Guided self-organization: inception
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Heidelberg [u.a.]
Springer
2014
|
| Schriftenreihe: | Emergence, complexity and computation
9 |
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | Literaturangaben |
| Beschreibung: | XXII, 475 S. graph. Darst. 24 cm |
| ISBN: | 3642537332 9783642537332 |
Internformat
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| 245 | 1 | 0 | |a Guided self-organization |b inception |c Mikhail Prokopenko ed. |
| 264 | 1 | |a Heidelberg [u.a.] |b Springer |c 2014 | |
| 300 | |a XXII, 475 S. |b graph. Darst. |c 24 cm | ||
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Datensatz im Suchindex
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CONTENTS
PART I: INTRODUCTION
1 ON THE CROSS-DISCIPLINARY NATURE OF GUIDED SELF-ORGANISATION 3
MIKHAIL PROKOPENKO, DANIEL POLANI, NIHAT AY
1.1 INTRODUCTION 3
1.2 BACKGROUND 4
1.3 STRUCTURE 6
1.4 FOUNDATIONAL FRAMEWORKS OF GSO 7
1.5 COORDINATED BEHAVIOUR AND LEARNING WITHIN AN EMBODIED
AGENT 9
1.6 SWARMS AND NETWORKS OF AGENTS 10
1.7 CONCLUSION 12
REFERENCES 13
PART II: FOUNDATIONAL FRAMEWORKS
2 INFORMATION MEASURES OF COMPLEXITY, EMERGENCE,
SELF-ORGANIZATION, HOMEOSTASIS, AND AUTOPOIESIS 19
NELSON FERNANDEZ, CARLOS MALDONADO, CARLOS GERSHENSON
2.1 INTRODUCTION 19
2.2 BACKGROUND 20
2.2.1 COMPLEXITY 20
2.2.2 EMERGENCE 21
2.2.3 SELF-ORGANIZATION 21
2.2.4 HOMEOSTASIS 22
2.2.5 AUTOPOIESIS 23
2.2.6 INFORMATION THEORY 23
2.2.7 RANDOM BOOLEAN NETWORKS 25
2.2.8 LIMNOLOGY 25
2.3 MEASURES 26
2.3.1 EMERGENCE 27
HTTP://D-NB.INFO/1044796162
X CONTENTS
2.3.2 SELF-ORGANIZATION 28
2.3.3 COMPLEXITY 29
2.3.4 HOMEOSTASIS 30
2.3.5 AUTOPOIESIS 32
2.3.6 MULTI-SCALE PROFILES 33
2.4 RESULTS 33
2.4.1 RANDOM BOOLEAN NETWORKS 33
2.4.2 AN ECOLOGICAL SYSTEM: AN ARCTIC LAKE 35
2.5 DISCUSSION 43
2.5.1 MEASURES 43
2.5.2 COMPLEXITY AS BALANCE OR ENTROPY? 43
2.5.3 FISHER INFORMATION 46
2.5.4 TSALLIS ENTROPY 46
2.5.5 GUIDED SELF-ORGANIZATION 46
2.5.6 SCALES 47
2.5.7 NORMALIZATION 47
2.5.8 AUTOPOIESIS AND REQUISITE VARIETY 47
2.6 CONCLUSIONS 47
REFERENCES 48
3 GENERATING FUNCTIONALS FOR GUIDED SELF-ORGANIZATION 53
CLAUDIUS GROS
3.1 CONTROLLING COMPLEX SYSTEMS 53
3.2 GUIDING SELF-ORGANIZATION 55
3.3 GENERATING FUNCTIONALS 56
3.4 EQUATIONS OF MOTION 58
3.5 ADAPTIVE PHASE SPACE 60
3.6 SELF-ORGANIZED DYNAMICAL STATES 62
3.7 DISCUSSION 64
REFERENCES 65
4 EMPOWERMENT * AN INTRODUCTION 67
CHRISTOPH SALGE, CORNELIUS GLACKIN, DANIEL POLANI
4.1 INTRODUCTION 67
4.1.1 OVERVIEW 69
4.2 RELATED WORK 69
4.2.1 INTRINSIC MOTIVATION 70
4.3 EMPOWERMENT HYPOTHESES 72
4.3.1 BEHAVIOURAL EMPOWERMENT HYPOTHESIS 72
4.3.2 EVOLUTIONARY EMPOWERMENT HYPOTHESIS 74
4.3.3 AI EMPOWERMENT HYPOTHESIS 74
