---
title: "More on the Power of Demand Queries in Combinatorial Auctions: Learning Atomic Languages and Handling Incentives"
authors:
  - "Sébastien Lahaie"
  - "Florin Constantin"
  - "David C. Parkes"
doi: ""
url: "https://ijcai.org/proceedings/2005/191"
pdf: "https://ijcai.org/proceedings/2005/0191.pdf"
venue: "Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence"
year: 2005
pages: "959-963"
track: ""
keywords:
---

# More on the Power of Demand Queries in Combinatorial Auctions: Learning Atomic Languages and Handling Incentives

Sébastien Lahaie, Florin Constantin, David C. Parkes

*Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence*, pages 959-963.

## Abstract

Query learning models from computational learning theory (CLT) can be adopted to perform elicitation in combinatorial auctions. Indeed, a recent elicitation framework demonstrated that the equivalence queries of CLT can be usefully simulated with price-based demand queries. In this paper, we validate the flexibility of this framework by defining a learning algorithm for atomic bidding languages, a class that includes XOR and OR. We also handle incentives, characterizing the communication requirements of the Vickrey-ClarkeGroves outcome rule. This motivates an extension to the earlier learning framework that brings truthful responses to queries into an equilibrium.