Diagrammatic reasoning: An artificial intelligence perspective

被引:7
|
作者
Olivier, P [1 ]
机构
[1] Univ York, Dept Comp Sci, York YO10 5DD, N Yorkshire, England
关键词
diagrammatic reasoning; knowledge representation and reasoning;
D O I
10.1023/A:1006669526043
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A common motivation for developing computational frameworks for diagrammatic reasoning is the hope that they might serve as re-configurable tools for studying human problem solving performance. Despite the ongoing debate as to the precise mechanisms by which diagrams, or any other external representation, are used in human problem solving, there is little doubt that diagrammatic representations considerably help humans solve certain classes of problems. In fact, there are a host of applications of diagrams and diagrammatic representations in computing, from data presentation to visual programming languages. In contrast to both the use of diagrams in human problem solving and the ubiquitous use of diagrams in the computing industry, the topic of this review is the use of diagrammatic representations in automated problem solving. We therefore investigate the common, and often implicit, assumption that if diagrams are so useful for human problem solving and are so apparent in human endeavour, then there must be analogous computational devices of similar utility.
引用
收藏
页码:63 / 78
页数:16
相关论文
共 50 条
  • [1] Diagrammatic Reasoning: An Artificial Intelligence Perspective
    Patrick Olivier
    [J]. Artificial Intelligence Review, 2001, 15 : 63 - 78
  • [2] QUALITATIVE REASONING ABOUT PHYSICAL SYSTEMS - AN ARTIFICIAL-INTELLIGENCE PERSPECTIVE
    TOP, JL
    AKKERMANS, JM
    BREEDVELD, PC
    [J]. JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 1991, 328 (5-6): : 1047 - 1065
  • [3] Economic reasoning and artificial intelligence
    Parkes, David C.
    Wellman, Michael P.
    [J]. SCIENCE, 2015, 349 (6245) : 267 - 272
  • [4] Tractable reasoning in artificial intelligence
    不详
    [J]. TRACTABLE REASONING IN ARTIFICIAL INTELLIGENCE, 1995, 941 : 133 - 139
  • [5] A Perspective on Artificial Intelligence
    Kirton, Carl A.
    [J]. AMERICAN JOURNAL OF NURSING, 2023, 123 (10) : 5 - 5
  • [6] Mathematical Reasoning Challenges Artificial Intelligence
    O'Neill, Sean
    [J]. ENGINEERING, 2019, 5 (05) : 817 - 818
  • [7] NONMONOTONIC REASONING IN ARTIFICIAL-INTELLIGENCE
    PEQUENO, T
    [J]. JOURNAL OF SYMBOLIC LOGIC, 1986, 51 (04) : 1101 - 1101
  • [8] ARTIFICIAL INTELLIGENCE Deep neural reasoning
    Jaeger, Herbert
    [J]. NATURE, 2016, 538 (7626) : 467 - 468
  • [9] Reasoning and interaction for social artificial intelligence
    Black, Elizabeth
    Brandao, Martim
    Cocarascu, Oana
    De Keijzer, Bart
    Du, Yali
    Long, Derek
    Luck, Michael
    McBurney, Peter
    Merono-Penuela, Albert
    Miles, Simon
    Modgil, Sanjay
    Moreau, Luc
    Polukarov, Maria
    Rodrigues, Odinaldo
    Ventre, Carmine
    [J]. AI COMMUNICATIONS, 2022, 35 (04) : 309 - 325
  • [10] Diagrammatic reasoning and cases
    Anderson, M
    McCartney, R
    [J]. PROCEEDINGS OF THE THIRTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE, VOLS 1 AND 2, 1996, : 1004 - 1009