4.4 FORMALISM 75
4.4.1 THE CAUSAL INTERPRETATION OF EMPOWERMENT 76
4.4.2 EMPOWERMENT IN THE PERCEPTION ACTION LOOP 77
4.4.3 N-STEP EMPOWERMENT 79
4.4.4 CONTEXT-DEPENDENT EMPOWERMENT 79
CONTENTS XI
4.4.5 OPEN VS. CLOSED-LOOP EMPOWERMENT 81
4.4.6 DISCRETE DETERMINISTIC EMPOWERMENT 81
4.4.7 NON-DETERMINISTIC EMPOWERMENT CALCULATION 82
4.5 DISCRETE EXAMPLES 83
4.5.1 MAZE 83
4.5.2 AVERAGE DISTANCE VS. EMPOWERMENT 83
4.5.3 SENSOR AND ACTUATOR SELECTION 85
4.5.4 HORIZON EXTENSION 87
4.5.5 IMPOVERISHED EMPOWERMENT 88
4.5.6 SENSOR AND ACTUATOR EVOLUTION 89
4.5.7 MULTI-AGENT EMPOWERMENT 91
4.6 CONTINUOUS EMPOWERMENT 92
4.6.1 CONTINUOUS INFORMATION THEORY 93
4.6.2 INFINITE CHANNEL CAPACITY 94
4.6.3 CONTINUOUS EMPOWERMENT APPROXIMATION 95
4.6.4 BINNING 95
4.6.5 EVALUATION OF BINNING 96
4.6.6 JUNG'S MONTE CARLO INTEGRATION 96
4.6.7 EVALUATION OF MONTE CARLO INTEGRATION 98
4.6.8 QUASI-LINEAR GAUSSIAN APPROXIMATION 98
4.6.9 MIMO CHANNEL CAPACITY 100
4.6.10 COLOURED NOISE 101
4.6.11 EVALUATION OF QLG EMPOWERMENT 102
4.7 CONTINUOUS EXAMPLES 102
4.7.1 PENDULUM 103
4.7.2 ACTION SELECTION 103
4.7.3 RESULTING CONTROL 104
4.7.4 POWER CONSTRAINT 106
4.7.5 MODEL ACQUISITION 108
4.8 CONCLUSION 110
REFERENCES ILL
S A FRAMEWORK FOR THE LOCAL INFORMATION DYNAMICS OF DISTRIBUTED
COMPUTATION IN COMPLEX SYSTEMS 115
JOSEPH T. LIZIER, MIKHAIL PROKOPENKO, ALBERT Y. ZOMAYA
5.1 INTRODUCTION 115
5.2 INFORMATION-THEORETIC PRELIMINARIES 117
5.2.1 FUNDAMENTAL QUANTITIES 117
5.2.2 MEASURES FOR TIME-SERIES PROCESSES 118
5.2.3 LOCAL INFORMATION-THEORETIC MEASURES 120
5.3 CELLULAR AUTOMATA 121
5.3.1 INTRODUCTION TO CELLULAR AUTOMATA 121
5.3.2 COMPUTATION IN CELLULAR AUTOMATA 122
5.3.3 EXAMPLES OF DISTRIBUTED COMPUTATION IN CAS 124
XII CONTENTS
5.4 INFORMATION STORAGE 126
5.4.1 EXCESS ENTROPY AS TOTAL INFORMATION STORAGE 126
5.4.2 LOCAL EXCESS ENTROPY 129
5.4.3 ACTIVE INFORMATION STORAGE 129
5.4.4 LOCAL INFORMATION STORAGE RESULTS 130
5.5 INFORMATION TRANSFER 138
5.5.1 LOCAL TRANSFER ENTROPY 138
5.5.2 TOTAL INFORMATION, ENTROPY RATE AND COLLECTIVE
INFORMATION TRANSFER 142
5.5.3 LOCAL INFORMATION TRANSFER RESULTS 142
5.6 INFORMATION MODIFICATION 145
5.6.1 LOCAL SEPARABLE INFORMATION 145
5.6.2 LOCAL SEPARABLE INFORMATION RESULTS 147
5.6.3 OUTLOOK FOR INFORMATION MODIFICATION 148
5.7 IMPORTANCE OF COHERENT COMPUTATION 149
5.8 CONCLUSION 152
REFERENCES 153
6 QUANTIFYING SYNERGISTIC MUTUAL INFORMATION 159
VIRGIL GRIFFITH, CHRIST OF KOCH
6.1 INTRODUCTION 159
6.1.1 NOTATION 160
6.1.2 UNDERSTANDING PI-DIAGRAMS 160
6.2 INFORMATION CAN BE REDUNDANT, UNIQUE, OR SYNERGISTIC 162
6.2.1 EXAMPLE RDN: REDUNDANT INFORMATION 162
6.2.2 EXAMPLE UNQ: UNIQUE INFORMATION 163
6.2.3 EXAMPLE XOR: SYNERGISTIC INFORMATION 163
