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Sequential optimality and coordination in multiagent systems
 In International Joint Conference on Artificial Intelligence
, 1999
"... Coordination of agent activities is a key problem in multiagent systems. Set in a larger decision theoretic context, the existence of coordination problems leads to difficulty in evaluating the utility of a situation. This in turn makes defining optimal policies for sequential decision processes pro ..."
Abstract

Cited by 166 (3 self)
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Coordination of agent activities is a key problem in multiagent systems. Set in a larger decision theoretic context, the existence of coordination problems leads to difficulty in evaluating the utility of a situation. This in turn makes defining optimal policies for sequential decision processes
Sequentially Optimal Mechanisms
 Review of Economic Studies
, 2006
"... We characterize the revenue maximizing mechanism in a twoperiod model. A risk neutral seller owns one unit of a durable good and faces a risk neutral buyer whose valuation is private information. The seller has all the bargaining power; she designs an institution to sell the object at t=0 but she c ..."
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Cited by 21 (2 self)
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period. Previous work has examined price dynamics when the seller behaves sequentially rationally. We provide a reason for the seller’s choice to post a price even though she can use infinitely many other possible institutions: posted price selling is the optimal strategy in the sense that it maximizes
SIS: A System for Sequential Circuit Synthesis
, 1992
"... SIS is an interactive tool for synthesis and optimization of sequential circuits. Given a state transition table, a signal transition graph, or a logiclevel description of a sequential circuit, it produces an optimized netlist in the target technology while preserving the sequential inputoutput b ..."
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Cited by 527 (44 self)
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SIS is an interactive tool for synthesis and optimization of sequential circuits. Given a state transition table, a signal transition graph, or a logiclevel description of a sequential circuit, it produces an optimized netlist in the target technology while preserving the sequential input
Sequentially Optimal Mechanisms
, 2005
"... This paper establishes that posting a price in each period is a revenue maximizing allocation mechanism in a …nite period model without commitment. A risk neutral seller has one object to sell and faces a risk neutral buyer whose valuation is private information and drawn from an arbitrary bounded s ..."
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subset of the real line. The seller has all the bargaining power; she designs a mechanism to sell the object at t, but if trade does not occur at t; she can propose another mechanism at t+1: We show that posting a price in each period is an optimal mechanism. A methodological contribution of the paper
Sequentially Optimal Mechanisms
, 2005
"... This paper characterizes the revenue maximizing allocation mechanism in a Tperiod model under noncommitment. A risk neutral seller has one object to sell and faces a risk neutral buyer whose valuation is private information and drawn from an arbitrary bounded subset of the real line. The seller ha ..."
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show that the optimal mechanism is to post a price in each period. A methodological contribution of the paper is to develop a procedure to characterize the optimal dynamic incentive schemes under noncommitment in asymmetric information environments that is valid irrespective of the structure
Sequentially Optimal Auctions
, 1997
"... In auctions where a seller can post a reserve price but if the object fails to sell cannot commit never to attempt to resell it, revenue equivalence between repeated first price and second price auctions without commitment results. When the time between auctions goes to zero, seller expected revenue ..."
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revenues converge to those of a static auction with no reserve price. With many bidders, the seller equilibrium reserve price approaches the reserve price in an optimal static auction. An auction in which the simple equilibrium reserve price policy of the seller mirrors a policy commonly used by many
Hidden Target search by sequential optimization
, 2004
"... We introduce the problem of searching a hidden target. Given a finite number of requests that map to a finite number of locations, we seek to locate a target hidden in one of the locations by using the information returned by the requests. A sequential optimization allows to determine a sequence of ..."
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We introduce the problem of searching a hidden target. Given a finite number of requests that map to a finite number of locations, we seek to locate a target hidden in one of the locations by using the information returned by the requests. A sequential optimization allows to determine a sequence
Sequential minimal optimization: A fast algorithm for training support vector machines
 Advances in Kernel MethodsSupport Vector Learning
, 1999
"... This paper proposes a new algorithm for training support vector machines: Sequential Minimal Optimization, or SMO. Training a support vector machine requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest possi ..."
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Cited by 461 (3 self)
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This paper proposes a new algorithm for training support vector machines: Sequential Minimal Optimization, or SMO. Training a support vector machine requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest
Results 1  10
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7,652