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54
An Efficient Boosting Algorithm for Combining Preferences
, 1999
"... The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new boosting ..."
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Cited by 515 (18 self)
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The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new boosting algorithm for combining preferences called RankBoost. We also describe an efficient implementation of the algorithm for certain natural cases. We discuss two experiments we carried out to assess the performance of RankBoost. In the first experiment, we used the algorithm to combine different WWW search strategies, each of which is a query expansion for a given domain. For this task, we compare the performance of RankBoost to the individual search strategies. The second experiment is a collaborativefiltering task for making movie recommendations. Here, we present results comparing RankBoost to nearestneighbor and regression algorithms.
Distributed Rational Decision Making
, 1999
"... Introduction Automated negotiation systems with selfinterested agents are becoming increasingly important. One reason for this is the technology push of a growing standardized communication infrastructureInternet, WWW, NII, EDI, KQML, FIPA, Concordia, Voyager, Odyssey, Telescript, Java, etco ..."
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Cited by 167 (0 self)
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Introduction Automated negotiation systems with selfinterested agents are becoming increasingly important. One reason for this is the technology push of a growing standardized communication infrastructureInternet, WWW, NII, EDI, KQML, FIPA, Concordia, Voyager, Odyssey, Telescript, Java, etcover which separately designed agents belonging to different organizations can interact in an open environment in realtime and safely carry out transactions. The second reason is strong application pull for computer support for negotiation at the operative decision making level. For example, we are witnessing the advent of small transaction electronic commerce on the Internet for purchasing goods, information, and communication bandwidth [29]. There is also an industrial trend toward virtual enterprises: dynamic alliances of small, agile enterprises which together can take advantage of economies of scale when available (e.g., respond to mor
Game Theory, Online Prediction and Boosting
 In Proceedings of the Ninth Annual Conference on Computational Learning Theory
, 1996
"... We study the close connections between game theory, online prediction and boosting. After a brief review of game theory, we describe an algorithm for learning to play repeated games based on the online prediction methods of Littlestone and Warmuth. The analysis of this algorithm yields a simple pr ..."
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Cited by 133 (13 self)
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We study the close connections between game theory, online prediction and boosting. After a brief review of game theory, we describe an algorithm for learning to play repeated games based on the online prediction methods of Littlestone and Warmuth. The analysis of this algorithm yields a simple proof of von Neumann's famous minmax theorem, as well as a provable method of approximately solving a game. We then show that the online prediction model is obtained by applying this gameplaying algorithm to an appropriate choice of game and that boosting is obtained by applying the same algorithm to the "dual" of this game. 1 INTRODUCTION The purpose of this paper is to bring out the close connections between game theory, online prediction and boosting. Briefly, game theory is the study of games and other interactions of various sorts. Online prediction is a learning model in which an agent predicts the classification of a sequence of items and attempts to minimize the total number of pre...
Complexity Results about Nash Equilibria
, 2002
"... Noncooperative game theory provides a normative framework for analyzing strategic interactions. ..."
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Cited by 130 (10 self)
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Noncooperative game theory provides a normative framework for analyzing strategic interactions.
Flexible Double Auctions for Electronic Commerce: Theory and Implementation
, 1998
"... We consider a general family of auction mechanisms that admit multiple buyers and sellers, and determine marketclearing prices. We analyze the economic incentives facing participants in such auctions, demonstrating that, under some conditions, it is possible to induce truthful revelation of val ..."
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Cited by 126 (20 self)
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We consider a general family of auction mechanisms that admit multiple buyers and sellers, and determine marketclearing prices. We analyze the economic incentives facing participants in such auctions, demonstrating that, under some conditions, it is possible to induce truthful revelation of values by buyers or sellers, but not both, and for single but not multiunit bids. We also perform a computational analysis of the auctioneer's task, exhibiting efficient algorithms for processing bids and calculating allocations.
Frugal path mechanisms
, 2002
"... We consider the problem of selecting a low cost s − t path in a graph, where the edge costs are a secret known only to the various economic agents who own them. To solve this problem, Nisan and Ronen applied the celebrated VickreyClarkeGroves (VCG) mechanism, which pays a premium to induce the edg ..."