6.3 TWO EXAMPLES ELUCIDATING PROPERTIES OF SYNERGY 164
6.3.1 DUPLICATING A PREDICTOR DOES NOT CHANGE SYNERGISTIC
INFORMATION 164
6.3.2 ADDING A NEW PREDICTOR CAN DECREASE SYNERGY 166
6.4 PRIOR MEASURES OF SYNERGY 167
6.4.1 I
MAX
SYNERGY: S
MAX
(X : Y) 167
6.4.2 WHOLEMINUSSUM SYNERGY: WMS (X : Y) 167
6.4.3 CORRELATIONAL IMPORTANCE: A I (X; Y) 168
6.5 SYNERGISTIC MUTUAL INFORMATION 170
6.6 PROPERTIES OF
I
VK
172
6.7 APPLYING THE MEASURES TO OUR EXAMPLES 173
6.8 CONCLUSION 174
APPENDIX 175
A THREE EXTRA EXAMPLES 175
B CONNECTING BACK TO IN 179
C DESIRED PROPERTIES OF LY 179
D ANALYTIC UPPERBOUND ON I
VK
(X : Y) 182
E ESSENTIAL PROOFS 184
CONTENTS
XL
"
E. 1 STATE-DEPENDENT I
V
K
AN
D S
VK
'84
E.2 PROOF DUPLICATE PREDICTORS DON'T INCREASE SYNERGY . 185
E.3 PROOF OF BOUNDS OF
S
V
K
(
X
: Y) 185
F ALGEBRAIC SIMPLIFICATION OF AI 188
REFERENCES 189
PART III: COORDINATED BEHAVIOUR AND LEARNING WITHIN AN EMBODIED AGENT
7 ON THE ROLE OF EMBODIMENT FOR SELF-ORGANIZING ROBOTS:
BEHAVIOR AS BROKEN SYMMETRY 193
RALFDER
7.1 INTRODUCTION 193
7.2 VEHICLES 195
7.2.1 BRAITENBERGS IDEA 195
7.2.2 AUTISTIC VEHICLES 195
7.2.3 SYMMETRIES 197
7.3 THE BRAITENBERG MAN*FUNDAMENTAL MODES 197
7.3
.1
THE
HUMANOID
197
7.3.2 A
FUNDAMENTAL MODE OF THE
HUMANOID 198
7.4 UNSUPERVISED LEARNING FOR SELF-ORGANIZATION 200
7.4.1 LEARNING RULES FOR SELF-MODEL AND CONTROL 200
7.4.2 ANTI-HEBBIAN AND DIFFERENTIAL HEBBIAN LEARNING:
A PRODUCTIVE COMPETITION 201
7.4.3 RELATION TO INFOMAX PRINCIPLES 202
7.5 HOMEOKINESIS: BODY INSPIRED BEHAVIOR 203
7.5.1 PRINCIPLES OF ACTION 204
7.6 VEHICLES: BEHAVIOR AS BROKEN SYMMETRY 205
7.6.1 LEAST BIASED INITIALIZATION 205
7.6.2 SYMMETRY BREAKING*A RULE OF THUMB 206
7.6.3 THE AUTISTIC VEHICLE: FUNDAMENTAL MODES 206
7.6.4 SYNERGY OF LEARNING AND PHYSICAL STATE DYNAMICS 208
7.6.5 THE PATTERN FACTORY 210
7.6.6 PATTERNS AS EXPRESSIONS OF EMBODIMENT 210
7.6.7 MODES 211
7.7 THE LOOPING
HUMANOID
212
7.7.1 HIGH SYMMETRY MOTION PATTERNS 212
7.7.2 EXTERIOCEPTION MAY GUIDE SELF-ORGANIZATION 213
7.7.3 STARTING IN A MODE 215
7.8 THEHEXAPOD 215
7.8.1 MODES 215
7.8.2 PERSPECTIVES FOR GUIDANCE AND REINFORCEMENT
LEARNING 217
7.9 CONCLUDING REMARKS 217
REFERENCES 219
XIV CONTENTS
8 ROBOT LEARNING BY GUIDED SELF-ORGANIZATION 223
GEORG MARTIUS, RALFDER, J. MICHAEL HERRMANN
8.1 INTRODUCTION 223
8.2 HOMEOKINESIS 224
8.2.1 EXAMPLE OF EMERGENT BEHAVIOR 227
8.2.2 BEHAVIOR AND CRITICAL DYNAMICS IN HIGH-
DIMENSIONAL CRICKET ROBOT '. 228
8.3 GUIDED SELF-ORGANIZATION 229
8.4 GUIDANCE BY MILD SUPERVISION 232
8.4.1 INTEGRATION OF PROBLEM-SPECIFIC ERROR FUNCTIONS 232
8.4.2 DIRECT MOTOR TEACHING 233
8.4.3 DIRECT SENSOR TEACHING AND DISTAL LEARNING 235
8.5 SELF-ORGANIZED INTERACTION WITH THE ENVIRONMENT 237
