• Search Research Projects
  • Search Researchers
  • How to Use
  1. Back to previous page

Research on User-Adaptive Interface that Learns to Improve its Performance

Research Project

Project/Area Number 09480065
Research Category

Grant-in-Aid for Scientific Research (B)

Allocation TypeSingle-year Grants
Section一般
Research Field Intelligent informatics
Research InstitutionOsaka University

Principal Investigator

MOTODA Hiroshi  ISIR, Osaka University, Professor, 産業科学研究所, 教授 (00283804)

Co-Investigator(Kenkyū-buntansha) HORIVCHI Tadashi  ISIR, Osaka University, Research Associate, 産業科学研究所, 助手 (50294129)
WASHIO Takashi  ISIR, Osaka University, Associate Professor, 産業科学研究所, 助教授 (00192815)
MIZOGVCH Riichiro  ISIR, Osaka University, Professor, 産業科学研究所, 教授 (20116106)
Project Period (FY) 1997 – 1999
Project Status Completed (Fiscal Year 1999)
Budget Amount *help
¥12,300,000 (Direct Cost: ¥12,300,000)
Fiscal Year 1999: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 1998: ¥4,700,000 (Direct Cost: ¥4,700,000)
Fiscal Year 1997: ¥6,500,000 (Direct Cost: ¥6,500,000)
KeywordsMachine Learning / Graph-Based Induction / Inductive Inference / Classification Rule Learning / Command Prediction / User Interface / 帰納推理
Research Abstract

Computer needs to come closer to humans if it is to be a good partner of its user as an easy-to-use tool by interpreting its user's intention, learning her preference and responding in accordance to her expertise of computer usage. This research targeted to develop a user-adaptive interface that learns user's preference from her past usage and predicts the next command. The key factor is to devise a right kind of machine learning technique that best suits to this needs. It was recognized that narrowing down the context in which the user was working is crucial, and to do this, use of the tree structured data that involve both command sequence data and process I/O data was essential. An efficient algorithm based on the notion of pairwise chunking was developed which enabled to induce a classifier in real time from a tree structured data. Evaluation results using both artificial and real data show that the prediction is accurate enough, and the implemented interface gradually improves its performance as it learns its user's preference and comes to respond differently for a different user. Further, the learning part was made an independent program and expanded to handle general graph structured data, i.e., directed/undirected graph that has colored/uncolored nodes and links with/out loops (including self-loops). Its computational complexity is confirmed to be linear to the size of graph. It was applied to finding typical patterns from WWW browsing history data provided by a commercial provider and to discovering characteristic substructures that are typical to carcinogen of organic chlorides. Both experiments gave satisfactory results.

Report

(4 results)
  • 1999 Annual Research Report   Final Research Report Summary
  • 1998 Annual Research Report
  • 1997 Annual Research Report
  • Research Products

    (38 results)

All Other

All Publications (38 results)

