|
|
|
|
| LEADER |
02798nam a22005415i 4500 |
| 001 |
978-3-319-65479-9 |
| 003 |
DE-He213 |
| 005 |
20170920161922.0 |
| 007 |
cr nn 008mamaa |
| 008 |
170920s2017 gw | s |||| 0|eng d |
| 020 |
|
|
|a 9783319654799
|9 978-3-319-65479-9
|
| 024 |
7 |
|
|a 10.1007/978-3-319-65479-9
|2 doi
|
| 040 |
|
|
|d GrThAP
|
| 050 |
|
4 |
|a Q295
|
| 050 |
|
4 |
|a QA402.3-402.37
|
| 072 |
|
7 |
|a GPFC
|2 bicssc
|
| 072 |
|
7 |
|a SCI064000
|2 bisacsh
|
| 072 |
|
7 |
|a TEC004000
|2 bisacsh
|
| 082 |
0 |
4 |
|a 519
|2 23
|
| 100 |
1 |
|
|a Tatarenko, Tatiana.
|e author.
|
| 245 |
1 |
0 |
|a Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
|h [electronic resource] /
|c by Tatiana Tatarenko.
|
| 264 |
|
1 |
|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2017.
|
| 300 |
|
|
|a IX, 171 p. 38 illus.
|b online resource.
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 347 |
|
|
|a text file
|b PDF
|2 rda
|
| 520 |
|
|
|a This book presents new efficient methods for optimization in realistic large-scale, multi-agent systems. These methods do not require the agents to have the full information about the system, but instead allow them to make their local decisions based only on the local information, possibly obtained during scommunication with their local neighbors. The book, primarily aimed at researchers in optimization and control, considers three different information settings in multi-agent systems: oracle-based, communication-based, and payoff-based. For each of these information types, an efficient optimization algorithm is developed, which leads the system to an optimal state. The optimization problems are set without such restrictive assumptions as convexity of the objective functions, complicated communication topologies, closed-form expressions for costs and utilities, and finiteness of the system’s state space. .
|
| 650 |
|
0 |
|a Mathematics.
|
| 650 |
|
0 |
|a Computer science
|x Mathematics.
|
| 650 |
|
0 |
|a System theory.
|
| 650 |
|
0 |
|a Mathematical optimization.
|
| 650 |
|
0 |
|a Probabilities.
|
| 650 |
|
0 |
|a Statistics.
|
| 650 |
|
0 |
|a Game theory.
|
| 650 |
1 |
4 |
|a Mathematics.
|
| 650 |
2 |
4 |
|a Systems Theory, Control.
|
| 650 |
2 |
4 |
|a Game Theory.
|
| 650 |
2 |
4 |
|a Math Applications in Computer Science.
|
| 650 |
2 |
4 |
|a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
|
| 650 |
2 |
4 |
|a Optimization.
|
| 650 |
2 |
4 |
|a Probability Theory and Stochastic Processes.
|
| 710 |
2 |
|
|a SpringerLink (Online service)
|
| 773 |
0 |
|
|t Springer eBooks
|
| 776 |
0 |
8 |
|i Printed edition:
|z 9783319654782
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1007/978-3-319-65479-9
|z Full Text via HEAL-Link
|
| 912 |
|
|
|a ZDB-2-SMA
|
| 950 |
|
|
|a Mathematics and Statistics (Springer-11649)
|
