Sample Efficient Multiagent Learning in the Presence of Markovian Agents:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
2014
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| Schriftenreihe: | Studies in Computational Intelligence
523 |
| Schlagworte: | |
| Online-Zugang: | BTU01 FHA01 FHI01 FHN01 FHR01 FKE01 FRO01 FWS01 FWS02 UBY01 Volltext Inhaltsverzeichnis Abstract |
| Beschreibung: | The problem of Multiagent Learning (or MAL) is concerned with the study of how intelligent entities can learn and adapt in the presence of other such entities that are simultaneously adapting. The problem is often studied in the stylized settings provided by repeated matrix games (a.k.a. normal form games). The goal of this book is to develop MAL algorithms for such a setting that achieve a new set of objectives which have not been previously achieved. In particular this book deals with learning in the presence of a new class of agent behavior that has not been studied or modeled before in a MAL context: Markovian agent behavior. Several new challenges arise when interacting with this particular class of agents. The book takes a series of steps towards building completely autonomous learning algorithms that maximize utility while interacting with such agents. Each algorithm is meticulously specified with a thorough formal treatment that elucidates its key theoretical properties |
| Beschreibung: | 1 Online-Ressource (XVIII, 147 p.) 31 illus |
| ISBN: | 9783319026060 |
| DOI: | 10.1007/978-3-319-02606-0 |
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| 500 | |a The problem of Multiagent Learning (or MAL) is concerned with the study of how intelligent entities can learn and adapt in the presence of other such entities that are simultaneously adapting. The problem is often studied in the stylized settings provided by repeated matrix games (a.k.a. normal form games). The goal of this book is to develop MAL algorithms for such a setting that achieve a new set of objectives which have not been previously achieved. In particular this book deals with learning in the presence of a new class of agent behavior that has not been studied or modeled before in a MAL context: Markovian agent behavior. Several new challenges arise when interacting with this particular class of agents. The book takes a series of steps towards building completely autonomous learning algorithms that maximize utility while interacting with such agents. Each algorithm is meticulously specified with a thorough formal treatment that elucidates its key theoretical properties | ||
| 505 | 0 | |a Introduction -- Background -- Learn or Exploit in Adversary Induced Markov Decision Processes -- Convergence, Targeted Optimality and Safety in Multiagent Learning -- Maximizing -- Targeted Modeling of Markovian agents -- Structure Learning in Factored MDPs -- Related Work -- Conclusion and Future Work | |
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| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Computational Intelligence | |
| 650 | 4 | |a Artificial Intelligence (incl. Robotics) | |
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Datensatz im Suchindex
| DE-BY-FWS_katkey | 1016034 |
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| _version_ | 1806174845435641856 |
| adam_text | SAMPLE EFFICIENT MULTIAGENT LEARNING IN THE PRESENCE OF MARKOVIAN AGENTS
/ CHAKRABORTY, DORAN
: 2014
TABLE OF CONTENTS / INHALTSVERZEICHNIS
INTRODUCTION
BACKGROUND
LEARN OR EXPLOIT IN ADVERSARY INDUCED MARKOV DECISION PROCESSES
CONVERGENCE, TARGETED OPTIMALITY AND SAFETY IN MULTIAGENT LEARNING
MAXIMIZING
TARGETED MODELING OF MARKOVIAN AGENTS
STRUCTURE LEARNING IN FACTORED MDPS
RELATED WORK
CONCLUSION AND FUTURE WORK
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
SAMPLE EFFICIENT MULTIAGENT LEARNING IN THE PRESENCE OF MARKOVIAN AGENTS
/ CHAKRABORTY, DORAN
: 2014
ABSTRACT / INHALTSTEXT
THE PROBLEM OF MULTIAGENT LEARNING (OR MAL) IS CONCERNED WITH THE STUDY
OF HOW INTELLIGENT ENTITIES CAN LEARN AND ADAPT IN THE PRESENCE OF OTHER
SUCH ENTITIES THAT ARE SIMULTANEOUSLY ADAPTING. THE PROBLEM IS OFTEN
STUDIED IN THE STYLIZED SETTINGS PROVIDED BY REPEATED MATRIX GAMES
