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Computational analysis of intelligent agents : = Social and strategic settings.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Computational analysis of intelligent agents :/
其他題名:	Social and strategic settings.
作者:	Sawant, Anshul.
面頁冊數:	1 online resource (225 pages)
附註:	Source: Dissertation Abstracts International, Volume: 77-12(E), Section: B.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9781339867359

Computational analysis of intelligent agents : = Social and strategic settings.
Sawant, Anshul.

Computational analysis of intelligent agents :Social and strategic settings. - 1 online resource (225 pages)

Source: Dissertation Abstracts International, Volume: 77-12(E), Section: B.

Thesis (Ph.D.)--University of Maryland, College Park, 2016.

Includes bibliographical references

The central motif of this work is prediction and optimization in presence of multiple interacting intelligent agents. We use the phrase 'intelligent agents' to imply in some sense, a 'bounded rationality', the exact meaning of which varies depending on the setting. Our agents may not be 'rational' in the classical game theoretic sense, in that they don't always optimize a global objective. Rather, they rely on heuristics, as is natural for human agents or even software agents operating in the real-world. Within this broad framework we study the problem of influence maximization in social networks where behavior of agents is myopic, but complication stems from the structure of interaction networks. In this setting, we generalize two well-known models and give new algorithms and hardness results for our models. Then we move on to models where the agents reason strategically but are faced with considerable uncertainty. For such games, we give a new solution concept and analyze a real-world game using out techniques. Finally, the richest model we consider is that of Network Cournot Competition which deals with strategic resource allocation in hypergraphs, where agents reason strategically and their interaction is specified indirectly via player's utility functions. For this model, we give the first equilibrium computability results. In all of the above problems, we assume that payoffs for the agents are known. However, for real-world games, getting the payoffs can be quite challenging. To this end, we also study the inverse problem of inferring payoffs, given game history. We propose and evaluate a data analytic framework and we show that it is fast and performant.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781339867359Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Computational analysis of intelligent agents : = Social and strategic settings.
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500 $a Source: Dissertation Abstracts International, Volume: 77-12(E), Section: B.
500 $a Advisers: MohammadTaghi Hajiaghayi; V. S. Subrahmanian.
502 $a Thesis (Ph.D.)--University of Maryland, College Park, 2016.
504 $a Includes bibliographical references
520 $a The central motif of this work is prediction and optimization in presence of multiple interacting intelligent agents. We use the phrase 'intelligent agents' to imply in some sense, a 'bounded rationality', the exact meaning of which varies depending on the setting. Our agents may not be 'rational' in the classical game theoretic sense, in that they don't always optimize a global objective. Rather, they rely on heuristics, as is natural for human agents or even software agents operating in the real-world. Within this broad framework we study the problem of influence maximization in social networks where behavior of agents is myopic, but complication stems from the structure of interaction networks. In this setting, we generalize two well-known models and give new algorithms and hardness results for our models. Then we move on to models where the agents reason strategically but are faced with considerable uncertainty. For such games, we give a new solution concept and analyze a real-world game using out techniques. Finally, the richest model we consider is that of Network Cournot Competition which deals with strategic resource allocation in hypergraphs, where agents reason strategically and their interaction is specified indirectly via player's utility functions. For this model, we give the first equilibrium computability results. In all of the above problems, we assume that payoffs for the agents are known. However, for real-world games, getting the payoffs can be quite challenging. To this end, we also study the inverse problem of inferring payoffs, given game history. We propose and evaluate a data analytic framework and we show that it is fast and performant.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
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710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Maryland, College Park. $b Computer Science. $3 1180862
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