Co-learning and the evolution of social activity:
Abstract: "We introduce the notion of co-learning, which refers to a process in which several agents simultaneously try to adapt to one another's behavior so as to produce desirable globabl system properties. Of particular interest are two specific co-learning settings, which relate to the...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Stanford, Calif.
1994
|
| Schriftenreihe: | Stanford University / Computer Science Department: Report STAN-CS
1511 |
| Schlagworte: | |
| Zusammenfassung: | Abstract: "We introduce the notion of co-learning, which refers to a process in which several agents simultaneously try to adapt to one another's behavior so as to produce desirable globabl system properties. Of particular interest are two specific co-learning settings, which relate to the emergence of conventions and the evolution of cooperation in societies, respectively. We define a basic co-learning rule, called Highest Cumulative Reward (HCR), and show that it gives rise to quite non- trivial system dynamics. In general, we are interested in the eventual convergence of the co-learning system to desirable states, as well as in the efficiency with which this convergence is attained. Our results on eventual convergence are analytic; the results on efficiency properties include analytic lower bounds as well as empirical upper bounds derived from rigorous computer simulations." |
| Beschreibung: | 36 S. graph. Darst. |
Internformat
MARC
| LEADER | 00000nam a2200000 cb4500 | ||
|---|---|---|---|
| 001 | BV009776750 | ||
| 003 | DE-604 | ||
| 005 | 00000000000000.0 | ||
| 007 | t | ||
| 008 | 940826s1994 d||| |||| 00||| eng d | ||
| 035 | |a (OCoLC)31972778 | ||
| 035 | |a (DE-599)BVBBV009776750 | ||
| 040 | |a DE-604 |b ger |e rakddb | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-29T | ||
| 100 | 1 | |a Shoham, Yoav |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Co-learning and the evolution of social activity |c by Yoav Shoham and Moshe Tennenholtz |
| 264 | 1 | |a Stanford, Calif. |c 1994 | |
| 300 | |a 36 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Stanford University / Computer Science Department: Report STAN-CS |v 1511 | |
| 520 | 3 | |a Abstract: "We introduce the notion of co-learning, which refers to a process in which several agents simultaneously try to adapt to one another's behavior so as to produce desirable globabl system properties. Of particular interest are two specific co-learning settings, which relate to the emergence of conventions and the evolution of cooperation in societies, respectively. We define a basic co-learning rule, called Highest Cumulative Reward (HCR), and show that it gives rise to quite non- trivial system dynamics. In general, we are interested in the eventual convergence of the co-learning system to desirable states, as well as in the efficiency with which this convergence is attained. Our results on eventual convergence are analytic; the results on efficiency properties include analytic lower bounds as well as empirical upper bounds derived from rigorous computer simulations." | |
| 650 | 4 | |a Künstliche Intelligenz | |
| 650 | 4 | |a Artificial intelligence | |
| 700 | 1 | |a Tennenholtz, Moshe |e Verfasser |4 aut | |
| 810 | 2 | |a Computer Science Department: Report STAN-CS |t Stanford University |v 1511 |w (DE-604)BV008928280 |9 1511 | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-006468870 | ||
Datensatz im Suchindex
| _version_ | 1804124125622435840 |
|---|---|
| any_adam_object | |
| author | Shoham, Yoav Tennenholtz, Moshe |
| author_facet | Shoham, Yoav Tennenholtz, Moshe |
| author_role | aut aut |
| author_sort | Shoham, Yoav |
| author_variant | y s ys m t mt |
| building | Verbundindex |
| bvnumber | BV009776750 |
| ctrlnum | (OCoLC)31972778 (DE-599)BVBBV009776750 |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01923nam a2200313 cb4500</leader><controlfield tag="001">BV009776750</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">940826s1994 d||| |||| 00||| eng d</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)31972778</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV009776750</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rakddb</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-29T</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Shoham, Yoav</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Co-learning and the evolution of social activity</subfield><subfield code="c">by Yoav Shoham and Moshe Tennenholtz</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Stanford, Calif.</subfield><subfield code="c">1994</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">36 S.