8.5.1 INTEGRATION OF VISION INTO THE SENSORIMOTOR LOOP 237
8.5.2 GUIDING TOWARDS AN OBJECT 238
8.5.3 EMERGENT BEHAVIORS 239
8.5.4 ROBUSTNESS AGAINST STRUCTURAL CHANGES 241
8.6 REWARD-DRIVEN SELF-ORGANIZATION 244
8.6.1 REINFORCEMENT LEARNING AND GUIDED
SELF-ORGANIZATION 244
8.6.2 MODULATION OF BEHAVIOR IN A SPHERICAL ROBOT 245
8.7 CHANNELING SELF-ORGANIZATION 248
8.7.1 FROM SPONTANEOUS TO GUIDED SYMMETRY BREAKING 249
8.7.2 MULTIPLE MOTOR RELATIONS 250
8.7.3 GUIDING TO DIRECTED LOCOMOTION 251
8.7.4 SCALING PROPERTIES 254
8.7.5 COORDINATION OF FINGER MOVEMENTS FOR GRASPING 255
8.8 DISCUSSION 257
REFERENCES 259
9 ON THE CAUSAL STRUCTURE OF THE SENSORIMOTOR LOOP 261
NIHATAY, KEYAN ZAHEDI
9.1 INTRODUCTION 261
9.2 CAUSAL NETWORKS 262
9.2.1 THE DEFINITION OF CAUSAL NETWORKS 262
9.2.2 THE CAUSAL STRUCTURE OF THE SENSORIMOTOR LOOP 266
9.3 CAUSAL EFFECTS 268
9.3.1 THE DEFINITION OF CAUSAL EFFECTS 268
9.3.2 IDENTIFICATION OF CAUSAL EFFECTS 271
9.3.3 CAUSAL EFFECTS IN THE SENSORIMOTOR LOOP 272
9.4 INFORMATION FLOWS 274
9.4.1 INFORMATION-THEORETIC PRELIMINARIES 274
CONTENTS
XV
9.4.2 TRANSFER ENTROPY AND CAUSALITY 275
9.4.3 INFORMATION FLOWS IN THE SENSORIMOTOR LOOP 281
9.5 PREDICTIVE INFORMATION AND ITS MAXIMIZATION - AN
EXPERIMENTAL CASE STUDY 284
APPENDIX 291
REFERENCES 293
10 ACTION SWITCHING IN BRAIN-BODY-ENVIRONMENT SYSTEMS 295
ERAN AGMON
10.1 INTRODUCTION 295
10.2 ASHBY'S SELF-ORGANIZATION IN BRAIN-BODY-ENVIRONMENT
SYSTEMS 297
10.3 BEER'S ADAPTIVE BEHAVIOR PROGRAM 301
10.3.1 CTRNNS AND GENETIC ALGORITHMS 302
10.3.2 DYNAMICAL SYSTEMS THEORY 304
10.3.3 A SIMPLE CHEMOTAXIS AGENT 305
10.4 ACTION SWITCHING 306
10.4.1 EVOLVING AN ACTION SWITCHER 308
10.4.2 THE AGENT'S BEHAVIOR AND DYNAMICS 309
10.4.3 DISCUSSION OF ACTION SWITCHING 314
10.5 THE PROSPECT OF BRAIN-BODY-ENVIRONMENT SYSTEMS 315
REFERENCES 316
11 GUIDED SELF-ORGANIZATION OF INPUT-DRIVEN RECURRENT NEURAL
NETWORKS 319
OLIVER OBST, JOSCHKCI BOEDECKER
11.1 INTRODUCTION 319
11.2 ASSESSING THE COMPUTATIONAL POWER AND MECHANISMS OF
INFORMATION PROCESSING OF RESERVOIRS 322
11.2.1 INFORMATION-THEORY RELATED MEASURES 323
11.2.2 MEASURES RELATED TO LEARNING THEORY 325
11.2.3 MEASURES RELATED TO DYNAMICAL SYSTEMS THEORY 327
11.3 IMPROVING RESERVOIR INFORMATION PROCESSING CAPABILITIES
THROUGH SELF-ORGANIZED ADAPTATION 328
11.3.1 SORN: SELF-ORGANIZED OPTIMIZATION BASED ON 3
LOCAL PLASTICITY MECHANISMS 329
11.3.2 HIERARCHICAL SELF-ORGANIZING RESERVOIRS 331
11.3.3 GUIDED SELF-ORGANIZATION OF RESERVOIR INFORMATION
TRANSFER 332
11.4 QUANTIFYING TASK COMPLEXITY 334
11.5 CONCLUSION 336
REFERENCES 337
XVI
CONTENTS
PART IV: SWARMS AND NETWORKS OF AGENTS
12 MEASURING INFORMATION DYNAMICS IN SWARMS 343
