LEADER 05681nam 2200709Ia 450 001 9910826593703321 005 20240416131000.0 010 $a1-281-07716-X 010 $a9786611077167 010 $a0-08-052976-3 035 $a(CKB)1000000000014921 035 $a(EBL)317212 035 $a(OCoLC)476110775 035 $a(SSID)ssj0000200192 035 $a(PQKBManifestationID)12056240 035 $a(PQKBTitleCode)TC0000200192 035 $a(PQKBWorkID)10219957 035 $a(PQKB)11571028 035 $a(OCoLC)647697011 035 $a(PPN)170243494 035 $a(FR-PaCSA)40000717 035 $a(MiAaPQ)EBC317212 035 $a(EXLCZ)991000000000014921 100 $a20080816d1995 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aDesigning intelligent machines$hVolume 2$iConcepts in artificial intelligence /$fby Jeffrey Johnson and Philip Picton 205 $a1st ed. 210 $aBoston ;$aOxford $cButterworth-Heinemann in association with the Open University$d1995 215 $a1 online resource (393 p.) 300 $a"... produced as the major components of the third-level undergraduate course Mechatronics: Designing intelligent machines, written by a Course Team at the Open University, UK." -- verso of t.p. 311 $a0-7506-2403-5 320 $aIncludes bibliographical references and index. 327 $aFront Cover; Concepts in Artificial Intelligence; Copyright Page; Contents; Preface; Overview of Volume 2; Chapter 1. Introduction; 1.1 Artificial intelligence in engineering; 1.2 Strong AI, weak AI and cognitive science; 7.3 Why build intelligence into machines?; 1.4 How much intelligence can be built into machines?; 1.5 What is artificial intelligence?; 1.6 How is AI applied to engineering in practice?; 1.7 The principles behind the applications; Chapter 2. Pattern recognition; 2.1 Introduction; 2.2 Theoretical foundations; 2.3 Relational patterns and graph matching 327 $a2.4 Hierarchical structure in pattern recognition 2.5 Data transformation in pattern recognition; 2.6 Pattern recognition using multidimensional data; 2.7 Multiple classifications and fuzzy sets; 2.8 Errors: non-recognition versus misclassification; 2.9 Rigorous procedures for training pattern recognizers; 2.10 Conclusion; Chapter 3. Search; 3.1 Introduction; 3.2 Tree search; 3.3 Calculus-based search; 3.4 Probabilistic search; 3.5 Conclusion; Chapter 4. Neural networks; 4.1 Introduction; 4.2 The artificial neural unit; 4.3 Pattern classification; 4.4 Feed forward networks 327 $a4.5 Learning in neural networks 4.6 Feedback networks; 4.7 Uses of the multi-layer perception; 4.8 Conclusion; Chapter 5. Scheduling; 5.1 Introduction; 5.2 Representation in scheduling; 5.3 Graphs and networks for representing scheduling problems; 5.4 Shortest paths; 5.5 Critical path analysis; 5.6 Critical path activity scheduling; 5.7 The 'travelling salesman problem'; 5.8 Intelligent scheduling; 5.9 Conclusion; Chapter 6. Reasoning; 6.1 introduction; 6.2 Reasoning with certainty; 6.3 Reasoning with uncertainty; 6.4 Conclusion; Chapter 7. Rule-based systems 327 $a7.1 Knowledge-based, rule-based and expert systems 7.2 Implementation; 7.3 Confidence levels and fuzzy rules; 7.4 Programming language and rule-based system shells; 7.5 Conclusion; Chapter 8. Learning; 8.1 Introduction; 8.2 Learning by memory; 8.3 Learning by updating parameters; 8.4 learning during execution using Bayesian updating; 8.5 learning from examples; 8.6 learning by analogy; 8.7 Learning by discovery; 8.8 Conclusion; Chapter 9. Intelligent control; 9.1 Introduction; 9.2 The broom-balancer; 9.3 Classical solution; 9.4 Neural network solution; 9.5 Genetic algorithms; 9.6 Fuzzy rules 327 $a9.7 Hierarchical control of complex systems 9.8 Conclusion: principles for intelligent control design; Chapter 10. Computer vision; 10.1 Introduction; 10.2 Abstracting information from digital images; 10.3 The nature of digital images; 10.4 Computer vision versus computer graphics; 10.5 Object recognition and measurement; 10.6 A summary of the basic techniques in computer vision; 10.7 A hierarchical architecture for computer vision; 10.8 Conclusion: computer vision in intelligent machines; Chapter 11. Integration; 11.1 An introduction to blackboard systems 327 $a11.2 The blackboard system as a development environment 330 $aMechatronics is the fusion of mechanics and electronics in the design of intelligent machines. This textbook is concerned with the concepts and techniques of artificial intelligence needed for the design of machines with advanced intelligent behaviour. It explores the topics of pattern recognition, neural networks, scheduling, reasoning, fuzzy logic, rule-based systems, machine learning, control and computer vision.This student guide shows how fifty years of research into artificial intelligence (AI) have borne fruit in the design of better and more intelligent machines. The twin o 606 $aMechatronics$xStudy and teaching 606 $aArtificial intelligence 606 $aEngineering design 615 0$aMechatronics$xStudy and teaching. 615 0$aArtificial intelligence. 615 0$aEngineering design. 676 $a621 676 $a629.89 676 $a629.89 700 $aJohnson$b Jeffrey$0719755 701 $aPicton$b Philip$01614725 712 02$aOpen University. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910826593703321 996 $aDesigning intelligent machines$93944641 997 $aUNINA