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28
Applying Learning Algorithms to Preference Elicitation in Combinatorial Auctions
, 2004
"... We consider the parallels between the preference elicitation problem in combinatorial auctions and the problem of learning an unknown function from learning theory. We show that learning algorithms can be used as a basis for preference elicitation algorithms. The resulting elicitation algorithms ..."
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Cited by 55 (12 self)
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We consider the parallels between the preference elicitation problem in combinatorial auctions and the problem of learning an unknown function from learning theory. We show that learning algorithms can be used as a basis for preference elicitation algorithms. The resulting elicitation algorithms perform a polynomial number of queries. We also give conditions under which the resulting algorithms have polynomial communication. Our conversion procedure allows us to generate combinatorial auction protocols from learning algorithms for polynomials, monotone DNF, and linearthreshold functions. In particular, we obtain an algorithm that elicits XOR bids with polynomial communication. We then characterize the communication requirements of implementing Vickrey payments with an elicitation algorithm. This suggests a modification to the queries in our elicitation algorithms so that truthful bidding becomes an expost Nash equilibrium.
Auction Design with Costly Preference Elicitation
 Annals of Mathematics and Artificial Intelligence
, 2003
"... We consider auction design in a setting with costly preference elicitation. We motivate the role of proxy agents, that are situated between bidders and the auction, and maintain partial information about agent preferences and compute equilibrium bidding strategies based on the available information. ..."
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Cited by 52 (9 self)
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We consider auction design in a setting with costly preference elicitation. We motivate the role of proxy agents, that are situated between bidders and the auction, and maintain partial information about agent preferences and compute equilibrium bidding strategies based on the available information. The proxy agents can also elicit additional preference information incrementally during an auction. We show that indirect mechanisms, such as proxied ascendingprice auctions, can achieve better allocative efficiency with less preference elicitation than direct mechanisms, such as sealedbid auctions.
CABOB: A Fast Optimal Algorithm for Winner Determination in Combinatorial Auctions
, 2005
"... Combinatorial auctions where bidders can bid on bundles of items can lead to more economically efficient allocations, but determining the winners is NPcomplete and inapproximable. We present CABOB, a sophisticated optimal search algorithm for the problem. It uses decomposition techniques, upper and ..."
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Cited by 48 (8 self)
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Combinatorial auctions where bidders can bid on bundles of items can lead to more economically efficient allocations, but determining the winners is NPcomplete and inapproximable. We present CABOB, a sophisticated optimal search algorithm for the problem. It uses decomposition techniques, upper and lower bounding (also across components), elaborate and dynamically chosen bidordering heuristics, and a host of structural observations. CABOB attempts to capture structure in any instance without making assumptions about the instance distribution. Experiments against the fastest prior algorithm, CPLEX 8.0, show that CABOB is often faster, seldom drastically slower, and in many cases drastically faster—especially in cases with structure. CABOB’s search runs in linear space and has significantly better anytime performance than CPLEX. We also uncover interesting aspects of the problem itself. First, problems with short bids, which were hard for the first generation of specialized algorithms, are easy. Second, almost all of the CATS distributions are easy, and the run time is virtually unaffected by the number of goods. Third, we test several random restart strategies, showing that they do not help on this problem—the runtime distribution does not have a heavy tail.
Combinatorial auctions with kwise dependent valuations
 In Proc. 20th National Conference on Artificial Intelligence (AAAI05
, 2005
"... We analyze the computational and communication complexity of combinatorial auctions from a new perspective: the degree of interdependency between the items for sale in the bidders’ preferences. Denoting by Gk the class of valuations displaying up to kwise dependencies, we consider the hierarchy G1 ..."
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Cited by 26 (7 self)
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We analyze the computational and communication complexity of combinatorial auctions from a new perspective: the degree of interdependency between the items for sale in the bidders’ preferences. Denoting by Gk the class of valuations displaying up to kwise dependencies, we consider the hierarchy G1 ⊂ G2 ⊂ ·· · ⊂ Gm, where m is the number of items for sale. We show that the minimum nontrivial degree of interdependency (2wise dependency) is sufficient to render NPhard the problem of computing the optimal allocation (but we also exhibit a restricted class of such valuations for which computing the optimal allocation is easy). On the other hand, bidders ’ preferences can be communicated efficiently (i.e., exchanging a polynomial amount of information) as long as the interdependencies between items are limited to sets of cardinality up to k, where k is an arbitrary constant. The amount of communication required to transmit the bidders ’ preferences becomes superpolynomial (under the assumption that only value queries are allowed) when interdependencies occur between sets of cardinality g(m), where g(m) is an arbitrary function such that g(m) →∞ as m → ∞. We also consider approximate elicitation, in which the auctioneer learns, asking polynomially many value queries, an approximation of the bidders ’ actual preferences.
Eliciting singlepeaked preferences using comparison queries
 In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems
, 2007
"... Voting is a general method for aggregating the preferences of multiple agents. Each agent ranks all the possible alternatives, and based on this, an aggregate ranking of the alternatives (or at least a winning alternative) is produced. However, when there are many alternatives, it is impractical to ..."
