An interactive approach to build an influence diagram based on Neural Neworks

저작시기 1997.01 |등록일 2000.08.21 어도비 PDF (pdf) | 17페이지 | 가격 800원


1. Introduction
2. Influence diagram
3. Decision class analysis and neural network
4. Neural network based process to build an ID
5. Interactive procedure to build an ID
6. An illustrative case example
7. Conclusions
8. References


Building an Influence diagram in decision analysis is known to be a most complicated and burdensome process. The use of neural networks to generate influence diagrams in the topological level results in a good performance, the generated ID is usually not a well-formed influence diagram. Furthermore it needs more modification to be applicable to real decision problems, especially when group decision participants are involved.
This research suggests an interactive procedure to build a well-formed influence diagram from the initial influence diagram generated from neural networks which are thought to be an approximation of experts’ (explicit or implicit) interpretation of decision problem. Our procedure is composed of two phases; one is to modify the influence diagram by each decision participant, the other one is to resolve the differences of group’s influence diagram interactively. We applied our procedure to an analogous land development and conservation problems. When the problem is complicated and group decision participants are involved, this research is expected to be more useful to inexpensively model a decision problem.

참고 자료

[BUN 84] BUNN D.W., Applied Decision Analysis, McGraw-Hill, New York, 1984.
[CHU 92] CHUNG T.Y., KIM J.K., KIM S.H., Building an influence diagram in a know-
ledge based decision system, Expert Systems With Applications, vol. 4, 1992, p. 33-44.
[HOL 89] HOLTZMAN S., Intelligent Decision Systems, Addison-Wesley, MA, 1989.
[HOW 84] HOWARD R.A., The used car buyer, in Readings on the Principles and
Applications of Decision Analysis. Vol. II, ed. R.A. Howard and J.E. Matheson, Strategic
Decision Group, Menlo Park, CA, 1984.
[HOW 88] HOWARD R.A., Decision analysis: practice and promise, Management Science,
vol. 34, 1988, p. 679-695.
[KIM 91] KIM J.K., A Knowledge-Based System for Decision Analysis, Ph.D. thesis,
Department of Industrial Engineering, KAIST, Korea, 1991.
[KIM 92] KIM J.K., CHUNG T.Y., KIM S.H., A knowledge-based decision system to build
an influence diagram: KIDS, Proceedings of the First World Congres on Expert
Systems, Orlando, Florida, 1992.
[KIM 95] KIM J.K., A Study on the Development of Intelligent Decision Systems Using
Influence Diagram, Journal of the Korean OR/MS Society, vol. 20, 1995, p. 77-104.
[KIM 97] KIM J.K., PARK K.S., Neural network-based decision clas analysis for building
topological-level influence diagram, International Journal of Human-Computer Studies,
vol. 46, 1997.
[KIM 98] KIM J.K., CHU, S.C., Sensitivity Analysis in the Decision Class Analysis Using
Neural Networks, Proceedings of the Fourth World Congres on Expert Systems,
Mexico, 1998.
[OLM 84] OLMSTED, S.M., On Representing and Solving Decision Problems, Ph.D.
thesis, Department of Engineering-Economic Systems, Stanford University, 1984.
[REE 89] REED, J., Building decision models that modify decision systems, Knowledge
System Laboratory, no. KSL-89-21, Stanford University, Stanford, CA, 1989.
[RUM 86] RUMELHART D., MCLELLAND J., Parallel Distributed Processing, vol. 1.
MIT Pres, Cambridge, Mas., 1986.
[SHA 86] SHACHTER R.D., Evaluating influence diagrams, Operations Research, vol. 34,
1986, p. 871-882.
An influence diagram based on neural networks 17
[SHA 88] SHACHTER R.D., Probabilistic inference and influence diagrams, Operations
Research, vol. 36, 1988, p. 589-604.
[SON 94] SHONNENBER F.A. et al., An Architecture for Knowledge-based Construction
of Decision Models, Medical Decision Making, vol. 14, 1994, p. 27-39.
[VOL 88] VOLKEMA R., Problem Complexity and the Formulation Proces in Planning
and Design, Behavioral Science, vol. 33, 1988, p. 292-300.
[WOO 81] WOOLLEY R., PIDD M., Problem Structuring A Literature Review, Journal
of Operational Research Society, vol. 32, 1981, p. 25-63.
[ZAH 91] ZAHEDI F., An introduction to neural networks and a comparison with artificial
intelligence and expert systems, Interfaces, vol. 21, 1991, p. 25-38.
*원하는 자료를 검색 해 보세요.
  • 신경망(Neural Network) 3페이지
    1. 개 념 신경망은 인간의 뇌 구조를 이용하여 모델링된 알고리즘으로 패턴 인식(숫자 인식)등 그 활용 범위가 대단히 광범위하다. 그도 그럴 것이 인간의 뇌 구조를 모델링했는데 제대로만 될 수 있다면 못할 것이 있겠는가?..
  • 인공지능 Backpropagation Neural Network 15페이지
  • 인공지능_#4 13페이지
    어떤 정당에서는 과거 일부 유권자의 투표 성향 자료를 분석하여 미래의 새로운 유권자들에 대한 투표 성향을 예측하려고 한다. 아래의 자료는 9명의 유권자들이 과거 여당(+) 또는 야당(-)에 투표한 기록이다. 각 유권자에 대하여..
  • 성균관대 인공지능 2014년 과제 (HW4, HW5, HW6) 8페이지
    15.4 1) contingent Won(x,y) : y는 x에서 이긴 적이 있다. 2) 문법적으로 옳지 않다. 3) contingent Attends(x,y) : x는 y에 출석한다. 4) valid 5) cont..
  • 신경망 모델 발표. (Mcculloch&pitts 의 논문읽고 자세한 설명포함) 34페이지
    McCulloch-Pitts 모델 특 징 1943년 맥클럭&피츠는 생물학적 뉴런을 단순화한 인공뉴런모델을 제시! 인간의 두뇌를 논리적 서술을 구현하는 이진원소들의 결합으로 추측! 논문내용) ‘ 신경활동의 all ..
      최근 구매한 회원 학교정보 보기
      1. 최근 2주간 다운받은 회원수와 학교정보이며
         구매한 본인의 구매정보도 함께 표시됩니다.
      2. 매시 정각마다 업데이트 됩니다. (02:00 ~ 21:00)
      3. 구매자의 학교정보가 없는 경우 기타로 표시됩니다.
      4. 지식포인트 보유 시 지식포인트가 차감되며
         미보유 시 아이디당 1일 3회만 제공됩니다.
      상세하단 배너
      최근 본 자료더보기
      상세우측 배너
      An interactive approach to build an influence diagram based on Neural Neworks