(A.K.A. NORMAL FORM GAMES). THE GOAL OF THIS BOOK IS TO DEVELOP MAL
ALGORITHMS FOR SUCH A SETTING THAT ACHIEVE A NEW SET OF OBJECTIVES WHICH
HAVE NOT BEEN PREVIOUSLY ACHIEVED. IN PARTICULAR THIS BOOK DEALS WITH
LEARNING IN THE PRESENCE OF A NEW CLASS OF AGENT BEHAVIOR THAT HAS NOT
BEEN STUDIED OR MODELED BEFORE IN A MAL CONTEXT: MARKOVIAN AGENT
BEHAVIOR. SEVERAL NEW CHALLENGES ARISE WHEN INTERACTING WITH THIS
PARTICULAR CLASS OF AGENTS. THE BOOK TAKES A SERIES OF STEPS TOWARDS
BUILDING COMPLETELY AUTONOMOUS LEARNING ALGORITHMS THAT MAXIMIZE UTILITY
WHILE INTERACTING WITH SUCH AGENTS. EACH ALGORITHM IS METICULOUSLY
SPECIFIED WITH A THOROUGH FORMAL TREATMENT THAT ELUCIDATES ITS KEY
THEORETICAL PROPERTIES
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
|
| any_adam_object | 1 |
| author | Chakraborty, Doran |
| author_facet | Chakraborty, Doran |
| author_role | aut |
| author_sort | Chakraborty, Doran |
| author_variant | d c dc |
| building | Verbundindex |
| bvnumber | BV041470995 |
| collection | ZDB-2-ENG |
| contents | Introduction -- Background -- Learn or Exploit in Adversary Induced Markov Decision Processes -- Convergence, Targeted Optimality and Safety in Multiagent Learning -- Maximizing -- Targeted Modeling of Markovian agents -- Structure Learning in Factored MDPs -- Related Work -- Conclusion and Future Work |
| ctrlnum | (OCoLC)874381652 (DE-599)BVBBV041470995 |
| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 |
| dewey-search | 006.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| doi_str_mv | 10.1007/978-3-319-02606-0 |
| format | Electronic eBook |
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| id | DE-604.BV041470995 |
| illustrated | Not Illustrated |
| indexdate | 2024-08-01T10:56:00Z |
| institution | BVB |
| isbn | 9783319026060 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-026917137 |
| oclc_num | 874381652 |
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| owner_facet | DE-Aug4 DE-92 DE-634 DE-859 DE-898 DE-BY-UBR DE-573 DE-861 DE-706 DE-863 DE-BY-FWS DE-862 DE-BY-FWS |
| physical | 1 Online-Ressource (XVIII, 147 p.) 31 illus |
| psigel | ZDB-2-ENG |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| record_format | marc |
| series | Studies in Computational Intelligence |
| series2 | Studies in Computational Intelligence |
| spellingShingle | Chakraborty, Doran Sample Efficient Multiagent Learning in the Presence of Markovian Agents Studies in Computational Intelligence Introduction -- Background -- Learn or Exploit in Adversary Induced Markov Decision Processes -- Convergence, Targeted Optimality and Safety in Multiagent Learning -- Maximizing -- Targeted Modeling of Markovian agents -- Structure Learning in Factored MDPs -- Related Work -- Conclusion and Future Work Engineering Artificial intelligence Computational Intelligence Artificial Intelligence (incl. Robotics) Ingenieurwissenschaften Künstliche Intelligenz |
| title | Sample Efficient Multiagent Learning in the Presence of Markovian Agents |
| title_auth | Sample Efficient Multiagent Learning in the Presence of Markovian Agents |
| title_exact_search | Sample Efficient Multiagent Learning in the Presence of Markovian Agents |
| title_full | Sample Efficient Multiagent Learning in the Presence of Markovian Agents by Doran Chakraborty |
| title_fullStr | Sample Efficient Multiagent Learning in the Presence of Markovian Agents by Doran Chakraborty |
| title_full_unstemmed | Sample Efficient Multiagent Learning in the Presence of Markovian Agents by Doran Chakraborty |
| title_short | Sample Efficient Multiagent Learning in the Presence of Markovian Agents |
| title_sort | sample efficient multiagent learning in the presence of markovian agents |
| topic | Engineering Artificial intelligence Computational Intelligence Artificial Intelligence (incl. Robotics) Ingenieurwissenschaften Künstliche Intelligenz |
| topic_facet | Engineering Artificial intelligence Computational Intelligence Artificial Intelligence (incl. Robotics) Ingenieurwissenschaften Künstliche Intelligenz |
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| volume_link | (DE-604)BV020822171 |
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