Interactive Cost Configuration Over Decision Diagrams

Abstract

In many AI domains such as product configuration, a user should interactively specify a solution that must satisfy a set of constraints. In such scenarios, offline compilation of feasible solutions into a tractable representation is an important approach to delivering efficient backtrack-free user interaction online. In particular,binary decision diagrams (BDDs) have been successfully used as a compilation target for product and service configuration. In this paper we discuss how to extend BDD-based configuration to scenarios involving cost functions which express user preferences. We first show that an efficient, robust and easy to implement extension is possible if the cost function is additive, and feasible solutions are represented using multi-valued decision diagrams (MDDs). We also discuss the effect on MDD size if the cost function is non-additive or if it is encoded explicitly into MDD. We then discuss interactive configuration in the presence of multiple cost functions. We prove that even in its simplest form, multiple-cost configuration is NP-hard in the input MDD. However, for solving two-cost configuration we develop a pseudo-polynomial scheme and a fully polynomial approximation scheme. The applicability of our approach is demonstrated through experiments over real-world configuration models and product-catalogue datasets. Response times are generally within a fraction of a second even for very large instances

An introduction to personalization and mass customization

“…The set of potential configurations is defined implicitly by the configuration knowledge base. A configuration system (configurator) determines a configuration that takes into account the defined set of requirements and the constraints in the configuration knowledge base of efficient reasoning engines that often exploit compiled knowledge representations such as binary decision diagrams (BDDs) (Andersen et al 2010) to more efficiently determine whether a solution exists for the given configuration task. In situations where no solution can be identified for a given set of customer requirements, conflict detection (Junker 2004) and diagnosis approaches (Bakker et al 1993;Felfernig et al 2004Felfernig et al , 2012 are applied to assist users in finding a way out from the no solution could be found dilemma.…”

Automated Analysis in Feature Modelling and Product Configuration

“…There are also some proposals to combine description logics and constraint satisfaction problems [35]. Furthermore, we have found papers in the product configuration literature that use binary decisions diagrams (BDD) to represent and solve configuration problems [9,4,28] and also proposals which combine CSPs with BDD techniques to obtain better product configurators [61].…”

Modeling and Solving Distributed Configuration Problems: A CSP-Based Approach

“…One of the earliest constraint-based configurators was the COSSACK system [22], which was developed already in the mid-1980s. Configuration problems have also been a test bed for advanced constraint-based techniques, for example to analyze aspects of variable interchangeability and symmetry, soft constraints or questions of interactive problem solving, see, e.g., [10], [25], [4], or [3].…”