New Guide to Artificial Intelligence

Name: New Guide to Artificial Intelligence
SKU: 9780893916077
Price: 379 SEK
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Author: Derek Partridge
ISBN: 9780893916077

Häftad, Engelska, 1991

AvDerek Partridge

379 kr

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In this textbook the author endeavors to cover the large and growing field of artificial intelligence (AI) in some detail. While there are books that examine and discuss global perspectives on AI, they make no attempt to cover the diversity of theories and programs. A global perspective on the subject is provided by this volume, but in conjunction with an exhaustive survey of the field. It covers all recognized AI work in sufficient detail to allow a critiquie from general concerns to be anchored, whenever possible, in the structure of specific AI programs. It can be used as a supplement to other AI texts, providing broader perspectives on the wealth of details that such texts contain. It can also be considered as a companion to the current AI literature for it is only in conference proceedings and journals that these up-to-date details are usually found.

Produktinformation

Utgivningsdatum1991-05-01
FormatHäftad
SpråkEngelska
Antal sidor200
FörlagIntellect
ISBN9780893916077

Tillhör följande kategorier

Artificiell intelligens inom Data och IT

Preface xviii1 AI: what is it? 1 definitions: what would it look like if I saw one? 1 True AI Story: 1.1-ELIZA meets PARRY:the syntax is willing but the semantics is weak 7 a history of scaling down 8 categorizations of AI work 14 the goals of AI research 15 the heuristic programming approach 18 the Samuel phenomenon 22 2 AI and the Science of Computer Usage: The Forging 23of a Methodology how to use the essential tool? 23 first specify, then verify 26 the nature of AI problems 27 a methodology of incremental exploration 30 rapid prototypes to the rescue? 32 supportive environments 33 forging a new methodology 34 is AI so different? 37 3 The Major Paradigms 39 symbolic search spaces 40 planning intelligent solutions 45 SSSP infrastructure 54 the pivotal role of searching strategies 56 heuristic pruning 59 connectionism: a possible alternative? 62 connectionism: the second coming 64 on not losing their inhibitions 66 the need for decay 67 subsymbolic connectionism: the good news 68 when is an AI system like a piece of fine china? 70 subsymbolic connectionism: the real news 71 reasoning with amorphous complexity 72 the myth of empirical guidance 73 what's the stopping rule? 77 single-minded models 78 philosophical objections 79 potential solutions to the dilemma 81 formal analysis 81 software support systems 83 approximate translation-the truth about mendacity 84 the SSSP and the CP: integration, bifurcation,or annilation? 86 simulated evolution: guess and try it out 89 'bad' paradigms 90 4 The Babel of AI Languages 98 it's all done by manipulating symbols 98 LISP 100 flexibility 100 the magic of recursion 101 code-data equivalence 103 the special assignment 105 lists of properties 106 PROLOG 108 the independence of declaration 109loss of control: better or worse? 111 extralogical pollutants 116 negation as failure 120 verify or compute 120 bidirectionality 121 pattern matching 121 the promises of PROLOG 122parallelism 122 a specification language 123 heuristic controls 124 object-oriented programming 125 programming environments 131 LISP environments 131LOOPS 132 POPLOG 134 True AI -Story: 4.1. DIMWIT (Do I Mean What I Tell):A PA (Programmer's Assailant) System 136 5 Current Expert Systems Technology (CEST) 139experts with tunnel vision 140the basic assumptions and the criticisms 141what can be CESTed? 145explanations and context sensitivity 146updating knowledge bases and machine learning 150let's dig deeper 155logical decision making 159human and computer decision making 161classes of human decision making 163connectionism: a possible answer? 165knowledge elicitation 167knowledge engineers and the third degree 167automatic learning from examples 168empirical techniques 168CEST: where is it and where is it going? 1696 Knowledge Representation: A Problem of Both 171Structure and Function why networks? 171why neurons? 