Interactive collaborative information systems:
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin ; Heidelberg
Springer
2010
|
| Schriftenreihe: | Studies in computational intelligence
281 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturangaben |
| Beschreibung: | XXV, 585 S. Ill., graph. Darst. 24 cm |
| ISBN: | 9783642116872 |
Internformat
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| 245 | 1 | 0 | |a Interactive collaborative information systems |c Robert Babuška and Frans C. A. Groen (eds.) |
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Datensatz im Suchindex
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| adam_text | CONTENTS PART I: REINFORCEMENT LEARNING APPROXIMATE DYNAMIC PROGRAMMING
AND REINFORCEMENT LEARNING 3 LUCIAN BUFONIU, BART DE SCHUTTER, ROBERT
BABUSKA 1 INTRODUCTION 3 2 MARKOV DECISION PROCESSES: EXACT DYNAMIC
PROGRAMMING AND REINFORCEMENT LEARNING 6 2.1 MARKOV DECISION PROCESSES
AND THEIR SOLUTION .... 6 2.2 EXACT VALUE ITERATION 9 2.3 EXACT POLICY
ITERATION 10 3 THE NEED FOR APPROXIMATION IN DYNAMIC PROGRAMMING AND
REINFORCEMENT LEARNING 12 4 APPROXIMATE VALUE ITERATION 13 4.1
APPROXIMATE MODEL-BASED VALUE ITERATION 13 4.2 APPROXIMATE MODEL-FREE
VALUE ITERATION 15 4.3 CONVERGENCE AND THE ROLE OF NONEXPANSIVE
APPROXIMATORS 15 5 APPROXIMATE POLICY ITERATION 20 5.1 APPROXIMATE
POLICY EVALUATION 20 5.2 POLICY IMPROVEMENT: APPROXIMATE POLICY
ITERATION 23 5.3 THEORETICAL GUARANTEES 24 5.4 ACTOR-CRITIC ALGORITHMS
28 6 FINDING VALUE FUNCTION APPROXIMATORS AUTOMATICALLY .... 29 6.1
RESOLUTION REFINEMENT 30 6.2 BASIS FUNCTION OPTIMIZATION 31 6.3 OTHER
METHODS FOR BASIS FUNCTION CONSTRUCTION 32 BIBLIOGRAFISCHE INFORMATIONEN
HTTP://D-NB.INFO/999263498 DIGITALISIERT DURCH X CONTENTS 7 APPROXIMATE
POLICY SEARCH 33 8 COMPARISON OF APPROXIMATE VALUE ITERATION, POLICY
ITERATION, AND POLICY SEARCH 36 9 SUMMARY AND OUTLOOK 37 REFERENCES 39
LEARNING WITH WHOM TO COMMUNICATE USING RELATIONAL REINFORCEMENT
LEARNING 45 MARC PONSEN, TOM CROONENBORGHS, KARL TUYLS, JAN RAMON, KURT
DRIESSENS, JAAP VAN DEN HERIK, ERIC POSTMA 1 INTRODUCTION 46 2
REINFORCEMENT LEARNING 47 3 RELATIONAL REINFORCEMENT LEARNING 50 4
MULTI-AGENT RELATIONAL REINFORCEMENT LEARNING 51 5 EMPIRICAL EVALUATION
53 5.1 LEARNING TASK 54 5.2 EXPERIMENTAL RESULTS 55 6 CONCLUSIONS 60
REFERENCES 61 SWITCHING BETWEEN REPRESENTATIONS IN REINFORCEMENT
LEARNING 65 HARM VAN SEIJEN, SHIMON WHITESON, LEON KESTER 1 INTRODUCTION
65 2 BACKGROUND ON FACTORED MDP 67 3 FEATURE SELECTION IN FACTORED MDPS
68 3.1 FEATURE TYPES 68 3.2 CANDIDATE REPRESENTATION 69 4 REPRESENTATION
SELECTION FOR A CONTEXTUAL BANDIT 71 4.1 A CONTEXTUAL BANDIT EXAMPLE 71
4.2 CONSTRUCTING THE SWITCH REPRESENTATION 72 4.3 EVALUATION OF A
REPRESENTATION 74 4.4 IMPROVING PERFORMANCE BY OFF-POLICY UPDATING ...
