MARC Record from marc_columbia

LEADER: 09547cam a2200601Ii 4500
001 15119492
005 20220403000008.0
006 m o d
007 cr cnu---unuuu
008 170522s2017 flu o 000 0 eng d
035 $a(OCoLC)ocn987619436
035 $a(NNC)15119492
040 $aN$T$beng$erda$epn$cN$T$dIDEBK$dYDX$dVGM$dOCLCQ$dVLB$dRRP$dINT$dOCLCQ$dK6U$dOCLCO
020 $a9781498776271$q(electronic bk.)
020 $a1498776272$q(electronic bk.)
020 $z9781498776264
020 $z1498776264
035 $a(OCoLC)987619436
050 4 $aQ335$b.L5 2017
072 7 $aCOM$x000000$2bisacsh
082 04 $a006.3$223
049 $aZCUA
100 1 $aLi, Deyi,$d1944-$eauthor.
245 10 $aArtificial intelligence with uncertainty /$cDeyi Li and Yi Du.
264 1 $aBoca Raton :$bCRC Press,$c[2017]
300 $a1 online resource
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
588 0 $aPrint version record.
505 0 $a1.1. The Uncertainty of Human Intelligence -- 1.1.1. The Charm of Uncertainty -- 1.1.2. The World of Entropy -- 1.2. Sixty Years of Artificial Intelligence Development -- 1.2.1. The Dartmouth Symposium -- 1.2.1.1. Collision between Different Disciplines -- 1.2.1.2. Ups and Downs in Development -- 1.2.2. Goals Evolve over Time -- 1.2.2.1. Turing Test -- 1.2.2.2. Proving Theorems by Machine -- 1.2.2.3. Rivalry between Kasparov and Deep Blue -- 1.2.2.4. Thinking Machine -- 1.2.2.5. Artificial Life -- 1.2.3. Significant Achievements in AI over the Past 60 Years -- 1.3. Research Methods for Al -- 1.3.1. Symbolism -- 1.3.2. Connectionism -- 1.3.3. Behaviorism -- 1.4. Interdisciplinary Trends in AI -- 1.4.1. Brain Science and AI -- 1.4.2. Cognitive Science and Al -- 1.4.3.Network Science and AI -- 1.4.4. Great Breakthroughs to Be Achieved by Interdisciplinary Research -- References -- 2.1. Starting Points for the Study of Artificial Intelligence with Uncertainty.
505 0 $a2.1.1. Multiple Starting Points -- 2.1.2. Keeping in Mind Concepts of Natural Languages -- 2.1.3. Randomness and Fuzziness in Concepts -- 2.2. Using Cloud Models to Represent Uncertainties of Concepts -- 2.2.1. Cloud and Cloud Drops -- 2.2.2. The Cloud's Digital Characteristics -- 2.2.3. Various Types of Cloud Models -- 2.3. Algorithm of Forward Gaussian Cloud -- 2.3.1. Description -- 2.3.1.1. Forward Gaussian Cloud Algorithm -- 2.3.1.2. Two-Dimensional Forward Gaussian Algorithm -- 2.3.2. Contributions Made by Cloud Drops to the Concept -- 2.3.3. Using Gaussian Cloud Model to Understand the 24 Solar Terms in the Chinese Lunar Calendar -- 2.4. Mathematical Properties of the Gaussian Cloud -- 2.4.1. Statistical Analysis of the Distribution of Cloud Drops -- 2.4.2. Statistical Analysis of Certainty Degree of Cloud Drops -- 2.4.3. Expectation Curves of Gaussian Cloud -- 2.4.4. From Cloud to Fog -- 2.5. Algorithm of Backward Gaussian Cloud -- 2.5.1. Description.
505 0 $a2.5.1.1. Backward Gaussian Cloud Algorithm with Certainty Degree -- 2.5.1.2. Backward Cloud Algorithm Based on the First-Order Absolute Central Moment and the Second-Order Central Moment -- 2.5.1.3. Backward Cloud Algorithm Based on the Second- and Fourth-Order Central Moments of Samples -- 2.5.2. Parameter Estimation and Error Analysis of Backward Gaussian Cloud -- 2.5.2.1. Error Analysis of Ex -- 2.5.2.2. Error Analysis of En and He -- 2.5.2.3. Determining the Number of Samples under the Condition of Given Errors and Confidence Level -- 2.6. Further Understanding of the Cloud Model -- 2.6.1. Judgment Shooting -- 2.6.2. Fractal with Uncertainty -- 2.7. Universality of the Gaussian Cloud -- 2.7.1. Universality of Gaussian Distribution -- 2.7.2. Universality of Bell-Shaped Membership Function -- 2.7.3. Universal Relevance of Gaussian Cloud -- References -- 3.1. Terminology in Granular Computing -- 3.1.1. Scale, Level, and Granularity.
505 0 $a3.1.2. Concept Tree and Pan-Concept Tree -- 3.2. Gaussian Transformation -- 3.2.1. Parameter Estimation of Gaussian Transform -- 3.2.2. Gaussian Transform Algorithm -- 3.3. Gaussian Cloud Transformation -- 3.3.1. From Gaussian Transformation to Gaussian Cloud Transformation -- 3.3.2. Heuristic Gaussian Cloud Transformation -- 3.3.3. Adaptive Gaussian Cloud Transformation -- 3.3.4. High-Dimensional Gaussian Cloud Transformation -- 3.4. Gaussian Cloud Transformation for Image Segmentation -- 3.4.1. Detection of the Transition Zone in Images -- 3.4.2. Differential Object Extraction in Image -- References -- 4.1. Data Field -- 4.1.1. Using Field to Describe Interactive Objects -- 4.1.2. From Physical Field to Data Field -- 4.1.3. Potential Field and Force Field of Data -- 4.1.4. Selection of Influence Coefficient in Field Function -- 4.2. Clustering Based on Data Field -- 4.2.1. Uncertainty in Classification and Clustering -- 4.2.2. Dynamic Clustering Based on Data Field.