176pandering to evolution: beware of classical reconditioning 178neural architectures: in the beginning 179knowledge representation: structure and function 183the SSSP and the CP: representational issues 188knowledge representation in the CP 189functionally distributed representations 189symbolic connectionist representations 189winner-takes-all subnets 194hybrid connectionism 195totally distributed representations 196path-like architectures in the CP 202bath-like architectures in the CP 204knowledge representation in the SSSP 209logic-based representations 210procedural representations of knowledge 213semantic networks 217elements of structured knowledge: frames,scripts, and schemata 2267 Vision: Seeing is Perceiving 228bottoming in: operators canny, uncanny,and cannyless 233pixel processing 233 edges and lines 237 vertices or junctions 238 texture: a truly superficial feature 239 illumination, reflectance, and other sourcesof nuisance 241 the intrinsic image 241 model-based vision systems 244 True AI Story: 7.1 247 beer cans, broomsticks, etc. 248seeing as perceiving 250 oversight and hallucination 252 the modularity of human vision 255 eyeballs and nervous optics 256 biological feature detectors 256human perceptual behavior 261 breaking up context 262structuring top-down information 262 a cognitive model of word recognition 264 the eye of the robot 267 general theories of visual perception 273 the vision of connectionists 2788 Language Processing: What You Hear is What You Are 283natural language 286what mode of natural language? 287the goals of AI-NLP 289natural language: the essential ingredients 289phonetics and phonology 290the lexical level and above 291 generation and analysis 291natural language generation (NLG) 293text generation systems 297empirical guidance for NLG 298natural language understanding (NLU) 299syntax, grammars, and parsing grammars 300furious transformational grammarians sleep curiously 303 transition networks: augmented and otherwise 305unification and the new grammatism 308semantic definite clause grammars (SDCG) 309NLP and a formal complaint 314semantics 318 the meaning of semantics 319 the atomic struture of meaning 320the case of the missing-blocks world 322 True AI Story: 8.1 SHRDLU and a "SORRY" story 324 revolting computational linguists 324 scripted NLU and its dependencies 326 True AI Story: 8.2 Try it again SAM 328 the conceptual dependency notation 329 a Swale of a tale 331 True AI Story: 8.3 Another SWALE of a tale 332 giving semantics preferential treatment 333bidirectional NLP 335pragmatics? 336 machine translation (MT) 339natural language interfaces (NLI) 341networks for NLP 344 9 Learning To Do it Right 351can we have intelligence without learning? 354can we have AI without learning? 355learning paradigms in AI 356learning as the accretion of symbolic structures 358learning as the adjustment of link weights 361external tutoring: learning by being told 363learning on the path 370learning in a bath - taking the plunge 381climbing hills because the 're there 384rote learning: if it might be useful, store it 390learning generalities 391induction 393overgeneralization and refinement 395 a first guess and generalization 397 True Al story: 9.1. Underneath the arches:an everyday story of concept learning 399competitive learning 404 learning particularities: removal of unwantedgeneralization 406 EBG, or is it EBL? 409 the EBL viewpoint 416 mechanized creativity 421learning by introspection 423rediscovering things 427learning by analogy 432 learning at the knowledge level 433 soaring through search spaces 437 the more you know the slower you go 443on finding needles in haystacks 448when to learn and what to learn 448giving credit where it is due 450unlearning 45310 Foundations of AI: Can we find any? 458foundations: why dig for them? 459formal foundations 460a disinterested user's guide to the FOPC 462the curse of nonmonotonicity 467logical odds and ends 471 True AI Story: 10 .1 It is not a closed world after all 471the epilogic 474methodological foundations 477 the roles of programs in AI 478 programs as theories 479programs as experiments 483rational reconstructions in AI 483 sorting out AI methodologies 486philosophical foundations 488there's nothing special about you, or me 488building the foundations on the CP 490undermining the foundations of the CP 491total disbelief: let's not be Searle-ish 49211 Prognostications, or W(h)ither AI? 496 abstract AI and concrete AI 496 is the mind an appropriate object for scientific study? 498 True AI Story: 11.1. Sand in the works 499AI as a magnifying glass 501 AI: can it be practically useful? 503AI: just wait till we get into parallel hardware 503last words 506References 507 Author Index 531 Subject Index

