We present a solution to the winner determination problem which takes into account not only costs but also risk aversion of the agent that accepts the bids, and which works for auctioning tasks that have time and precedence constraints. We use Expected Utility Theory as the basic mechanism for decision-making. Our theoretical and experimental analysis shows that Expected Utility is useful for c...

In this paper we address the problem of (1) representing bids for combinatorial auctions and (2) employing those structures for “reasoning”. We propose a graph-based language who’s novelty lies (1) in the use of generalized network flows to represent the bids and (2) in the interpretation of winner determination as an adequate aggregation of individual preferences. We motivate the language both...

The original possible winner problem is: Given an unweighted election with partial preferences and a distinguished candidate c, can the preferences be extended to total ones such that c wins? We introduce a novel variant of this problem in which not some of the voters’ preferences are uncertain but some of their weights. Not much has been known previously about the weighted possible winner prob...

The original possible winner problem is: Given an unweighted election with partial preferences and a distinguished candidate c, can the preferences be extended to total ones such that c wins? We introduce a novel variant of this problem in which not some of the voters’ preferences are uncertain but some of their weights. Not much has been known previously about the weighted possible winner prob...

Combinatorial auctions, i.e. auctions where bidders can bid on combinations of items, tend to lead to more efficient allocations than traditional auctions in multi-item auctions where the agents' valuations of the items are not additive. However, determining the winners so as to maximize revenue is NP-complete. First, existing approaches for tackling this problem are reviewed: exhaustive enumer...

Computational social choice (comsoc) theory is currently an important area of research in computer science and more specifically in AI. The field started with the pioneering work of Bartholdi et al. in 1989 where they explored the the possibility of using computational intractability as a barrier against manipulation. Following that, a vast amount of research explored computational complexity o...

The migration of auctions to the Internet provides a unique opportunity to harness the power of computing to create new auction forms that were previously impossible. We describe a new type of combinatorial auction that accepts rule-based bids. Allowing bids in the form of high-level rules relieves the buyer from the burden of enumerating all possible acceptable bundles. The allocation of goods...

This paper presents the problem of multiple quadrature amplitude modulated signals equalization and argues the use of a radial basis functions neural network (RBF-NN) equalizer. Different competitive learning algorithms for the RBF-NN centres determination are discussed. A new competitive learning algorithm is introduced, the rival penalized competitive learning, which rewards the winner and pe...

We consider the problem of rational, self-interested, economic agents who must negotiate with each other in a market environment in order to carry out their plans. Customer agents express their plans in the form of task networks with temporal and precedence constraints. A combinatorial reverse auction allows supplier agents to submit bids specifying prices for combinations of tasks, along with ...

The Winner Determination Problem (WDP) in Combinatorial Auctions comes up in a wide range of applications. Linear Programming (LP) relaxations are a standard method for approximating combinatorial optimisation problems. In this paper we propose how to encode the WDP so that it can be approximated with AD. Moreover, we contribute with PAR-AD, the first parallel implementation of AD. We show that...