNOWLEDGE-BASED INTELLIGENT INFORMATION ENGINEERING SYSTEMS & ALLIED TECHNOLOGIES, PTS 1 AND 2 I O S PRESS 69 910 - 913 0922-6389 2001 [Refereed]
     
    In game programming for Chess, Reversi, and so on, it is difficult to evaluate each state and to decide which attributes should be used for state evaluation. Hence, state evaluation functions are often designed by using heuristics of human. In this paper. we adopt a neural network as a state evaluation function for game player, and evolve its weights and structure by Evolutionary Strategy (ES). In the proposed method, we attempt to acquire better state evaluation function by evolving weights and structure simultaneously.
  • H Handa; O Katai; T Konishi; M Baba
    IECON 2000: 26TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, VOLS 1-4 IEEE 2935 - 2940 1553-572X 2000 [Refereed]
     
    We discuss on adaptability of Evolutionary Computations in dynamic environments. Hence, we introduce two classes of dynamic environments which are utilizing the notion of Constraint Satisfaction Problems: changeover and gradation. The changeover environment is a problem class which consists of a sequence Of the constraint networks with the same nature. On the other hand, the gradation environment is a problem class which consists of a sequence of the constraint networks such that the sequence is associated to two constraint networks, i.e., initial and target., and all constraint networks in the sequence metamorphosis from the initial constraint network to the target constraint network. We compare Coevolutionary Genetic Algorithms With SGA in computational simulations. Experimental results On above dynamic environments confirm us the effectiveness Of Our approach, i.e., Coevolutionary Genetic Algorithm.
  • H Handa; A Ninomiya; T Horiuchi; T Konishi; M Baba
    IECON 2000: 26TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, VOLS 1-4 IEEE 2732 - 2737 1553-572X 2000 [Refereed]
     
    In this paper, we propose a new incremental state segmentation method by utilizing information of agents state transition table which consists of tuple of (state, action. state) in order to reduce the effort of designers and which is generated by ART Neural Network. In the proposed method, if inconsistent situation in the state transition table is observed, agents refine their map from perceptual inputs to states such that such inconsistency is resolved. We introduce two kinds of inconsistency.. i.e., "Different Results Caused by the Same States and the Same Actions" and "Contradiction due to Ambiguous States." Several computational simulations on cart-pole problems confirm us the effectiveness of the proposed method.
  • H Handa; O Katai; T Konishi; M Baba
    PROCEEDINGS OF THE 2000 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2 IEEE 1184 - 1189 2000 [Refereed]
     
    In this paper, we discuss how many satisfiable solutions Genetic Algorithms can find in the problem instance of Constraint Satisfaction Problems at single execution. Hence, we propose a framework of a new fitness function which can apply to traditional fitness functions. However the mechanism of proposed fintess function is quite simple, several experimental results on a variety of instances of General Constraint Satisfaction Problems demonstrate the effectiveness of proposed fitness function.
  • H Handa; O Katai; T Konishi; M Baba
    GECCO-99: PROCEEDINGS OF THE GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE MORGAN KAUFMANN PUB INC 252 - 257 1999 [Refereed]
     
    In this paper, we discuss the adaptability of Coevolutionary Genetic Algorithms on dynamic environments. Our CGA consists of two populations: solution-level one and schema-level one. The solution-level population searches for the good solution in a given problem. The schema-level population searches for the good schemata in the former population. Our CGA performs effectively by exchanging genetic information between these populations. Also, we define Dynamic Constraint Satisfaction Problems as such dynamic environments. General CSPs are defined by two stochastic parameters: density and tightness, then, Dynamic CSPs are defined as a sequence of static constraint networks of General CSPs. Computational results on DCSPs confirm us the effectiveness of our approach.
  • H Handa; N Baba; O Katai; T Sawaragi
    PROGRESS IN CONNECTIONIST-BASED INFORMATION SYSTEMS, VOLS 1 AND 2 SPRINGER-VERLAG SINGAPORE PTE LTD 424 - 427 1998 [Refereed]
     
    A new genetic algorithm (GA) which coevolves individuals and schemata is presented. The proposed method is inspired by the idea that effective utilization of symbolized information on solution space may be useful for GA-based search for solutions. This symbolized information may be represented as schemata, and schemata can be expressed in subspaces which will have high average fitness values, i.e. building blocks. In this paper, a specialized GA model for schemata search is introduced. This GA model directory tries to search for useful schemata that have not been yet discovered by the search in GA model. Thus, the GA for schemata search is expected to guide the traditional GA-based search. Several analyses based on various computer simulations reveal the effectiveness of the proposed method.
  • Coevolutionary searching method by double-layered population with genetic information exchange
    Hisashi Handa; Osamu Katai; Norio Baba; Tetsuo Sawaragi
    Australian Journal of Intelligent Information Processing Systems 4 (3/4) 196 - 205 1998 [Refereed]

Books etc

  • Markov Networks in Evolutionary Computation, EDA-RL: EDA with Conditional Random Fields for Solving Reinforcement Learning Problems
    半田 久志 (Joint work)Springer Berlin Heidelberg 2012/05
  • ソフトコンピューティングの基礎と応用
    半田 久志 (Joint work)共立出版 2012/04 
    本書では，ニューラルネットをはじめとするファジー工学で代表されるソフトコンピューティングの基礎と応用を学ぶ。具体的な応用事例として生体信号処理とパターン認識，株価予測，コンピュータゲーミングについても詳解している。さらに遺伝的アルゴリズムや進化戦略などの進化計算についても概説し，適用範囲の広さを学べる内容となっている。さらにはファジー集合・推論・制御の基礎とその応用について概説している。また各章末には演習問題が用意されており，本書1冊でソフトコンピューティングの概要が初学者でもわかるように工夫されている。

MISC

Awards & Honors

  • 2009年度 人工知能学会 研究会優秀賞
  • 2009 ACM GECCO Best Paper Award

Other link

researchmap


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