User-Involved Tradeoff Analysis in Configuration Tasks

We describe configuration systems using constraint problem solving formalisms where feasible products are computed by constraint problem solvers. A feasible product is a configuration of constituent components that violates none of the configuration constraints and meets users' preferences as much as possible. To help users find desirable products, a system must possess and understand users' preference model as well as value functions. A constraint-based multi-attribute optimization problem (MOP) assigns utility functions to the set of feasible configurable products so that optimal ones stand out. Due to the incompleteness and uncertainties of user's preference models, optimal solutions are difficult to compute in practical settings if systems do not constantly interact with users and refine user's preference models. While building four user-involved MOPs, we have accumulated a set of interaction principles that optimize user and system collaboration while computing optimal solutions. In particular, we will concentrate here on our approaches and solutions to address tradeoff tasks in interactive MOPS