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UID:549@lincs.fr
DTSTART;TZID=Europe/Paris:20200625T140000
DTEND;TZID=Europe/Paris:20200625T160000
DTSTAMP:20200625T110926Z
URL:https://www.lincs.fr/events/models-and-solutions-of-strategic-resource
 -allocation-problems-approximate-equilibrium-and-online-learning-in-blotto
 -games/
SUMMARY:Models and Solutions of Strategic Resource Allocation Problems:
 Approximate Equilibrium and Online Learning in Blotto Games
DESCRIPTION:In this thesis\, we investigate resource allocation games\,
 broadly defined as resource allocation problems involving interactions
 between competitive decision makers. We primarily focus on the Colonel
 Blotto game (CB game). In the CB game\, two competitive players\, each
 having a fixed budget\, simultaneously distribute their resources toward n
 battlefields.  Each player evaluates each battlefield with a certain value.
 In each battlefield\, the player who has the higher allocation wins and
 gains the corresponding value. Each player's payoff is her aggregate gains
 from all the battlefields. \n\nFirst\, we model several variants of the CB
 game and their extensions as one-shot complete-information games and
 analyze players' strategic behaviors. Our first main contribution is a
 class of approximate equilibria in these games for which we prove that the
 approximation error can be well-controlled. Second\, we model resource
 allocation games with combinatorial structures as online learning problems
 to study situations involving sequential plays and incomplete information. 
 We make a connection between these games and online shortest path problems
 (OSP). Our second main contribution is a set of novel regret-minimization
 algorithms for OSP under several feedback settings that provide significant
 improvements in regret guarantees and running time in comparison with
 existing solutions.
CATEGORIES:PhD Defense
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DTSTART:20200329T030000
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