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New Guide to Artificial Intelligence

Häftad, Engelska, 1991

AvDerek Partridge

379 kr

Tillfälligt slut

Produktinformation

Utgivningsdatum1991-05-01
FormatHäftad
SpråkEngelska
Antal sidor200
FörlagIntellect
ISBN9780893916077

Tillhör följande kategorier

Artificiell intelligens inom Data och IT

Preface xviii1 AI: what is it? 1 definitions: what would it look like if I saw one? 1 True AI Story: 1.1-ELIZA meets PARRY:the syntax is willing but the semantics is weak 7 a history of scaling down 8 categorizations of AI work 14 the goals of AI research 15 the heuristic programming approach 18 the Samuel phenomenon 22 2 AI and the Science of Computer Usage: The Forging 23of a Methodology how to use the essential tool? 23 first specify, then verify 26 the nature of AI problems 27 a methodology of incremental exploration 30 rapid prototypes to the rescue? 32 supportive environments 33 forging a new methodology 34 is AI so different? 37 3 The Major Paradigms 39 symbolic search spaces 40 planning intelligent solutions 45 SSSP infrastructure 54 the pivotal role of searching strategies 56 heuristic pruning 59 connectionism: a possible alternative? 62 connectionism: the second coming 64 on not losing their inhibitions 66 the need for decay 67 subsymbolic connectionism: the good news 68 when is an AI system like a piece of fine china? 70 subsymbolic connectionism: the real news 71 reasoning with amorphous complexity 72 the myth of empirical guidance 73 what's the stopping rule? 77 single-minded models 78 philosophical objections 79 potential solutions to the dilemma 81 formal analysis 81 software support systems 83 approximate translation-the truth about mendacity 84 the SSSP and the CP: integration, bifurcation,or annilation? 86 simulated evolution: guess and try it out 89 'bad' paradigms 90 4 The Babel of AI Languages 98 it's all done by manipulating symbols 98 LISP 100 flexibility 100 the magic of recursion 101 code-data equivalence 103 the special assignment 105 lists of properties 106 PROLOG 108 the independence of declaration 109loss of control: better or worse? 111 extralogical pollutants 116 negation as failure 120 verify or compute 120 bidirectionality 121 pattern matching 121 the promises of PROLOG 122parallelism 122 a specification language 123 heuristic controls 124 object-oriented programming 125 programming environments 131 LISP environments 131LOOPS 132 POPLOG 134 True AI -Story: 4.1. DIMWIT (Do I Mean What I Tell):A PA (Programmer's Assailant) System 136 5 Current Expert Systems Technology (CEST) 139experts with tunnel vision 140the basic assumptions and the criticisms 141what can be CESTed? 145explanations and context sensitivity 146updating knowledge bases and machine learning 150let's dig deeper 155logical decision making 159human and computer decision making 161classes of human decision making 163connectionism: a possible answer? 165knowledge elicitation 167knowledge engineers and the third degree 167automatic learning from examples 168empirical techniques 168CEST: where is it and where is it going? 1696 Knowledge Representation: A Problem of Both 171Structure and Function why networks? 171why neurons? 176pandering to evolution: beware of classical reconditioning 178neural architectures: in the beginning 179knowledge representation: structure and function 183the SSSP and the CP: representational issues 188knowledge representation in the CP 189functionally distributed representations 189symbolic connectionist representations 189winner-takes-all subnets 194hybrid connectionism 195totally distributed representations 196path-like architectures in the CP 202bath-like architectures in the CP 204knowledge representation in the SSSP 209logic-based representations 210procedural representations of knowledge 213semantic networks 217elements of structured knowledge: frames,scripts, and schemata 2267 Vision: Seeing is Perceiving 228bottoming in: operators canny, uncanny,and