JENNIFER M. MILLER, X. ROSALIND WANG, JOSEPH T. LIZIER,
MIKHAIL PROKOPENKO, LOUIS F. ROSSI
12.1 INTRODUCTION 343
12.1.1 BACKGROUND ON INFORMATION CASCADES 344
12.1.2 MOTIVATION AND OBJECTIVES 345
12.2 THREE ZONES MODEL FOR SWARMS 346
12.3 INFORMATION DYNAMICS IN SWARMS 347
12.4 RESULTS AND DISCUSSION 349
12.4.1 VARIABLE-SPEED SWARM MODEL 349
12.4.2 CONSTANT-SPEED SWARM MODEL 355
12.5 CONCLUSIONS 361
REFERENCES 362
13 GUIDING DESIGNS OF SELF-ORGANIZING SWARMS: INTERACTIVE AND
AUTOMATED APPROACHES 365
HIROKI SAYAMA
13.1 INTRODUCTION 365
13.2 MODEL: SWARM CHEMISTRY 367
13.3 INTERACTIVE APPROACH 370
13.3.1 USER EXPERIENCE 372
13.3.2 DESIGN QUALITY 373
13.4 AUTOMATED APPROACH 375
13.4.1 EXPLORING EXPERIMENTAL CONDITIONS 377
13.4.2 QUANTIFYING OBSERVED EVOLUTIONARY DYNAMICS 380
13.5 CONCLUSIONS 385
REFERENCES 385
14 MUTUAL INFORMATION AS A TASK-INDEPENDENT UTILITY FUNCTION FOR
EVOLUTIONARY ROBOTICS 389
VALERIO SPERATI, VITO TRIANNI, STEFANO NOLFI
14.1 INTRODUCTION 389
14.2 SHORT INTRODUCTION TO INFORMATION THEORY 390
14.3 RELATED WORK 392
14.4 EXPERIMENTAL SETUP 394
14.4.1 THE ROBOT AND THE NEURAL CONTROLLER 395
14.4.2 THE EVOLUTIONARY PROCESS 398
14.4.3 THE FITNESS FUNCTION 398
14.5 RESULTS 400
14.5.1 EXPERIMENT E{ 400
14.5.2 EXPERIMENT 405
14.6 CONCLUSION 410
REFERENCES 411
CONTENTS
AVI1
15
EVOLUTION OF COMPLEXITY AND NEURAL TOPOLOGIES 415
LARRY S. YAEGER
15.1 INTRODUCTION 415
15.2 COMPLEXITY 416
15.3 SIMULATION SOFTWARE 419
15.4 NATURAL SELECTION VS. RANDOM DRIFT 420
15.5 DATA GENERATION AND ACQUISITION 426
15.6 COMPLEXITY AS A FITNESS FUNCTION 427
15.7 EVOLUTIONARY TRENDS OF COMPLEXITY 428
15.8 EVOLUTIONARY TRENDS OF NETWORK TOPOLOGY 433
15.8.1 CLUSTERING COEFFICIENT 434
15.8.2 CHARACTERISTIC PATH LENGTH 435
15.8.3 GLOBAL EFFICIENCY 436
15.8.4 SMALL-WORLD INDEX 438
15.9 RELATING NEURAL COMPLEXITY TO NETWORK TOPOLOGY 439
15.9.1 CLUSTERING COEFFICIENT 440
15.9.2 CHARACTERISTIC PATH LENGTH 440
15.9.3 GLOBAL EFFICIENCY 441
15.9.4 SMALL-WORLD INDEX 442
15.10 BROADER APPLICABILITY 444
15.11 DISCUSSION AND CONCLUSIONS 446
REFERENCES 450
16 CLUSTERING AND MODULARITY IN SELF-ORGANIZED NETWORKS 455
SOMWRITA SARKAR, PETER A. ROBINSON
16.1 INTRODUCTION 455
16.2 MODULARITY OF SELF-ORGANIZED SYSTEMS 456
16.3 CHAPTER SUMMARY 457
16.4 BACKGROUND 458
16.4.1 SPECTRA AND GRAPH STRUCTURE 458
16.4.2 SPECTRAL CLUSTERING AND PARTITIONING APPROACHES 459
16.4.3 SPECTRAL FINGERPRINTS OF MODULARITY AND HIERARCHICAL
MODULARITY: ADJACENCY MATRIX 462
16.5 DETECTING THE MODULAR STRUCTURE 464
16.6 DISCUSSION AND CONCLUSION 466
REFERENCES 467
INDEX
469 |
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| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV041900256 |
