Learning automata and their applications to intelligent systems / JunQi Zhang, MengChu Zhou.
"A learning automaton represents an important and powerful tool in the area of reinforcement learning and aims at learning the optimal one that maximizes the probability of being rewarded out of a set of allowable systems, actions, alternatives, candidates, or designs by the interaction with a...
Saved in:
| Online Access: |
Full Text (via Wiley) |
|---|---|
| Main Authors: | , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hoboken, New Jersey :
John Wiley & Sons, Inc.,
[2024]
|
| Subjects: |
MARC
| LEADER | 00000cam a2200000 i 4500 | ||
|---|---|---|---|
| 001 | in00000116891 | ||
| 006 | m o d | ||
| 007 | cr ||||||||||| | ||
| 008 | 230907s2024 njua ob 001 0 eng | ||
| 005 | 20240102184823.9 | ||
| 010 | |a 2023040654 | ||
| 020 | |a 9781394188536 |q electronic book | ||
| 020 | |a 1394188536 |q electronic book | ||
| 020 | |a 9781394188529 |q electronic book | ||
| 020 | |a 1394188528 |q electronic book | ||
| 020 | |a 9781394188505 |q electronic book | ||
| 020 | |a 1394188501 |q electronic book | ||
| 020 | |z 9781394188499 |q hardcover | ||
| 024 | 7 | |a 10.1002/9781394188536 |2 doi | |
| 035 | |a (OCoLC)wol1405190907 | ||
| 035 | |a (OCoLC)1405190907 | ||
| 040 | |a DLC |b eng |e rda |c DLC |d OCLCF |d YDX |d OCLCO |d IEEEE | ||
| 042 | |a pcc | ||
| 049 | |a GWRE | ||
| 050 | 0 | 4 | |a QA267 |b .Z43 2024 |
| 100 | 1 | |a Zhang, JunQi |c (Professor), |e author. |0 http://id.loc.gov/authorities/names/no2023099798 | |
| 245 | 1 | 0 | |a Learning automata and their applications to intelligent systems / |c JunQi Zhang, MengChu Zhou. |
| 264 | 1 | |a Hoboken, New Jersey : |b John Wiley & Sons, Inc., |c [2024] | |
| 300 | |a 1 online resource (xvii, 251 pages) : |b illustrations (chiefly color) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a "A learning automaton represents an important and powerful tool in the area of reinforcement learning and aims at learning the optimal one that maximizes the probability of being rewarded out of a set of allowable systems, actions, alternatives, candidates, or designs by the interaction with a random environment. During a cycle, an automaton chooses an action and then receives a stochastic response that can be either a reward or penalty from the environment. The action probability vector of choosing the next action is then updated by employing this response. The ability of learning how to choose the optimal action endows learning automata with high adaptability to the environment, thus saving great expense and time to find the optimal one in various difficult stochastic environments."-- |c Provided by publisher. | ||
| 588 | |a Description based on online resource; title from digital title page (viewed on November 22, 2023). | ||
| 505 | 0 | |a About the Authors ix -- Preface xi -- Acknowledgments xiii -- A Guide to Reading this Book xv -- Organization of the Book xvii -- 1 Introduction 1 -- 1.1 Ranking and Selection in Noisy Optimization 2 -- 1.2 Learning Automata and Ordinal Optimization 5 -- 1.3 Exercises 7 -- 2 Learning Automata 9 -- 2.1 Environment and Automaton 9 -- 2.1.1 Environment 9 -- 2.1.2 Automaton 10 -- 2.1.3 Deterministic and Stochastic Automata 11 -- 2.1.4 Measured Norms 15 -- 2.2 Fixed Structure Learning Automata 16 -- 2.2.1 Tsetlin Learning Automaton 16 -- 2.2.2 Krinsky Learning Automaton 18 -- 2.2.3 Krylov Learning Automaton 19 -- 2.2.4 IJA Learning Automaton 20 -- 2.3 Variable Structure Learning Automata 21 -- 2.3.1 Estimator-Free Learning Automaton 22 -- 2.3.2 Deterministic Estimator Learning Automaton 24 -- 2.3.3 Stochastic Estimator Learning Automaton 26 -- 2.4 Summary 27 -- 2.5 Exercises 28 -- 3 Fast Learning Automata 31 -- 3.1 Last-position Elimination-based Learning Automata 31 -- 3.1.1 Background and Motivation 32 -- 3.1.2 Principles and Algorithm Design 35 -- 3.1.3 