Representing Eliciting and Reasoning with Preferences: Nov. 11 and Nov. 18, 2009

In the LARGE meeting, Yixin presented the tutorial paper about preference handeling by Brafman&Domshlak.

In the first session (Nov. 11, 2009), we focused on models and languages for representing preferences. Starting with the quantitative languages, we discussed about two important classes of value functions: Additively Independent (AI) and Generalized Additively Independent (GAI) functions, as well as some nice properties of the corresponding representation structure. Despite of the computational efficiency for preference comparison or ordering, those quantitative languages put too much cognitive burden on the users' slide and they are usually difficult for users to reflect upon. Hence we need somewhat more "easy'' languages which, hopefully, carry with them desirable properties from both cognition and computation aspects. Given these considerations, a natural choice would be taking generalizing preference expressions. When equipped with CP-nets, such quanlitative language can handel different kinds of queries very efficiently.

In the second session (Nov. 18, 2009), we started with some discussion about preference compilation techniques, namely, structure-based and structure-free. Both aim to combine the quantitative and qualitative models in such a way that we can map diverse statements into a single, well-understood representation. Then, we had a quick look at uncertainty and utility functions. During the rest part of the meeting, we focused on preference specification and elicitation, discuessed about different methods which include "prior-based'' ones such as maximum likelihood and Bayesian reasoning, as well as ''prior-free" minmax regret method. For elicitation, since it usually works in a sequential way, we need to come up with either nice heuristics to determine the optimal sequence of queries. Or if the computational price is not a big concern, we can use the elegant partially observable Markov decision process (POMDP) model.

The paper provides a very nice framework of preference handeling. In the nex few months, we will study the existing methods for preference elicitation and representation and ideally be able to construct a more concrete framework to model preferences in energy market.

Dutch Flower Auction Recommender Agent

Last LARGE meeting, I (Meditya Wasesa) presented the status of the Recommender Agent development in the Dutch Flower Auction domain.

I reported that we have finalized the first step of data exploration, that is meant to elicit all determining factors which significantly influence the revenue of a good in Dutch Flower Auction domain. Our exploratory research infers that there are several auction design parameters which the auctioneer can adjust to control the level of revenue.

Knowing these auction design parameters, we have developed an analytical that we will drive the logic of the future recommender agent. At future we will implement our analytical model to a recommendation agent and will test it with simulation, lab experiments, and field experiments.