74 5 REPRESENTATION SELECTION FOR AN MDP 77 5.1 OFF-POLICY UPDATING OF
THE UNSELECTED REPRESENTATIONS 77 5.2 OFF-POLICY UPDATING OF THE
UNSELECTED SWITCH ACTION CONTENTS XI PART II: COLLABORATIVE DECISION
MAKING A DECISION-THEORETIC APPROACH TO COLLABORATION: PRINCIPAL
DESCRIPTION METHODS AND EFFICIENT HEURISTIC APPROXIMATIONS 87 FRANS A.
OLIEHOEK, ARNOUD VISSER 1 INTRODUCTION 87 1.1 FORMS OF UNCERTAINTY 89
1.2 DECISION-THEORETIC APPROACH TO MASS 90 1.3 OVERVIEW 92 2 THE
OBJECTIVE APPROACH: DEC-POMDPS 93 2.1 DECENTRALIZED POMDPS 93 2.2
HISTORIES AND POLICIES 96 2.3 SOLVING DEC-POMDPS 99 2.4 SPECIAL CASES
AND GENERALIZATION 103 3 THE SUBJECTIVE APPROACH 105 3.1 INTERACTIVE
POMDPS 106 3.2 SOLVING I-POMDPS 108 3.3 THE COMPLEXITY OF SOLVING
I-POMDPS 109 4 APPLICATION OF DECISION-THEORETIC MODELS AND THE NEED TO
SCALE UP 109 4.1 AN EXAMPLE: ROBOCUP RESCUE AS A DEC-POMDP 109 4.2
AGGREGATION AND HIERARCHICAL DECOMPOSITIONS .... 113 4.3 MODELING AND
EXPLOITING INDEPENDENCE BETWEEN AGENTS 114 4.4 COMPRESSION OF THE
CONSIDERED POLICY SPACE 115 5 EFFICIENT HEURISTIC APPROACHES FOR TEAMS
OF AGENTS 116 5.1 ALLOCATING PRE-SPECIFIED ROLES SENSIBLY 116 5.2
FRONTIER SELECTION IN EXPLORATION 117 6 CONCLUSIONS 119 REFERENCES 120
EFFICIENT METHODS FOR NEAR-OPTIMAL SEQUENTIAL DECISION MAKING UNDER
UNCERTAINTY 125 CHRISTOS DIMITRAKAKIS 1 INTRODUCTION 125 2 X N CONTENTS
3.3 REINFORCEMENT LEARNING AND CONTROL 136 4 MARKOV DECISION PROCESSES
136 5 BELIEF-AUGMENTED MARKOV DECISION PROCESSES (BAMDPS) 137 5.1
BAYESIAN INFERENCE WITH A SINGLE MDP MODEL CLASS 138 5.2 CONSTRUCTING
AND SOLVING BAMDPS 139 5.3 BELIEF TREE EXPANSION 140 5.4 BOUNDS ON THE
OPTIMAL VALUE FUNCTION 141 5.5 DISCUSSION AND RELATED WORK 144 6 PARTIAL
OBSERVABILITY 146 6.1 BELIEF POMDPS 146 6.2 THE BELIEF STATE 147 6.3
BELIEF COMPRESSION 148 7 CONCLUSION, FUTURE DIRECTIONS AND OPEN PROBLEMS
149 REFERENCES 150 ANT COLONY LEARNING ALGORITHM FOR OPTIMAL CONTROL 155
JELMER MARINUS VAN AST, ROBERT BABUSKA, BART DE SCHUTTER 1 INTRODUCTION
155 2 ANT COLONY OPTIMIZATION 157 2.1 ACO FRAMEWORK 157 2.2 THE ANT
SYSTEM 158 2.3 THE ANT COLONY SYSTEM 160 3 ANT COLONY LEARNING 161 3.1
THE OPTIMAL CONTROL PROBLEM 161 3.2 GENERAL LAYOUT OF ACL 162 3.3 ACL
WITH CRISP STATE SPACE PARTITIONING 163 3.4 ACL WITH FUZZY STATE SPACE
PARTITIONING 165 3.5 PARAMETER SETTINGS 172 3.6 RELATION TO
REINFORCEMENT LEARNING 173 4 EXAMPLE: NAVIGATION WITH VARIABLE DAMPING
174 4.1 PROBLEM FORMULATION 174 4.2 STATE SPACE PARTITIONING AND
PARAMETERS 174 4.3 RESULTS 175 5 CONCLUSIONS AND FUTURE WORK 180
REFERENCES 181 MAP-BASE CONTENTS XIII 4 NETWORK BREAKDOWNS AND
COLLABORATION 189 4.1 DEALING WITH UNSTABLE NETWORKS 190 4.2
