Configuration Systems - CSE Sudhan Kanitkar

Outline of the Presentation What is a Configuration System ? Requirements, Issues and Approaches Use of Case-based Reasoning for Configuration Systems Adaptation Problem and its Issues

What is Configuration System ? What is a Configuration Task ? – Components – Connections – Constraints – Requirements Example: Electrical system of a house Example: Automatic Code Generation

Configuration – A Design Task Function of Configuration System is thus analogous to a Design Task Types of Design – Routine Design – Innovative Design – Creative Design Configuration Task can be modeled as Routine Design

Configuration Task Predefined Components Connection of Components Task Specification – Set of components (XCON) – Constraints Constraint Satisfaction Problem

Properties Comparatively tractable design task Usually applied on relatively Closed Problems Comparison with Planning tasks Full Automation which is desired is possible.

Approaches Rule-Based Systems – Very attractive choice owing to closeness to Configuration System – Drawbacks - Knowledge Acquisition, Consistency checking, Lack of modularity & Adaptability – Limited use resolves these issues. – Found useful for representation of Glue components

XCON – eXpert CONfigurer Assist in configuration of DEC’s VAX computer systems Input: Required Characteristics of computer Output: Specification of computer system Inference Engine: Forward chaining rules, 95-98% accuracy Processed orders by 1986 Developed to overcome lack of technical knowledge of sales people

Approaches Concept Hierarchies – Use of Object-oriented techniques such as frames for knowledge representation – Supports IS-A and HAS-A relationships – Supports the use of description logics – Enables definitions of “Skeletal Plans”. – OWL – Web Ontology Language may provide the ability to represent data on the web.

Concept Hierarchy – An Example Component MonitorKeyboardMainboardProcessorPC-CaseOSMouse Single-CoreDual-Core PentiumCeleronCoreDuo ServerClient Windows 2003XP-Prof.XP-Home

Approaches Structure-Based Approach – Relies on forming solutions based on structure of knowledge (Concept Hierarchies) – Solution is represented in same form as the knowledge and can be generalized or specialized. e.g. Music-Photos-Videos PC – Flatscreen Monitor, Printer, Card Reader. e.g. Database Server – Hard drive, RAM, Processor

Example - Structured Approach Music – Photos – Videos PC DVD-R/W Printer Sound Card Flatscreen Monitor Video Card Card Reader

Example – Structured Approach Database Server ProcessorOS MemoryHard Drive

Approaches Constraint-Based System – Usually used in specifying requirements rather than knowledge representation – Used in conjunction with a knowledge base represented using a different approach – Impose restrictions as relations between objects. – Incremental built up of a solution causing reduction – Gives rise to the concept of constraint satisfaction – e.g. Use with skeletons generated by structured approach to obtain complete solutions.

Approaches Resource-Based Approach – Requirements specify only the functionality of the developed system. – Component knowledge needs to be represented in terms of the task it performs. – Depending on tasks needed to fulfill a function components are selected. – Connectivity knowledge representation can be incorporated into components itself

Example – Resource based Approach Robot - Localization – GPS – Map building and Path planning algorithms Robot – Obstacle Avoidance – Sensors – IR, Sonar, Laser, Camera – Potential fields and Obstacle avoidance algorithms.

Approaches Case-based Reasoning – Stores a set of solutions called Cases – Possible use of preprocessing on stored cases – Use these case to generate new solutions – May/May Not use a case completely – Methods of use: Retrieval Adaptation Derivational Analogy

Example - Case-Based Reasoning Problem is to configure an alarm system for a house from a set of pre defined components. Problem Description consists number of rooms, windows and doors on each floor AttributeValue Doors1 Rooms-12 Windows-12 Rooms-21 Windows-21 Fire ProtectionTrue Motion ProtectionFalse

Example – Case based Reasoning Properties – Maximum 6 sensors to one circuit – Fire & Motion sensors should be on separate circuits so that they can be operated independently – Fire & motion if required should protect all the rooms – Atleast one door should have a door delay sensor – Each system should have atleast one sensor unit and one alarm unit

Example – Case Based Reasoning

Case-Based Reasoning Compositional Adaptation – Select 2-3 cases that match most closely with the input case. – For each attribute use the value from the case which suits the solution the best – No modifications are made on the values – Values are however used from multiple cases

Case Based Reasoning

Case-Based Reasoning Transformational Adaptation – Used when compositional adaptation doesn’t provide satisfactory – Arises when input case is quite different from all the cases in the case base. – Starts with an approach similar to compositional adaptation – Involves changing of values, adding new values or deleting values for certain attributes.

Case Based Reasoning

Adaptation – Knowledge Growth Configuration systems provide – Case Representation – Similarity Assessment – Retrieval Configuration system don’t provide – Case adaptation – New case generation Result: No learning

Adaptation as Configuration Adaptation can be said to be an incremental step to Configuration. Should be able to generate new cases Add the new cases to the case base Update the inference engine accordingly

Adaptation Techniques Substitutional Adaptation Transformational Adaptation Derivational Adaptation Composition Adaptation (Configuration) Incremental level of complexity in above methods Actual adaptation system may be semi- automatic

Adaptation – Configuration Perspective Configuration task involves organizing an identifiable and predefined set of components in order to satisfy some constraints Configuration Problem should involve: – Set of Configurable components – Set of ordering constraints – Configuration Operators that encode valid component configurations Adaptation Operators

Configuration Operators Describe a unit of operation in the configuration task Application of a certain operator will govern the next step in the configuration process Maintain the dependencies and constraints at each stage Operators are an abstract concept and implementation is domain dependent. Application of operators is generally controlled by a certain rule based system

Object Model for Electrical System

Object Model of Electrical System

Common Configuration Operators Configure (Object) Parameterize (Attribute) Compose (Concept) Decompose (Concept) Specialize (Concept)

Benefits of Operators Explicit representation of decisions made during generating solution and dependencies Support for withdrawing decisions in effective manner Can easily be controlled through rules and constraints

Configuration based Adaptation Most CBR systems implement adaptation limited to substitutional techniques Difference between description space and solution space. If there is a mapping between these two spaces then it might be possible to generate cases for various solutions created

Adaptation Operator & Action Adaptation Operator is applied on attributes of a solution to adapt them into a case This assumes the use of concept hierarchy representation Adaptation Action involves application of a sequence of one of more Adaptation Operators – Simple Action – Applies on just one attribute – Complex Action – Applies on a more than one operator

Doesn’t always work Conflict Resolution Optimal solution not found/Incorrect of Incompatible Solutions Revision of Decisions Repair of a partial solution

Inferences There are primarily two approaches – Case based reasoning – Conceptual hierarchy Although the latter is more promising in almost all criteria it also comes with a level of complexity Most of the configuration systems currently use the CBR based techniques.

References Configuration techniques for Adaptation – Wilke, Smyth, Cunningham. Knowledge-Based Configuration – Survey and Future Directions – Andreas Gunter, Christian Kuhn