  • [Publications] 元田浩: "逐次ペアに基づく帰納推論"人工知能学会誌. Vol.12. 58-67 (1997)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      1999 Final Research Report Summary
  • [Publications] H. Motoda: "Machine Learning Techniques to Make Computers Easier to Use"Journal of Artificial Intelligence. Vol.103. 295-321 (1998)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      1999 Final Research Report Summary
  • [Publications] T. Horiuchi: "Graph-Based Induction for General Graph Structured Data"Proc. Of the Second International Conference on Discovery Science. 340-342 (1999)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      1999 Final Research Report Summary
  • [Publications] T. Horiuchi: "Characterization of Default Knowledge in Ripple Down Rules Method"Proc. Of the Third Pacific-Asia Conference on Knowledge Discovery and Data Mining. 284-295 (1999)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      1999 Final Research Report Summary
  • [Publications] H. Motoda: "Discovery of laws"IEICE Transactions on Information and Systems. Vol.E00-A. 44-51 (2000)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      1999 Final Research Report Summary
  • [Publications] H. Motoda: "Special Feature on Discovery Science"New Generation Computing. Vol.18. 13-16 (2000)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      1999 Final Research Report Summary
  • [Publications] 堀内匡: "Ripple Down Rules法における知識獲得の特性評価に基づくデフォルト知識の決定規範"人工知能学会誌. Vol.15. 177-186 (2000)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      1999 Final Research Report Summary
  • [Publications] 鷲尾隆: "属性間相関ルールにもとづく属性生成法"人工知能学会誌. Vol.15. 187-197 (2000)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      1999 Final Research Report Summary
  • [Publications] K. Yoshida and H. Motoda: "Inductive Inference by Stepwise Pair Extension"Journal of Japanese Society for Artificial Intelligence. Vol.12. 58-67 (1997)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      1999 Final Research Report Summary
  • [Publications] H. Motoda and K. Yoshida: "Machine Learning Techniques to Make Computers Easier to Use"Journal of Artificial Intelligence. Vol.103. 295-321 (1998)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      1999 Final Research Report Summary
  • [Publications] T. Wada, T. Horiuchi, H. Motoda and T. Washio: "Characterization of Default Knowledge in Ripple Down Rules Method"Proc.of the Third Pacific-Asia Conference on Knowledge Discovery and Data Mining. 284-295 (1999)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      1999 Final Research Report Summary
  • [Publications] T. Matsuda, T. Horiuchi, H. Motoda, T. Washio, K. Kumazawa and N. Arai: "Graph-Based Induction for General Graph Structured Data"Proc. of the Second International Conference on Discovery Science. 340-342. (1999)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      1999 Final Research Report Summary
  • [Publications] H. Motoda and T. Washio: "Discovery of Laws"IEICE Transactions on Information and Systems. Vol.E00-A. 44-51 (2000)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      1999 Final Research Report Summary
  • [Publications] H. Motoda: "Special Feature on Discovery Science"New Generation Computing. Vol.18. 13-16 (2000)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      1999 Final Research Report Summary
  • [Publications] T. Wada, T. Horiuchi, H. Motoda and T. Washio: "Decision Criterion of Default Knowledge Based on Characterization of Knowledge Acquisition in Ripple Down Rules Method"Journal of Japanese Society for Artificial Intelligence. Vol.15. 177-186 (2000)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      1999 Final Research Report Summary
  • [Publications] M. Terabe, O. Katai, T. Sawaragi, T. Washio and H. Motoda: "Attribute Generation Based on Association Rules"Journal of Japanese Society for Artificial Intelligence. Vol.15. 187-197 (2000)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      1999 Final Research Report Summary
  • [Publications] H. Motoda: "Computer Assisted Discovery of First Principle Equations from Numeric Data"Proc. of the Third Pacific-Asia Conference on Knowledge Discovery and Data Mining. 2-2 (1999)

    • Related Report
      1999 Annual Research Report
  • [Publications] H. Motoda: "Discovery of laws"IEICE Transactions on Information and Systems. Vol.E00-A. (2000)

    • Related Report
      1999 Annual Research Report
  • [Publications] H. Motoda: "Special Feature on Discovery Science"New Generation Computing. Vol.18. 13-16 (2000)

    • Related Report
      1999 Annual Research Report
  • [Publications] 鷲尾隆: "数値属性離酸化におけるMDLPとAICの比較"第42回知識ベース研究会資料. 45-52 (1999)

    • Related Report
      1999 Annual Research Report
  • [Publications] 堀内匡: "Graph-Based Inductionの一般グラフへの拡張と World Wide Webデータへの適用"第37回人工知能基礎論研究会資料. 25-30 (1999)

    • Related Report
      1999 Annual Research Report
  • [Publications] T. Horiuchi: "Graph-Based Induction for General Graph Structured Data"Proc. of the Second International Conference on Discovery Science. 340-342 (1999)