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Cited by 26 (4 self)
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Voting is a general method for aggregating the preferences of multiple agents. Each agent ranks all the possible alternatives, and based on this, an aggregate ranking of the alternatives (or at least a winning alternative) is produced. However, when there are many alternatives, it is impractical to simply ask agents to report their complete preferences. Rather, the agents’ preferences, or at least the relevant parts thereof, need to be elicited. This is done by asking the agents a (hopefully small) number of simple queries about their preferences, such as comparison queries, which ask an agent to compare two of the alternatives. Prior work on preference elicitation in voting has focused on the case of unrestricted preferences. It has been shown that in this setting, it is sometimes necessary to ask each agent (almost) as many queries as would be required to determine an arbitrary ranking of the alternatives. In contrast, in this paper, we focus on singlepeaked preferences. We show that such preferences can be elicited using only a linear number of comparison queries, if either the order with respect to which preferences are singlepeaked is known, or at least one other agent’s complete preferences are known. We show that using a sublinear number of queries does not suffice. We also consider the case of cardinally singlepeaked preferences. For this case, we show that if the alternatives ’ cardinal positions are known, then an agent’s preferences can be elicited using only a logarithmic number of queries; however, we also show that if the cardinal positions are not known, then a sublinear number of queries does not suffice. We present experimental results for all elicitation algorithms. We also consider the problem of only eliciting enough information to determine the aggregate ranking, and show that even for this more modest objective, a sublinear number of queries per agent does not suffice for known ordinal or unknown cardinal positions. Finally, we discuss whether and how these techniques can be applied when preferences are almost singlepeaked. 1 1
Automated Mechanism Design: A New Application Area for Search Algorithms
 In Proceedings of the International Conference on Principles and Practice of Constraint Programming (CP 03), Kinsale, County
, 2003
"... Mechanism design is the art of designing the rules of the game (aka. mechanism) so that a desirable outcome (according to a given objective) is reached despite the fact that each agent acts in his own selfinterest. ..."
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Cited by 24 (2 self)
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Mechanism design is the art of designing the rules of the game (aka. mechanism) so that a desirable outcome (according to a given objective) is reached despite the fact that each agent acts in his own selfinterest.
Querying the semantic web with preferences
 ISWC
, 2006
"... Abstract. Ranking is an important concept to avoid empty or overfull and unordered result sets. However, such scoring can only express total orders, which restricts its usefulness when several factors influence result relevance. A more flexible way to express relevance is the notion of preferences. ..."
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Cited by 21 (3 self)
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Abstract. Ranking is an important concept to avoid empty or overfull and unordered result sets. However, such scoring can only express total orders, which restricts its usefulness when several factors influence result relevance. A more flexible way to express relevance is the notion of preferences. Users state which kind of answers they ‘prefer ’ by adding soft constraints to their queries. Current approaches in the Semantic Web offer only limited facilities for specification of scoring and result ordering. There is no common language element to express and formalize ranking and preferences. We present a comprehensive extension of SPARQL which directly supports the expression of preferences. This includes formal syntax and semantics of preference expressions for SPARQL. Additionally, we report our implementation of preference query processing, which is based on the ARQ query engine.
On the Computational Power of Iterative Auctions I: Demand Queries
 In Proceedings of the 6th ACM Conference on Electronic Commerce (EC
, 2005
"... ..."
Making markets and democracy work: A story of incentives and computing
 In Proceedings of the International Joint Conference on Artificial Intelligence
, 2003
"... Collective choice settings are the heart of society. Game theory provides a basis for engineering the incentives into the interaction mechanism (e.g., rules of an election or auction) so that a desirable systemwide outcome (e.g., president, resource allocation, or task allocation) is chosen even th ..."
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Cited by 16 (0 self)
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Collective choice settings are the heart of society. Game theory provides a basis for engineering the incentives into the interaction mechanism (e.g., rules of an election or auction) so that a desirable systemwide outcome (e.g., president, resource allocation, or task allocation) is chosen even though every agent acts based on selfinterest. However, there are a host of computer science issues not traditionally addressed in game theory that have to be addressed in order to make mechanisms work in the real world. Those computing, communication, and privacy issues are deeply intertwined with the economic incentive issues. For example, the fact that agents have limited computational capabilities to determine their own (and others') preferences ruins the incentive properties of established auction mechanisms, and gives rise to new issues. On the positive side, computational complexity can be used as a barrier to strategic behavior in settings where economic mechanism design falls short. Novel computational approaches also enable new economic institutions. For example, market clearing technology with specialized search algorithms is enabling a form of interaction that I call expressive competition. As another example, selective incremental preference elicitation can determine the optimal outcome while requiring the agents to determine and reveal only a small portion of their preferences. Furthermore, automated mechanism design can yield better mechanisms than the best known to date.
Automated Design of Multistage Mechanisms
 IN IJCAI
, 2007
"... Mechanism design is the study of preference aggregation protocols that work well in the face of selfinterested agents. We present the first generalpurpose techniques for automatically designing multistage mechanisms. These can reduce elicitation burden by only querying agents for information that ..."
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Cited by 16 (4 self)
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Mechanism design is the study of preference aggregation protocols that work well in the face of selfinterested agents. We present the first generalpurpose techniques for automatically designing multistage mechanisms. These can reduce elicitation burden by only querying agents for information that is relevant given their answers to previous queries. We first show how to turn a given (e.g., automatically designed using constrained optimization techniques) singlestage mechanism into the most efficient corresponding multistage mechanism given a specified elicitation tree. We then present greedy and dynamic programming (DP) algorithms that will determine the elicitation tree (optimal in the DP case). Next, we show how the query savings inherent in the multistage model can be used to design the underlying singlestage mechanism to maximally take advantage of this approach. We illustrate all of these techniques on an optimal auction example. Finally, we present negative results on the design of multistage mechanisms that do not correspond to dominantstrategy singlestage mechanisms: an optimal multistage mechanism in general has to randomize over queries to hide information from the agents.