Uncertainty in artificial intelligence 4
著者
書誌事項
Uncertainty in artificial intelligence 4
(Machine intelligence and pattern recognition, v. 9)
North-Holland , Distributors for the U.S. and Canada, Elsevier Science Pub. Co., 1990
- pbk.
- タイトル別名
-
Uncertainty in artificial intelligence four
大学図書館所蔵 全5件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references
内容説明・目次
内容説明
Clearly illustrated in this volume is the current relationship between Uncertainty and AI. It has been said that research in AI revolves around five basic questions asked relative to some particular domain: What knowledge is required? How can this knowledge be acquired? How can it be represented in a system? How should this knowledge be manipulated in order to provide intelligent behavior? How can the behavior be explained? In this volume, all of these questions are addressed. From the perspective of the relationship of uncertainty to the basic questions of AI, the book divides naturally into four sections which highlight both the strengths and weaknesses of the current state of the relationship between Uncertainty and AI.
目次
I. Causal Models. On the Logic of Causal Models (D. Geiger, J. Pearl). Process, Structure, and Modularity in Reasoning with Uncertainty (B. D'Ambrosio). Probabilistic Causal Reasoning (T. Dean, K. Kanazawa). Generating Decision Structures and Causal Explanations for Decision Making (S. Star). Control of Problem Solving: Principles and Architecture (J.S. Breese, M.R. Fehling). Causal Networks: Semantics and Expressiveness (T. Verma, J. Pearl). II. Uncertainty Calculi and Comparisons. 1. Uncertainty Calculi. Stochastic Sensitivity Analysis Using Fuzzy Influence Diagrams (P. Jain, A.M. Agogino). A Linear Approximation Method for Probabilistic Inference (R.D. Shachter). Minimum Cross Entropy Reasoning in Recursive Causal Networks (W.X. Wen). Probabilistic Semantics and Defaults (E. Neufeld, D. Poole, R. Aleliunas). Modal Logics of Higher-Order Probability (P. Haddawy, A.M. Frisch). A General Non-Probabilistic Theory of Inductive Reasoning (W. Spohn). Epistemological Relevance and Statistical Knowledge (H.E. Kyburg, Jr.). Axioms for Probability and Belief-Function Propagation (P.F. Shenoy, G. Shafer). A Summary of a New Normative Theory of Probabilistic Logic (R. Aleliunas). Hierarchical Evidence and Belief Functions (P.K. Black, K.B. Laskey). On Probability Distributions over Possible Worlds (F. Bacchus). A Framework of Fuzzy Evidential Reasoning (J. Yen). 2. Comparisons. Parallel Belief Revision (D. Hunter). Evidential Reasoning Compared in a Network Usage Prediction Testbed: Preliminary Report (R.P. Loui). A Comparison of Decision Analysis and Expert Rules for Sequential Diagnosis (J. Kalagnanam, M. Henrion). An Empirical Comparison of Three Inference Methods (D. Heckerman). Modeling Uncertain and Vague Knowledge in Possibility and Evidence Theories (D. Dubois, H. Prade). Probabilistic Inference and Non-Monotonic Inference (H.E. Kyburg, Jr.). Multiple Decision Trees (S.W. Kwok, C. Carter). III. Knowledge Acquisition and Explanation. KNET: Integrating Hypermedia and Normative Bayesian Modeling (R.M. Chavez, G.F. Cooper). Generating Explanations of Decision Models Based on an Augmented Representation of Uncertainty (H.B. Jimison). IV. Applications. Induction and Uncertainty Management Techniques Applied to Veterinary Medical Diagnosis (M. Cecile, M. McLeish, P. Pascoe, W. Taylor). Predicting the Likely Behaviors of Continuous Nonlinear Systems in Equilibrium (A. Yeh). The Structure of Bayes Networks for Visual Recognition (J.M. Agosta). Utility-Based Control for Computer Vision (T.S. Levitt, T.O. Binford, G.J. Ettinger).
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