NETWORK-AWARE SUPPORT: BLOB INTERFACE 192 5 MAP ORIENTATION AND
COLLABORATION 195 6 LIMITATIONS AND VALIDITY 199 7 CONCLUSIONS 200
REFERENCES 200 PART III: COMPUTER-HUMAN INTERACTION MODELING AFFECTIVE
DIALOGUE MANAGEMENT USING FACTORED POMDPS ... 207 TRUNG H. BUI, JOB
ZWIERS, MANNES POEL, ANTON NIJHOLT 1 INTRODUCTION 207 2 COMPONENTS OF AN
AFFECTIVE DIALOGUE SYSTEM 209 3 THEORY OF POMDPS 211 3.1 BASIC FRAMEWORK
212 3.2 EMPATHIE DIALOGUE AGENT EXAMPLE 213 3.3 COMPUTING BELIEF STATES
215 3.4 FINDING AN OPTIMAL POLICY 216 4 REVIEW OF THE POMDP-BASED
DIALOGUE MANAGEMENT 218 5 THE FACTORED POMDP APPROACH 219 6 USER
SIMULATION 222 7 EXAMPLE: SINGLE-SLOT ROUTE NAVIGATION EXAMPLE 223 8
EVALUATION 226 8.1 PARAMETER TUNING 228 8.2 INFLUENCE OF STRESS TO THE
PERFORMANCE 229 8.3 COMPARISON WITH OTHER TECHNIQUES 230 8.4
TRACTABILITY 231 9 CONCLUSIONS 233 REFERENCES 233 CONTEXT-AWARE
MULTIMODAL HUMAN-COMPUTER INTERACTION 237 SISKA FITRIANIE, ZHENKE YANG,
DRAGO§ DATCU, ALIN G. CHTFU, LEON J.M. ROTHKRANTZ 1 INTRODUCTION 237 2
RELATED WORK 239 2.1 MULTIMODAL SYSTEMS 239 2.2 VISUAL LANGUAGES FOR
HUMAN OBSERVATION REPORTIN XIV CONTENTS 5 INPUT RECOGNITION 247 5.1
AUDIO-VISUAL SPEECH INPUT 247 5.2 CIRCULAR TEXT ENTRY 249 5.3 VISUAL
LANGUAGE-BASED MESSAGE 250 5.4 FACE DETECTION AND FACIAL EXPRESSION
RECOGNITION 252 6 ONTOLOGY-BASED COMPUTED CONTEXT AWARENESS 253 6.1
LANGUAGE UNDERSTANDING 254 6.2 SINGLE-USER MESSAGE INTERPRETATION 254
6.3 MULTI-USER MESSAGE INTEGRATION 256 7 INTERACTION MANAGER 259 8
INFORMATION GENERATION 261 9 EXPERIMENTS 265 10 CONCLUSIONS 267
REFERENCES 268 DESIGN ISSUES FOR PEN-CENTRIC INTERACTIVE MAPS 273 LOUIS
VUURPIJL, DON WILLEMS, RALPH NIELS, MARCEL VAN GERVEN 1 INTRODUCTION 274
1.1 MULTIMODAL INTERACTION 274 1.2 PEN-CENTRIC INTERACTIVE MAPS 275 1.3
A BRIEF PRIMER ON PATTERN RECOGNITION FOR PEN-CENTRIC SYSTEMS 277 1.4
DATA COLLECTIONS FOR DEVELOPING PEN-CENTRIC SYSTEMS 279 1.5 ORGANIZATION
OF THE REMAINDER OF THIS CHAPTER... 280 2 THE DESIGN OF THE NICLCON
DATABASE OF ICONIC PEN GESTURES 280 2.1 THE DATA COLLECTION SETUP 282
2.2 DATA SEGMENTATION AND STATISTICS 282 2.3 DATA SUBSETS FOR TRAINING,
TESTING, AND EVALUATION 283 3 DESIGN OF PATTERN RECOGNITION SYSTEMS FOR
ICONIC GESTURES 283 3.1 FEATURE EXTRACTION AND SELECTION 283 3.2
CLASSIFIER DESIGN AND LEARNING 288 3.3 MULTIPLE CLASSIFIER SYSTEM 289 4
RESULTS 289 5 CONTENTS XV 2.1 USER INTERACTION WITH USER-ADAPTIVE
SYSTEMS 301 2.2 USER ATTITUDES TOWARD SYSTEMS AUTONOMOUS BEHAVIOR 303
2.3 TRUST IN ADAPTIVE AND AUTONOMOUS SYSTEMS 304 2.4 TRANSPARENCY OF
ADAPTIVE SYSTEMS 307 2.5 THE ROLE OF TRUST IN ACCEPTANCE OF ADAPTIVE
SYSTEMS 309 2.6 CONCLUSIONS 310 3 RESULTS FROM STUDIES THAT INVESTIGATE