| illustrated | Illustrated |
| indexdate | 2024-09-10T01:16:22Z |
| institution | BVB |
| isbn | 3642537332 9783642537332 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027343994 |
| oclc_num | 865147397 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG DE-11 |
| owner_facet | DE-473 DE-BY-UBG DE-11 |
| physical | XXII, 475 S. graph. Darst. 24 cm |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Springer |
| record_format | marc |
| series | Emergence, complexity and computation |
| series2 | Emergence, complexity and computation |
| spelling | Guided self-organization inception Mikhail Prokopenko ed. Heidelberg [u.a.] Springer 2014 XXII, 475 S. graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Emergence, complexity and computation 9 Literaturangaben Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Embodiment (DE-588)7627636-3 gnd rswk-swf Selbstorganisation (DE-588)4126830-1 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Selbstorganisation (DE-588)4126830-1 s Künstliche Intelligenz (DE-588)4033447-8 s Embodiment (DE-588)7627636-3 s DE-604 Prokopenko, Mikhail (DE-588)134014073 edt Erscheint auch als Online-Ausgabe 978-3-642-53734-9 Emergence, complexity and computation 9 (DE-604)BV041227294 9 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=4528793&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027343994&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Guided self-organization inception Emergence, complexity and computation Künstliche Intelligenz (DE-588)4033447-8 gnd Embodiment (DE-588)7627636-3 gnd Selbstorganisation (DE-588)4126830-1 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)7627636-3 (DE-588)4126830-1 (DE-588)4143413-4 |
| title | Guided self-organization inception |
| title_auth | Guided self-organization inception |
| title_exact_search | Guided self-organization inception |
| title_full | Guided self-organization inception Mikhail Prokopenko ed. |
| title_fullStr | Guided self-organization inception Mikhail Prokopenko ed. |
| title_full_unstemmed | Guided self-organization inception Mikhail Prokopenko ed. |
| title_short | Guided self-organization |
| title_sort | guided self organization inception |
| title_sub | inception |
| topic | Künstliche Intelligenz (DE-588)4033447-8 gnd Embodiment (DE-588)7627636-3 gnd Selbstorganisation (DE-588)4126830-1 gnd |
| topic_facet | Künstliche Intelligenz Embodiment Selbstorganisation Aufsatzsammlung |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=4528793&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027343994&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV041227294 |
| work_keys_str_mv | AT prokopenkomikhail guidedselforganizationinception |