    • Related Report
      1999 Annual Research Report
  • [Publications] T. Horiuchi: "Characterization of Default Knowledge in Ripple Down Rules Method"Proc. of the Third Pacific-Asia Conference on Knowledge Discovery and Data Mining. 284-295 (1999)

    • Related Report
      1999 Annual Research Report
  • [Publications] 堀内匡: "Ripple Down Rules法における知識獲得の特性評価に基づくデフォルト知識の決定規範"人工知能学会誌. Vol.15. 177-186 (2000)

    • Related Report
      1999 Annual Research Report
  • [Publications] H.Motoda: "Machine Learning Techniques to Make Computers Easier to USE" Journal of Artificial Intelligence. 103. 295-321 (1998)

    • Related Report
      1998 Annual Research Report
  • [Publications] 堀内 匡: "Graph-Based Induction によるコマンド予測-予測精度向上に関する枝刈りの効果-" 第12回人工知能学会全国大会論文集. 141-147 (1998)

    • Related Report
      1998 Annual Research Report
  • [Publications] 鷲尾 隆: "バスケット分析のグラフ構造データへの拡張と通信ネットワークデータへの適用" 人工知能学会第33回人工知能基礎論研究会(SIG-FAI-9801). 55-60 (1998)

    • Related Report
      1998 Annual Research Report
  • [Publications] T.Wasio: "Mining Association Rules for Estimation and Prediction" Research and Development in Knowledge Discovery and Data Mining,Lecture Notes in Artificial Intelligence. 417-419 (1998)

    • Related Report
      1998 Annual Research Report
  • [Publications] 鷲尾 隆: "数値属性データに対するバスケット分析手法" 第12回人工知能学会全国大会論文集. 74-76 (1998)

    • Related Report
      1998 Annual Research Report
  • [Publications] H.Motoda: "A Monotonic Measure for Optimal Feature Selection" Machine Learning:ECML98,Lecture Notes in Artificial Intelligence. 101-106 (1998)

    • Related Report
      1998 Annual Research Report
  • [Publications] H.Motoda: "Feature Extraction,Construction and Selection-Data Mining Perspective-" Kluwer Academic Publishers, 410 (1998)

    • Related Report
      1998 Annual Research Report
  • [Publications] H.Motoda: "Feature Selection for Knowledge Discovery and Data Mining" Kluwer Academic Publishers, 214 (1998)

    • Related Report
      1998 Annual Research Report
  • [Publications] 元田 浩: "逐次ペアに基づく帰納推論" 人工知能学会誌. 12. 58-67 (1997)

    • Related Report
      1997 Annual Research Report
  • [Publications] H.Motoda: "Extracting Behavioral Patterns from Relational History Data" Proc.of the Workshop on Machine Learning for User Modeling. 6-1-6-6 (1997)

    • Related Report
      1997 Annual Research Report
  • [Publications] H.Motoda: "Machine Learning Techiniques to Make Computers Easier to Use" Proc.of IJCAI'97 : Fifteenth International Joint Conference on Artificial Intelligence. 2. 1622-1631 (1997)

    • Related Report
      1997 Annual Research Report
  • [Publications] 鹿山俊洋: "Graph-Based Inductionによるコマンド予測-予測精度向上に関する履歴情報の定量的評価" 人工知能学会研究会資料(SIG-J-9701). 89-94 (1997)

    • Related Report
      1997 Annual Research Report
  • [Publications] 鷲尾 隆: "機械学習からみたマルチエージェント学習課程に関する考察" 計測自動制御学会システム/情報合同シンポジウム'97予稿集. 71-76 (1997)

    • Related Report
      1997 Annual Research Report
  • [Publications] T.Horiuchi: "Fuzzy Interpolation-Based Q-Learning with Profit Sharing Plan Scheme" Proc.of the 6th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'97). 3. 1707-1712 (1997)

    • Related Report
      1997 Annual Research Report

URL: 

Published: 1997-04-01   Modified: 2016-04-21  

Information User Guide FAQ News Terms of Use Attribution of KAKENHI

Powered by NII kakenhi