USER INTERACTION WITH INTELLIGENT SYSTEMS 311 3.1 TRUST IN INTERACTION
WITH ADAPTIVE SPAM FILTERS 311 3.2 THE EFFECTS OF TRANSPARENCY ON TRUST
IN AND ACCEPTANCE OF A CONTENT-BASED ART RECOMMENDER 314 3.3 EFFECTS OF
AUTONOMY, TRAFFIC CONDITIONS, AND DRIVER PERSONALITY TRAITS ON ATTITUDES
AND TRUST TOWARD IN-VEHICLE AGENTS 318 4 DISCUSSION AND CONCLUSION 320
REFERENCES 322 EXAMPLE-BASED HUMAN POSE RECOVERY UNDER PREDICTED PARTIAL
OCCLUSIONS 327 RONALD POPPE 1 INTRODUCTION 327 2 RELATED WORK ON HUMAN
MOTION ANALYSIS 329 2.1 HUMAN DETECTION 329 2.2 HUMAN POSE RECOVERY 330
2.3 DISCRIMINATIVE APPROACHES TO POSE RECOVERY 331 3 POSE RECOVERY USING
HOG 335 3.1 HISTOGRAM OF ORIENTED GRADIENTS 338 3.2 POSE RECOVERY USING
NEAREST NEIGHBOR INTERPOLATION 340 4 EXPERIMENT RESULTS 341 4.1 HUMANEVA
DATASET 341 4.2 EXAMPLE SETS 343 4.3 TEST SETS 343 4.4 RESULTS 344 4.5
DISCUSSION 345 5 XVI CONTENTS PART IV: ARCHITECTURES FOR DISTRIBUTED
AGENT-ACTOR COMMUNITIES AGILITY AND ADAPTIVE AUTONOMY IN NETWORKED
ORGANIZATIONS 357 MARTIJN NEEF, BOB VAN DER VECHT 1 INTRODUCTION 357 2
AUTONOMY AND AGILITY IN AGENT SYSTEMS 359 2.1 THE ROLE OF AUTONOMY IN
AGENT SYSTEMS 359 2.2 AUTONOMY AND COORDINATION MECHANISMS 360 2.3
ADAPTIVE AUTONOMY AND AGILE ORGANIZATIONS 361 3 A MODEL FOR ADAPTIVE
AUTONOMY 362 3.1 EVENT PROCESSING 362 3.2 BASIC ATTITUDES 363 3.3
META-KNOWLEDGE FOR INFLUENCE CONTROL 364 4 USING ADAPTIVE AUTONOMY FOR
COORDINATION 365 4.1 AGILE ORGANIZATIONS 366 4.2 EXAMPLE APPLICATION
SCENARIO 367 5 PRACTICAL APPLICATION IN NEC ENVIRONMENTS 369 6
DISCUSSION 370 7 CONCLUSIONS 371 REFERENCES 372 ADAPTIVE HIERARCHICAL
MULTI-AGENT ORGANIZATIONS 375 MATTIJS GHIJSEN, WOUTER N.H. JANSUIEIJER,
BOB J. WIELINGA 1 INTRODUCTION 375 2 HIERARCHICAL MAS ORGANIZATIONS 377
2.1 ORGANIZATION STRUCTURE 378 2.2 ORGANIZATION BEHAVIOR 379 2.3 DYNAMIC
HIERARCHIES 380 3 HIERARCHICAL SEARCH AND RESCUE ORGANIZATIONS 383 3.1
ROBOCUPRESCUE 384 3.2 ORGANIZATION DESIGN 384 4 EXPERIMENT 389 4.1
DISTRIBUTION OF WORKLOAD 390 4.2 LIMITED COMMUNICATION 393 4.3
HETEROGENEOUS WORKLOAD AND LIMITED COMMUNICATION 395 5 REFERENCES 450
CONTENTS XVII METHOD FOR DESIGNING NETWORKING ADAPTIVE INTERACTIVE
HYBRID SYSTEMS 401 LEON KESTER 1 INTRODUCTION 401 2 DESIGN PROCESS 403 3
PROBLEM STATEMENT 404 4 SYSTEM MODELING 405 4.1 HIGH-LEVEL MODEL 405 4.2
DECOMPOSITION STRATEGIES 406 4.3 TOPOLOGY OF THE FUNCTIONAL COMPONENTS
411 4.4 HYBRID COMPONENTS 412 5 INTEGRATION 413 5.1 INTEROPERABILITY 413
5.2 INTERACTION 413 6 LAUNCHING THE SYSTEM 417 7 PERFORMANCE ASSESSMENT
418 8 APPLICABILITY OF THE NAIHS DESIGN METHOD 419 9 CONCLUSIONS 419
REFERENCES 420 PART V: CASE STUDIES AND APPLICATIONS A CALL FOR
SENSEMAKING SUPPORT SYSTEMS IN CRISIS MANAGEMENT 425 WILLEM J. MUHREN,