505 0 $a4.2.2.1. Selecting Representative Objects through Mass Estimation -- 4.2.2.2. Initial Clustering of Data Samples -- 4.2.2.3. Dynamic Clustering of Representative Objects -- 4.2.3. Expression Clustering of Human Face Images Based on Data Field -- 4.2.3.1. Feature Extraction Based on Face Image Data Field -- 4.2.3.2. Recognition of Facial Expression Cluster Based on K-L Transformation and Second- Order Data Field -- 4.3.Complex Network Research Based on Topological Potential -- 4.3.1. From Data Field to Topological Potential -- 4.3.2. Important Network Nodes Detected with Topological Potential -- 4.3.3.Network Community Discovery Based on Topological Potential -- 4.3.4. Hot Entries in Wikipedia Discovered with Topological Potential -- References -- 5.1. Cloud Reasoning -- 5.1.1. Using a Cloud Model to Construct Qualitative Rules -- 5.1.1.1. One-Dimensional Precondition Cloud Generator -- 5.1.1.2. Postcondition Cloud Generator.
505 0 $a5.1.1.3. Single-Condition-Single-Rule Generator -- 5.1.2. Generation of Rule Sets -- 5.2. Cloud Control -- 5.2.1. Mechanism of Cloud Control -- 5.2.2. Theoretical Explanation of the Mamdani Fuzzy Control Method -- 5.3. Uncertainty Control in Inverted Pendulum -- 5.3.1. Inverted Pendulum System and Its Control -- 5.3.2. Qualitative Control Mechanisms for Single-Link/ Double-Link Inverted Pendulums -- 5.3.3. Cloud Control Strategy for a Triple-Link Inverted Pendulum -- 5.3.3.1. Qualitative Analysis of the Triple-Link Inverted Pendulum System -- 5.3.3.2. The Cloud Controller of the Triple-Link Inverted Pendulum System -- 5.3.4. Balancing Patterns of the Inverted Pendulum -- 5.3.4.1. Balancing Patterns of the Single-Link Inverted Pendulum -- 5.3.4.2. Balancing Patterns of the Double-Link Inverted Pendulum -- 5.3.4.3. Balancing Patterns of the Triple-Link Inverted Pendulum -- 5.4. Uncertainty Control in Intelligent Driving -- 5.4.1. Intelligent Driving of Automobiles.
505 0 $a5.4.1.1. Integration of the Right of Way Radar Map -- 5.4.1.2. Cloud Control Strategy of Intelligent Vehicle -- 5.4.2. Driving Behavior Simulation Based on Intelligent Automobiles -- References -- 6.1. Interaction: The Important Cause of Swarm Intelligence -- 6.1.1. Swarm Intelligence -- 6.1.2. Emergence as a Form to Represent Group Behavior -- 6.2. Application of Cloud Model and Data Field in Swarm Intelligence -- 6.2.1. Cloud Model to Represent Discrete Individual Behavior -- 6.2.2. Data Field to Describe Interactions between Individuals -- 6.3. Typical Case: "Applause Sounded" -- 6.3.1. Cloud Model to Represent People's Applauding Behavior -- 6.3.1.1. Simplification and Modeling of Individual Behavior -- 6.3.1.2. Simplification and Modeling of the Environment -- 6.3.1.3. Initial Distribution and Presentation of Individual Behavior -- 6.3.2. Data Field to Reflect Mutual Spread of Applause -- 6.3.3.Computing Model for "Applause Sounded."
505 0 $a6.3.4. Experimental Platform -- 6.3.5. Diversity Analysis of Emergence -- 6.3.6. Guided Applause Synchronization -- References -- 7.1. An Insight into the Contributions and Limitations of Fuzzy Set from the Perspective of a Cloud Model -- 7.1.1. Paradoxical Argument over Fuzzy Logic -- 7.1.2. Dependence of Fuzziness on Randomness -- 7.1.3. From Fuzzy to Uncertainty Reasoning -- 7.2. From Turing Computing to Cloud Computing -- 7.2.1. Cloud Computing beyond the Turing Machine -- 7.2.2. Cloud Computing and Cloud Model -- 7.2.3. Cloud Model Walking between Gaussian and Power Law Distribution -- 7.3. Big Data Calls for AI with Uncertainties -- 7.3.1. From Database to Big Data -- 7.3.2.Network Interaction and Swarm Intelligence -- 7.4. Prospect of AI with Uncertainty -- References.
650 0 $aArtificial intelligence.
650 0 $aUncertainty (Information theory)
650 2 $aArtificial Intelligence
650 6 $aIntelligence artificielle.
650 6 $aIncertitude (Théorie de l'information)
650 7 $aartificial intelligence.$2aat
650 7 $aCOMPUTERS$xGeneral.$2bisacsh
650 7 $aArtificial intelligence.$2fast$0(OCoLC)fst00817247
650 7 $aUncertainty (Information theory)$2fast$0(OCoLC)fst01160838
655 0 $aElectronic books.
655 4 $aElectronic books.
700 1 $aDu, Yi,$d1971-$eauthor.
776 08 $iPrint version:$aLi, Deyi, 1944-$tArtificial intelligence with uncertainty.$dBoca Raton : CRC Press, [2017]$z9781498776264$w(DLC) 2016050022$w(OCoLC)953981477
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio15119492$zTaylor & Francis eBooks
852 8 $blweb$hEBOOKS