Anna Ryabokon

Configuration Knowledge Representation and Reasoning

Configuration models specify the set of possible configurations (solutions). A configuration model together with a defined set of (customer) requirements are the major elements of a configuration task (problem). In this chapter, we discuss different knowledge representations that can be used for the definition of a configuration model. We provide examples that help to further develop the understanding of the underlying concepts and include a UML-based personal computer (PC) configuration model that is used as a reference example throughout this book.

Optimization methods for the partner units problem

In this work we present the Partner Units Problem as a novel challenge for optimization methods. It captures a certain type of configuration problem that frequently occurs in industry. Unfortunately, it can be shown that in the most general case an optimization version of the problem is intractable. We present and evaluate encodings of the problem in the frameworks of answer set programming, propositional satisfiability testing, constraint solving, and integer programming. We also show how to adapt these encodings to a class of problem instances that we have recently shown to be tractable.

Re)configuration based on model generation

Reconfiguration is an important activity for companies selling configurable products or services which have a long life time. However, identification of a set of required changes in a legacy configuration is a hard problem, since even small changes in the requirements might imply significant modifications. In this paper we show a solution based on answer set programming, which is a logic-based knowledge representation formalism well suited for a compact description of (re)configuration problems. Its applicability is demonstrated on simple abstractions of several real-world scenarios. The evaluation of our solution on a set of benchmark instances derived from commercial (re)configuration problems shows its practical applicability.

Combining Heuristics for Configuration Problems Using Answer Set Programming

This paper describes an abstract problem derived from a combination of Siemens product configuration problems encountered in practice. Often isolated parts of configuration problems can be solved by mapping them to well-studied problems for which efficient heuristics exist (graph coloring, bin-packing, etc.). Unfortunately, these heuristics may fail to work when applied to a problem that combines two or more subproblems. In the paper we show how to formulate a combined configuration problem in Answer Set Programming (ASP) and to solve it using heuristics a la hclasp. In addition, we present a novel method for heuristic generation based on a combination of greedy search with ASP that allows to improve the performance of an ASP solver.

OOASP: Connecting Object-Oriented and Logic Programming

Most of contemporary software systems are implemented using an object-oriented approach. Modeling phases – during which software engineers analyze requirements to the future system using some modeling language – are an important part of the development process, since modeling errors are often hard to recognize and correct.

Representing Production Scheduling with Constraint Answer Set Programming

Answer Set Programming and Constraint Programming constitute declarative programming approaches with different strengths which have already been shown to be highly effective for many hard combinatorial problems. In this article we discuss two hybrid Constraint Answer Set Programming approaches with regard to their suitability for encoding production scheduling problems. Our exemplifications are done on the basis of a production scheduling problem of Infineon Technologies Austria AG.

Conflict-Based Program Rewriting for Solving Configuration Problems

Many real-world design problems such as product configuration require a flexible number of components and thus rely on tuple generating dependencies in order to express relations between entities. Often, such problems are subject to optimization, preferring models which include a minimal number of constants substituted in existentially quantified formulas. In this paper we propose an approach based on automated program rewriting which avoids such substitutions of existentially quantified variables that would lead to a contradiction. While preserving all solutions, the method significantly reduces runtime and solves instances of a class of real-world configuration problems which could not be efficiently solved by current techniques.