BARTEL VAN DE WALLE 1 INTRODUCTION 425 2 SENSEMAKING 426 2.1 SENSEMAKING
CONSTRUCTS 427 3 INFORMATION PROCESSING CHALLENGES AND SUPPORT 429 3.1
SENSEMAKING VERSUS DECISION MAKING 430 3.2 INFORMATION SYSTEMS 431 4
CASE STUDIES 132 4.1 METHODOLOGY 433 4.2 CASE STUDY 1: BARENTS RESCUE
EXERCISE 434 4.3 CASE STUDY 2: FOREST FIRES IN PORTUGAL 437 4.4 CASE
STUDY 3: EUROPEAN UNION POLICE MISSION IN BOSNIA AND HERZEGOVINA 442 5
DESIGN OF CRISIS MANAGEMENT INFORMATION SYSTEMS 446 6 CONCLUSION 448
XVIII CONTENTS A DISTRIBUTED APPROACH TO GAS DETECTION AND SOURCE
LOCALIZATION USING HETEROGENEOUS INFORMATION 453 GREGOR PAVLIN, FRANS
GROEN, PATRICK DE OUDE, MICHIEL KAMERMANS 1 INTRODUCTION 454 2 CAUSAL
PROBABILISTIC MODELS 456 2.1 MODELING DYNAMIC PROCESSES 458 3 ESTIMATING
THE SOURCE 459 3.1 DEDICATED DOMAIN MODELS 460 3.2 MODELING TEMPORAL
ASPECTS 461 4 DISTRIBUTED MODELING AND INFERENCE 462 4.1 DISTRIBUTED
OBSERVATION MODELS 463 4.2 DYNAMIC GAS PROPAGATION MODELS 463 5
CONSTRUCTION AND MAINTENANCE OF ADEQUATE DOMAIN MODELS 465 5.1
DETERMINATION OF CAUSAL DEPENDENCIES 466 5.2 DETERMINATION OF ADEQUATE
MODELING PARAMETERS 466 5.3 DETERMINATION OF DOWNWIND AREAS 468 6 SYSTEM
DESIGN 468 6.1 DPN LAYER 469 6.2 INCORPORATION OF ARBITRARY INFORMATION
SOURCES . .. 470 6.3 AUTOMATED QUERYING 472 7 CONCLUSIONS AND FUTURE
WORK 472 REFERENCES 473 TRAFFIC LIGHT CONTROL BY MULTIAGENT
REINFORCEMENT LEARNING SYSTEMS 475 BRAM BAKKER, SHIMON WHITESON, LEON
KESTER, FRANS C.A. GROEN 1 INTRODUCTION 476 2 TRAFFIC MODEL 477 3
MULTIAGENT REINFORCEMENT LEARNING FOR URBAN TRAFFIC CONTROL 478 4
REPRESENTING AND HANDLING TRAFFIC CONGESTION 482 4.1 TC-SBC METHOD 482
4.2 TC-GAC METHOD 483 4.3 EXPERIMENTAL RESULTS 484 5 CONTENTS XIX 5.7
EXPERIMENTAL RESULTS: COMDP VS. POMDP ALGORITHMS 495 5.8 EXPERIMENTAL
RESULTS: LEARNING THE MODEL UNDER PARTIAL OBSERVABILITY 498 6 MULTIAGENT
COORDINATION OF TRAFFIC LIGHT CONTROLLERS 498 6.1 MAX-PLUS FOR URBAN
TRAFFIC CONTROL 500 6.2 EXPERIMENTAL RESULTS 500 6.3 DISCUSSION OF
MAX-PLUS RESULTS 505 7 CONCLUSIONS 507 REFERENCES 508 FUSING
HETEROGENEOUS AND UNRELIABLE DATA FROM TRAFFIC SENSORS 511 QING OU, HANS
VAN LINT, SERGE P. HOOGENDOORN 1 INTRODUCTION 512 1.1 CONTEXT AND
BACKGROUND: THE NEED FOR RELIABLE TRAFFIC DATA FUSION METHODS 512 1.2
MULTI-SENSOR DATA FUSION: A BRIEF OVERVIEW 513 1.3 CHAPTER OUTLINE 514 2
SPATIOTEMPORAL CHARACTERISTICS OF TRAFFIC DATA 514 2.1 BASIC MACROSCOPIC
TRAFFIC VARIABLES AND RELATIONSHIPS 514 2.2 DYNAMICS OF TRAFFIC:
SPATIOTEMPORAL PATTERNS 515 2.3 TRAVEL TIME AND TRAJECTORY DATA 517 2.4
SUMMARY AND CLASSIFICATION OF TRAFFIC DATA 518 3 TRAFFIC STATE
ESTIMATION AND DATA FUSION APPROACHES 519 3.1 RECURSIVE STATE ESTIMATION
