science

Knowledge-based configuration

Knowledge-based configuration is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Knowledge-based configuration rather than just read about it. In short: Knowledge-based configuration, also referred to as product configuration or product customization, is an activity of customising a product to meet the needs of a particular customer. The product in question may consist of mechanical parts, services, and software.

Key takeaways

Knowledge-based configuration belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Knowledge-based configuration to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Knowledge-based configuration from memory before moving on to harder problems.

Reference excerpt

Knowledge-based configuration, also referred to as product configuration or product customization, is an activity of customising a product to meet the needs of a particular customer. The product in question may consist of mechanical parts, services, and software. Knowledge-based configuration is a major application area for artificial intelligence (AI), and it is based on modelling of the configurations in a manner that allows the utilisation of AI techniques for searching for a valid configuration to meet the needs of a particular customer.

Background Knowledge-based configuration (of complex products and services) has a long history as an artificial intelligence application area, see, e.g. Informally, configuration can be defined as a "special case of design activity, where the artifact being configured is assembled from instances of a fixed set of well-defined component types which can be composed conforming to a set of constraints". Such constraints represent technical restrictions, restrictions related to economic aspects, and conditions related to production processes. The result of a configuration process is a product configuration (concrete configuration), i.e., a list of instances and in some cases also connections between these instances. Examples of such configurations are computers to be delivered or financial service portfolio offers (e.g., a combination of loan and corresponding risk insurance).

Theory and complexity of configuration Numerous practical configuration problems can be analyzed by the theoretical framework of Najmann and Stein, an early axiomatic approach that does not presuppose any particular knowledge representation formalism. One important result of this methodology is that typical optimization problems (e.g. finding a cost-minimal configuration) are NP-complete. Thus they require (potentially) excessive computation time, making heuristic configuration algorithms the preferred choice for complex artifacts (products, services).

Configuration systems Configuration systems, also referred to as configurators or mass customization toolkits, are one of the most successfully applied artificial intelligence technologies. Examples are the automotive industry, the telecommunication industry, the computer industry, and power electric transformers. Starting with rule-based approaches such as R1/XCON, model-based representations of knowledge (in contrast to rule-based representations) have been developed that strictly separate product domain knowledge from problem solving knowledge—examples thereof are the constraint satisfaction problem, the Boolean satisfiability problem, and different answer set programming (ASP) representations. There are two commonly cited conceptualizations of configuration knowledge. The most important concepts in these are components, ports, resources and functions. This separation of product domain knowledge and problem solving knowledge increased the effectiveness of configuration application development and maintenance, since changes in the product domain knowledge do not affect search strategies and vice versa. Configurators are also often considered as "open innovation toolkits", i.e., tools that support customers in the product identification phase. In this context customers are innovators who articulate their requirements leading to new innovative products. "Mass Confusion" – the overwhelming of customers by a large number of possible solution alternatives (choices) – is a phenomenon that often comes with the application of configuration technologies. This phenomenon motivated the creation of personalized configuration environments taking into account a customer's knowledge and preferences.

Configuration process Core configuration, i.e., guiding the user and checking the consistency of user requirements with the knowledge base, solution presentation and translation of configuration results into bill of materials (BOM) are major tasks to be supported by a configurator. Configuration knowledge bases are often built using proprietary languages. In most cases knowledge bases are developed by knowledge engineers who elicit product, marketing and sales knowledge from domain experts. Configuration knowledge bases are composed of a formal description of the structure of the product and further constraints restricting the possible feature and component combinations. Configurators known as characteristic based product configurators use sets of discrete variables that are either binary or have one of several values, and these variables define every possible product variation.

Software and service configuration Recently, knowledge-based configuration has been extended to service and software configuration. Modeling software configuration has been based on two main approaches: feature modeling, and component-connectors. Kumbang domain ontology combines the previous approaches building on the tradition of knowledge-based configuration.

See also Configurator Configure price quote Constraint satisfaction Feature model Mass customization Open innovation Product differentiation Product family engineering Software product line

References

Conference and journal papers

Books

External links 20+ years of International Workshops on Configuration

Research prototypes 1991 PLAKON / Project TeX-K 1999 Konwerk / Project Prokon 2002 ConIPF 2003 WeCoTin 2005 Kumbang tools 2014 WeeVis (Wiki-based learning environment for simple problems) Archived 2014-03-01 at the Wayback Machine

Journal special issues on configuration AIEDAM 1998 Special Issue on Configuration Design IEEE Intelligent Systems Special Issue on Configuration 1998 (vol. 13, No. 4) AIEDAM 2003 Special Issue on Configuration IEEE Intelligent Systems Special Issue on Configuration 2007 Special Issue on Configuration in the International Journal of Mass Customization 2006 International Journal of Mass Customization Special Issue on Configuration 'Advances in Configuration Systems' 2010 (vol 3, No: 4). AIEDAM 2011 Special Issue on Configuration AI Communications 2013 Special Issue on Engineering techniques for knowledge bases

Worked examples

Example 1 — a first encounter with Knowledge-based configuration

Start with the simplest possible case. Write down what Knowledge-based configuration claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Knowledge-based configuration before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Knowledge-based configuration ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Knowledge-based configuration

In research: Knowledge-based configuration appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Knowledge-based configuration in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Knowledge-based configuration is common in secondary-school and first-year university syllabi. It links to neighbouring topics Artificial intelligence, Innovation, so understanding it makes those chapters shorter.
In everyday life: Look for Knowledge-based configuration outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Knowledge-based configuration in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Knowledge-based configuration means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Knowledge-based configuration out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Knowledge-based configuration in simple terms?

Why does Knowledge-based configuration matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Knowledge-based configuration?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Knowledge-based configuration.