Difference Analysis 37 -- 3.1.4 Simulation Studies 40 -- 3.1.5 Summary 45 -- 3.2 Fast Discretized Pursuit Learning Automata 46 -- 3.2.1 Background and Motivation 46 -- 3.2.2 Algorithm Design of Fast Discretized Pursuit LAs 48 -- 3.2.3 Optimality Analysis 54 -- 3.2.4 Simulation Studies 59 -- 3.2.5 Summary 63 -- 3.3 Exercises 63 -- 4 Application-Oriented Learning Automata 67 -- 4.1 Discovering and Tracking Spatiotemporal Event Patterns 67 -- 4.1.1 Background and Motivation 69 -- 4.1.2 Spatiotemporal Pattern Learning Automata 70 -- 4.1.3 Adaptive Tunable Spatiotemporal Pattern Learning Automata 73 -- 4.1.4 Optimality Analysis 76 -- 4.1.5 Simulation Studies 83 -- 4.1.6 Summary 89 -- 4.2 Stochastic Searching on the Line 89 -- 4.2.1 Background and Motivation 89 -- 4.2.2 Symmetrical Hierarchical Stochastic Searching on the Line 95 -- 4.2.3 Simulation Studies 99 -- 4.2.4 Summary 104 -- 4.3 Fast Adaptive Search on the Line in Dual Environments 104 -- 4.3.1 Background and Motivation 109 -- 4.3.2 Symmetrized ASS with Buffer 111 -- 4.3.3 Simulation Studies 114 -- 4.3.4 Summary 118 -- 4.4 Exercises 118 -- 5 Ordinal Optimization 123 -- 5.1 Optimal Computing-Budget Allocation 123 -- 5.2 Optimal Computing-Budget Allocation for Selection of Best and Worst Designs 125 -- 5.2.1 Background and Motivation 125 -- 5.2.2 Approximate Optimal Simulation Budget Allocation 126 -- 5.2.3 Simulation Studies 138 -- 5.2.4 Summary 150 -- 5.3 Optimal Computing-Budget Allocation for Subset Ranking 151 -- 5.3.1 Background and Motivation 151 -- 5.3.2 Approximate Optimal Simulation Budget Allocation 153 -- 5.3.3 Simulation Studies 159 -- 5.3.4 Summary 167 -- 5.4 Exercises 167 -- 6 Incorporation of Ordinal Optimization into Learning Automata 175 -- 6.1 Background and Motivation 175 -- 6.2 Learning Automata with Optimal Computing Budget Allocation 178 -- 6.3 Proof of Optimality 182 -- 6.4 Simulation Studies 187 -- 6.5 Summary 193 -- 6.6 Exercises 193 -- 7 Noisy Optimization Applications 199 -- 7.1 Background and Motivation 200 -- 7.2 Particle Swarm Optimization 202 -- 7.2.1 Parameters Configurations 203 -- 7.2.2 Topology Structures 203 -- 7.2.3 Hybrid PSO 203 -- 7.2.4 Multiswarm Techniques 204 -- 7.3 Resampling for Noisy Optimization Problems 204 -- 7.4 PSO-Based LA and OCBA 205 -- 7.5 Simulations Studies 209 -- 7.6 Summary 223 -- 7.7 Exercises 224 -- 8 Applications and Future Research Directions of Learning Automata 231 -- 8.1 Summary of Existing Applications 231 -- 8.1.1 Classification 231 -- 8.1.2 Clustering 233 -- 8.1.3 Games 233 -- 8.1.4 Knapsack Problems 234 -- 8.1.5 Decision Problems in Networks 235 -- 8.1.6 Optimization 236 -- 8.1.7 LA Parallelization and Design Ranking 238 -- 8.1.8 Scheduling 240 -- 8.2 Future Research Directions 241 -- 8.3 Exercises 243 -- References 243 -- Index 249. | |
| 650 | 0 | |a Machine theory. |0 http://id.loc.gov/authorities/subjects/sh85079341 | |
| 650 | 7 | |a Machine theory. |2 fast | |
| 700 | 1 | |a Zhou, MengChu, |e author. |0 http://id.loc.gov/authorities/names/n92108716 |1 http://isni.org/isni/0000000114391426 | |
| 776 | 0 | 8 | |i Print version: |a Zhang, JunQi |t Learning automata and their applications to intelligent systems |d Hoboken, New Jersey : Wiley, [2024] |z 9781394188499 |w (DLC) 2023040653 |
| 856 | 4 | 0 | |u https://colorado.idm.oclc.org/login?url=https://onlinelibrary.wiley.com/doi/book/10.1002/9781394188536 |z Full Text (via Wiley) |
| 915 | |a - | ||
| 944 | |a MARS - RDA ENRICHED | ||
| 956 | |a Wiley Online Library eBooks | ||
| 956 | |b Wiley Online Library: Complete oBooks | ||
| 994 | |a 92 |b COD | ||
| 998 | |b Added to collection wiley.ebooks | ||
| 999 | f | f | |s 3c41a369-6ada-48d0-8891-469c23bc1991 |i 3ed16417-c82b-4d41-bf79-791877956456 |
| 952 | f | f | |p Can circulate |a University of Colorado Boulder |b Online |c Online |d Online |e QA267 .Z43 2024 |h Library of Congress classification |i web |