APPROACHES (KALMAN FILTERS) 519 3.2 THE SPATIOTEMPORAL ALIGNMENT PROBLEM
520 4 NEW TRAFFIC DATA FUSION APPROACHES: METHODOLOGY 521 4.1 EXPLOITING
TRAVEL TIME CONSISTENCY: PIECE-WISE INVERSE SPEED CORRECTION BY USING
INDIVIDUAL TRAVEL TIME (PISCIT) 522 4.2 CONSERVATION LAW FOR FLOATING
CAR DATA: FUSING LO XX CONTENTS BAYESIAN NETWORKS FOR EXPERT SYSTEMS:
THEORY AND PRACTICAL APPLICATIONS 547 WIM WIEGERINCK, BERT KAPPEN,
WILLEM BURGERS 1 INTRODUCTION 547 2 BAYESIAN NETWORKS 550 2.1 BAYESIAN
NETWORK THEORY 550 2.2 BAYESIAN NETWORK MODELING 551 3 PROMEDAS: A
PROBABILISTIC MODEL FOR MEDICAL DIAGNOSTIC DECISION SUPPORT 552 3.1
BUILDING LARGE SCALE PROBABILISTIC MODELS 554 3.2 INFERENCE 558 3.3 THE
CURRENT APPLICATION 560 3.4 SUMMARY 561 4 A PETROPHYSICAL DECISION
SUPPORT SYSTEM 561 4.1 PROBABILISTIC MODELING 562 4.2 THE PRIOR AND THE
OBSERVATION MODEL 563 4.3 BAYESIAN INFERENCE 564 4.4 DECISION SUPPORT
566 4.5 THE APPLICATION 567 4.6 SUMMARY 567 5 BONAPARTE: A BAYESIAN
NETWORK FOR DISASTER VICTIM IDENTIFICATION 568 5.1 LIKELIHOOD RATIO OF
TWO HYPOTHESES 569 5.2 DNA PROFILES 570 5.3 A BAYESIAN NETWORK FOR
KINSHIP ANALYSIS 571 5.4 INFERENCE 574 5.5 THE APPLICATION 575 5.6
SUMMARY 575 6 DISCUSSION 576 REFERENCES 577 INDEX 579
|
| any_adam_object | 1 |
| author2 | Babuška, Robert |
| author2_role | edt |
| author2_variant | r b rb |
| author_facet | Babuška, Robert |
| building | Verbundindex |
| bvnumber | BV036475420 |
| classification_rvk | ST 300 |
| ctrlnum | (OCoLC)614479341 (DE-599)DNB999263498 |
| dewey-full | 004.019 006.33 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 004 - Computer science 006 - Special computer methods |
| dewey-raw | 004.019 006.33 |
| dewey-search | 004.019 006.33 |
| dewey-sort | 14.019 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Book |
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| id | DE-604.BV036475420 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:40:17Z |
| institution | BVB |
| isbn | 9783642116872 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-020347030 |
| oclc_num | 614479341 |
| open_access_boolean | |
| owner | DE-11 |
| owner_facet | DE-11 |
| physical | XXV, 585 S. Ill., graph. Darst. 24 cm |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | Springer |
| record_format | marc |
| series | Studies in computational intelligence |
| series2 | Studies in computational intelligence |