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Cited by 115 (2 self)
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We consider the problem of selecting a low cost s − t path in a graph, where the edge costs are a secret known only to the various economic agents who own them. To solve this problem, Nisan and Ronen applied the celebrated VickreyClarkeGroves (VCG) mechanism, which pays a premium to induce the edges to reveal their costs truthfully. We observe that this premium can be unacceptably high. There are simple instances where the mechanism pays Θ(k) times the actual cost of the path, even if there is an alternate path available that costs only (1 + ɛ) times as much. This inspires the frugal path problem, which is to design a mechanism that selects a path and induces truthful cost revelation without paying such a high premium. This paper contributes negative results on the frugal path problem. On two large classes of graphs, including ones having three nodedisjoint s − t paths, we prove that no reasonable mechanism can always avoid paying a high premium to induce truthtelling. In particular, we introduce a general class of min function mechanisms, and show that all min function mechanisms can be forced to overpay just as badly as VCG. On the other hand, we prove that (on two large classes of graphs) every truthful mechanism satisfying some reasonable properties is a min function mechanism. 1
Negotiation Among Selfinterested Computationally Limited Agents
, 1996
"... A Dissertation Presented by TUOMAS W. SANDHOLM ..."
A framework for sequential planning in multiagent settings
 Journal of Artificial Intelligence Research
, 2005
"... This paper extends the framework of partially observable Markov decision processes (POMDPs) to multiagent settings by incorporating the notion of agent models into the state space. Agents maintain beliefs over physical states of the environment and over models of other agents, and they use Bayesian ..."
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Cited by 91 (27 self)
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This paper extends the framework of partially observable Markov decision processes (POMDPs) to multiagent settings by incorporating the notion of agent models into the state space. Agents maintain beliefs over physical states of the environment and over models of other agents, and they use Bayesian update to maintain their beliefs over time. The solutions map belief states to actions. Models of other agents may include their belief states and are related to agent types considered in games of incomplete information. We express the agents ’ autonomy by postulating that their models are not directly manipulable or observable by other agents. We show that important properties of POMDPs, such as convergence of value iteration, the rate of convergence, and piecewise linearity and convexity of the value functions carry over to our framework. Our approach complements a more traditional approach to interactive settings which uses Nash equilibria as a solution paradigm. We seek to avoid some of the drawbacks of equilibria which may be nonunique and are not able to capture offequilibrium behaviors. We do so at the cost of having to represent, process and continually revise models of other agents. Since the agent’s beliefs may be arbitrarily nested the optimal solutions to decision making problems are only asymptotically computable. However, approximate belief updates and approximately optimal plans are computable. We illustrate our framework using a simple application domain, and we show examples of belief updates and value functions. 1.
Rationality and SelfInterest in Peer to Peer Networks
 IN 2ND INT. WORKSHOP ON PEERTOPEER SYSTEMS (IPTPS’03)
, 2003
"... Much of the existing work in peer to peer networking assumes that users will follow prescribed protocols without deviation. This assumption ignores the user's ability to modify the behavior of an algorithm for selfinterested reasons. ..."
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Cited by 77 (7 self)
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Much of the existing work in peer to peer networking assumes that users will follow prescribed protocols without deviation. This assumption ignores the user's ability to modify the behavior of an algorithm for selfinterested reasons.
On the Economics of Anonymity
 Financial Cryptography. SpringerVerlag, LNCS 2742
, 2003
"... Decentralized anonymity infrastructures are still not in wide use today. While there are technical barriers to a secure robust design, our lack of understanding of the incentives to participate in such systems remains a major roadblock. Here we explore some reasons why anonymity systems are particul ..."
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Cited by 74 (22 self)
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Decentralized anonymity infrastructures are still not in wide use today. While there are technical barriers to a secure robust design, our lack of understanding of the incentives to participate in such systems remains a major roadblock. Here we explore some reasons why anonymity systems are particularly hard to deploy, enumerate the incentives to participate either as senders or also as nodes, and build a general model to describe the effects of these incentives. We then describe and justify some simplifying assumptions to make the model manageable, and compare optimal strategies for participants based on a variety of scenarios.