cannyless 233pixel processing 233 edges and lines 237 vertices or junctions 238 texture: a truly superficial feature 239 illumination, reflectance, and other sourcesof nuisance 241 the intrinsic image 241 model-based vision systems 244 True AI Story: 7.1 247 beer cans, broomsticks, etc. 248seeing as perceiving 250 oversight and hallucination 252 the modularity of human vision 255 eyeballs and nervous optics 256 biological feature detectors 256human perceptual behavior 261 breaking up context 262structuring top-down information 262 a cognitive model of word recognition 264 the eye of the robot 267 general theories of visual perception 273 the vision of connectionists 2788 Language Processing: What You Hear is What You Are 283natural language 286what mode of natural language? 287the goals of AI-NLP 289natural language: the essential ingredients 289phonetics and phonology 290the lexical level and above 291 generation and analysis 291natural language generation (NLG) 293text generation systems 297empirical guidance for NLG 298natural language understanding (NLU) 299syntax, grammars, and parsing grammars 300furious transformational grammarians sleep curiously 303 transition networks: augmented and otherwise 305unification and the new grammatism 308semantic definite clause grammars (SDCG) 309NLP and a formal complaint 314semantics 318 the meaning of semantics 319 the atomic struture of meaning 320the case of the missing-blocks world 322 True AI Story: 8.1 SHRDLU and a "SORRY" story 324 revolting computational linguists 324 scripted NLU and its dependencies 326 True AI Story: 8.2 Try it again SAM 328 the conceptual dependency notation 329 a Swale of a tale 331 True AI Story: 8.3 Another SWALE of a tale 332 giving semantics preferential treatment 333bidirectional NLP 335pragmatics? 336 machine translation (MT) 339natural language interfaces (NLI) 341networks for NLP 344 9 Learning To Do it Right 351can we have intelligence without learning? 354can we have AI without learning? 355learning paradigms in AI 356learning as the accretion of symbolic structures 358learning as the adjustment of link weights 361external tutoring: learning by being told 363learning on the path 370learning in a bath - taking the plunge 381climbing hills because the 're there 384rote learning: if it might be useful, store it 390learning generalities 391induction 393overgeneralization and refinement 395 a first guess and generalization 397 True Al story: 9.1. Underneath the arches:an everyday story of concept learning 399competitive learning 404 learning particularities: removal of unwantedgeneralization 406 EBG, or is it EBL? 409 the EBL viewpoint 416 mechanized creativity 421learning by introspection 423rediscovering things 427learning by analogy 432 learning at the knowledge level 433 soaring through search spaces 437 the more you know the slower you go 443on finding needles in haystacks 448when to learn and what to learn 448giving credit where it is due 450unlearning 45310 Foundations of AI: Can we find any? 458foundations: why dig for them? 459formal foundations 460a disinterested user's guide to the FOPC 462the curse of nonmonotonicity 467logical odds and ends 471 True AI Story: 10 .1 It is not a closed world after all 471the epilogic 474methodological foundations 477 the roles of programs in AI 478 programs as theories 479programs as experiments 483rational reconstructions in AI 483 sorting out AI methodologies 486philosophical foundations 488there's nothing special about you, or me 488building the foundations on the CP 490undermining the foundations of the CP 491total disbelief: let's not be Searle-ish 49211 Prognostications, or W(h)ither AI? 496 abstract AI and concrete AI 496 is the mind an appropriate object for scientific study? 498 True AI Story: 11.1. Sand in the works 499AI as a magnifying glass 501 AI: can it be practically useful? 503AI: just wait till we get into parallel hardware 503last words 506References 507 Author Index 531 Subject Index