| spelling | Interactive collaborative information systems Robert Babuška and Frans C. A. Groen (eds.) Berlin ; Heidelberg Springer 2010 XXV, 585 S. Ill., graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Studies in computational intelligence 281 Literaturangaben Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd rswk-swf Mehragentensystem (DE-588)4389058-1 gnd rswk-swf Entscheidungsunterstützungssystem (DE-588)4191815-0 gnd rswk-swf Kollaboration Informatik (DE-588)4807425-1 gnd rswk-swf Mensch-Maschine-Kommunikation (DE-588)4125909-9 gnd rswk-swf Informationssystem (DE-588)4072806-7 gnd rswk-swf Informationssystem (DE-588)4072806-7 s Entscheidungsunterstützungssystem (DE-588)4191815-0 s Kollaboration Informatik (DE-588)4807425-1 s Mensch-Maschine-Kommunikation (DE-588)4125909-9 s Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 s Mehragentensystem (DE-588)4389058-1 s DE-604 Babuška, Robert edt Studies in computational intelligence 281 (DE-604)BV020822171 281 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020347030&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Interactive collaborative information systems Studies in computational intelligence Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd Mehragentensystem (DE-588)4389058-1 gnd Entscheidungsunterstützungssystem (DE-588)4191815-0 gnd Kollaboration Informatik (DE-588)4807425-1 gnd Mensch-Maschine-Kommunikation (DE-588)4125909-9 gnd Informationssystem (DE-588)4072806-7 gnd |
| subject_GND | (DE-588)4825546-4 (DE-588)4389058-1 (DE-588)4191815-0 (DE-588)4807425-1 (DE-588)4125909-9 (DE-588)4072806-7 |
| title | Interactive collaborative information systems |
| title_auth | Interactive collaborative information systems |
| title_exact_search | Interactive collaborative information systems |
| title_full | Interactive collaborative information systems Robert Babuška and Frans C. A. Groen (eds.) |
| title_fullStr | Interactive collaborative information systems Robert Babuška and Frans C. A. Groen (eds.) |
| title_full_unstemmed | Interactive collaborative information systems Robert Babuška and Frans C. A. Groen (eds.) |
| title_short | Interactive collaborative information systems |
| title_sort | interactive collaborative information systems |
| topic | Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd Mehragentensystem (DE-588)4389058-1 gnd Entscheidungsunterstützungssystem (DE-588)4191815-0 gnd Kollaboration Informatik (DE-588)4807425-1 gnd Mensch-Maschine-Kommunikation (DE-588)4125909-9 gnd Informationssystem (DE-588)4072806-7 gnd |
| topic_facet | Bestärkendes Lernen Künstliche Intelligenz Mehragentensystem Entscheidungsunterstützungssystem Kollaboration Informatik Mensch-Maschine-Kommunikation Informationssystem |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020347030&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV020822171 |
| work_keys_str_mv | AT babuskarobert interactivecollaborativeinformationsystems |