</subfield><subfield code="b">graph. Darst.</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="1" ind2=" "><subfield code="a">Stanford University / Computer Science Department: Report STAN-CS</subfield><subfield code="v">1511</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">Abstract: "We introduce the notion of co-learning, which refers to a process in which several agents simultaneously try to adapt to one another's behavior so as to produce desirable globabl system properties. Of particular interest are two specific co-learning settings, which relate to the emergence of conventions and the evolution of cooperation in societies, respectively. We define a basic co-learning rule, called Highest Cumulative Reward (HCR), and show that it gives rise to quite non- trivial system dynamics. In general, we are interested in the eventual convergence of the co-learning system to desirable states, as well as in the efficiency with which this convergence is attained. Our results on eventual convergence are analytic; the results on efficiency properties include analytic lower bounds as well as empirical upper bounds derived from rigorous computer simulations."</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Künstliche Intelligenz</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial intelligence</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tennenholtz, Moshe</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="810" ind1="2" ind2=" "><subfield code="a">Computer Science Department: Report STAN-CS</subfield><subfield code="t">Stanford University</subfield><subfield code="v">1511</subfield><subfield code="w">(DE-604)BV008928280</subfield><subfield code="9">1511</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-006468870</subfield></datafield></record></collection> |
| id | DE-604.BV009776750 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T17:40:42Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-006468870 |
| oclc_num | 31972778 |
| open_access_boolean | |
| owner | DE-29T |
| owner_facet | DE-29T |
| physical | 36 S. graph. Darst. |
| publishDate | 1994 |
| publishDateSearch | 1994 |
| publishDateSort | 1994 |
| record_format | marc |
| series2 | Stanford University / Computer Science Department: Report STAN-CS |
| spelling | Shoham, Yoav Verfasser aut Co-learning and the evolution of social activity by Yoav Shoham and Moshe Tennenholtz Stanford, Calif. 1994 36 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Stanford University / Computer Science Department: Report STAN-CS 1511 Abstract: "We introduce the notion of co-learning, which refers to a process in which several agents simultaneously try to adapt to one another's behavior so as to produce desirable globabl system properties. Of particular interest are two specific co-learning settings, which relate to the emergence of conventions and the evolution of cooperation in societies, respectively. We define a basic co-learning rule, called Highest Cumulative Reward (HCR), and show that it gives rise to quite non- trivial system dynamics. In general, we are interested in the eventual convergence of the co-learning system to desirable states, as well as in the efficiency with which this convergence is attained. Our results on eventual convergence are analytic; the results on efficiency properties include analytic lower bounds as well as empirical upper bounds derived from rigorous computer simulations." Künstliche Intelligenz Artificial intelligence Tennenholtz, Moshe Verfasser aut Computer Science Department: Report STAN-CS Stanford University 1511 (DE-604)BV008928280 1511 |
| spellingShingle | Shoham, Yoav Tennenholtz, Moshe Co-learning and the evolution of social activity Künstliche Intelligenz Artificial intelligence |
| title | Co-learning and the evolution of social activity |
| title_auth | Co-learning and the evolution of social activity |
| title_exact_search | Co-learning and the evolution of social activity |
| title_full | Co-learning and the evolution of social activity by Yoav Shoham and Moshe Tennenholtz |
| title_fullStr | Co-learning and the evolution of social activity by Yoav Shoham and Moshe Tennenholtz |
| title_full_unstemmed | Co-learning and the evolution of social activity by Yoav Shoham and Moshe Tennenholtz |
| title_short | Co-learning and the evolution of social activity |
| title_sort | co learning and the evolution of social activity |
| topic | Künstliche Intelligenz Artificial intelligence |
| topic_facet | Künstliche Intelligenz Artificial intelligence |
| volume_link | (DE-604)BV008928280 |
| work_keys_str_mv | AT shohamyoav colearningandtheevolutionofsocialactivity AT tennenholtzmoshe colearningandtheevolutionofsocialactivity |