Products:

1
Chapter 9

• Products
• Representation, Design and Customization

Stand 20.12.00
2
Recommended Reference

• The algorithms are presented in this chapter in
  some detail. Further information can be found in
  the following original literature
• Stahl, A., Bergmann, R. Schmitt, S. (2000). A
  Customization Approach for Structured Products in
  Electronic Shops. 13th Bled Electronic Commerce
  Conference.
• Schmitt, S. Bergmann, R. (1999). Applying
  case-based reasoning technology for product
  selection and customization in electronic
  commerce environments. 12th Bled Electronic
  Commerce Conference.
• Bergmann, R. Vollrath, I. (1999). Generalized
  cases Representation and steps towards efficient
  similarity assessment. In W. Burgard, Th.
  Christaller A. B. Cremers (Hrsg.) KI-99
  Advances in Artificial Intelligence, Lecture
  Notes in Artificial Intelligence, 1701, Springer,
  195-206.

3
Products

• We understand products as the objects which are
  demanded by the customer and sold by the
  supplier.
• The concept of a product cover a wide range
• Technical devices (PCs, cars, video recorders,
  ....
• Natural objects (plants, food, animals,...)
• Real estate, houses,...
• Events (concerts,...), Travel, ...
• Services (insurance's, financial advice, ...)
• etc.
• The supplier may products have some on stock,
  others may have to be designed or modified by the
  supplier and again others may have to be ordered
  from other suppliers.

4
General Description Aspects

• Products have in general two descriptions
• one for the customer
• one for the supplier
• The description for the supplier is mainly
  intended
• to identify the product
• to design the product
• The descriptions uses attributes, properties etc.
• Technical products (but others too) are objects
  which are a (possibly complex) composition of
  atomic parts. Between the parts certain
  constraints are defined.

5
Representation of Products

• Representation in the form of frames and frame
  hierarchies
• The most important relations are (see chapters 4
  and 5)
• Is-a-relation More spezial - more general,
  including an inheritance mechanism
• Instantiation Class - Instance
• The part-of-relation between components
• Constraints between components (hard and weak
  constraints)
• Components have an inner structure and
  connections to the outside represented by port
  variables (constraints are defined between them)
• We have complex components and atomic components
  which cannot be decomposed.
• Between components the relation direct
  subcomponent is defined

6
Decomposition of Products
Product
The decomposition into parts is often necessary
to meet customer requirements
Some questions can only be answered on the level
of parts.
7
Functionalities (1)

• A functionality is the ability to carry out one
  or more intended functions, e.g.
• to drive, show a movie, play music,...
• A product (e.g. a machine or a technical device)
  realizes a functionality if it is able to carry
  out the intended functions.
• Functionalities are again described in two ways
• for the customer
• for the supplier
• The description is about what the functionality
  is and how it has to be performed (quality
  aspects). Both are usually expressed in terms of
  constraints.

8
Products and Functionalities (2)

• Devices and machines
• Concrete (e.g. technical as cars or manufacturing
  machines)
• Abstract (e.g. finite automata, computer
  programs)
• Component-orientation (static) Is interested in
  the structure of machines, their configuration
  etc.
• Process-orientation (dynamic) Is interested in
  the processes running on the machine e.g.
  organization, correctness, resources, values of
  parameters, etc. Here, it is the process for
  which we need the product, not the process during
  which the product is produced.
• Reminder Processes are (partially ordered) sets
  of actions
• Both aspects can be considered interleaved as
  well as completely independent of each other.

9
Products and Functionalities (3)

• Many products which are not technical devices
  share their basic properties, in particular the
  component orientation.
• Examples are travel offers and real estate
  objects.
• Their functionality is not given as directly
  described processes (like nice holidays) but
  indirectly in terms of constraints.
• The task is to find an optimal composition of
  components with respect to the (hard or weak)
  constraints.

10
Decomposition of Functionalities (1)
Functionality
Decomposition

• Decomposition of functionalities is often
  necessary because not one single product can
  realize a functionality.
• The decomposition terminates if realization is
  possible or it is clear that no realization can
  take place.
• Termination happens always at atomic actions but
  possibly earlier.

11
Decomposition of Functionalities (2)

• Example Decomposition of the complex function
  build a house
• Find a lot
• Select an architect
• Select companies for construction, electricity,
  roofs, ....
• Get a loan from a bank
• Make appropriate insurances
• etc.
• These functions are not only different but
  carried out by different companies.
• A portal can be provided from a company which
  organizes the search in such a way that the
  customer has nothing to do with it. Compare also
  chapter 14.

12
Products Realize Functionalities
General product
functionality
Object class Printer Company String Price Number
realizes
Object class Printing
taxonomy
Object class laser printer Superclass Printer
Object class colour printer Superclass Printer
Object class ink injection printer Superclass
Printer
instances
Object instance BestPr Company Miller Price
1200
Object instance WonderPr Company Mayer Price
1600
Subclasses and instances are chosen according to
customer utilities
13
Description by Semantic Nets

• Labeled Graph
• Node labels
• Demands (for products, for functionalities) and
  some properties
• Products and some properties
• Edge labels
• Product-product relations (first net)
• Demand-demand relations (second net)
• Demand-product relations (third net)
• See chapter 5

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The Net Labels (1) Products

• Product relations domain knowledge (and general
  knowledge about customers)
• Unary relations (properties, at the nodes)
• is available, has to be configured...
• Binary relations
• Part-of, kind-of, needs, improves, constraints
  between properties and parameters of products ...

15
The Net Labels (2) Functionalities as Demands

• Functionality relations Domain knowledge and
  customer preferences
• Unary relations (properties, at the nodes)
• technical data, intended utilities
• Binary relations (at the edges)
• part-of, kind-of, equivalence, is usually wanted
  both by the customer, is usually not wanted
  together by the customer, dependencies (and
  functionality requires another one as
  precondition or makes it necessary as a
  consequence)...

16
The Net Labels (3) Products as Demands

• Products required by demands do not necessarily
  exist, they are wishes in the first place
• Unary relations Desired technical data, quality
  properties, price, ...
• Binary relations Is usually wanted both by the
  customer, is usually not wanted together by the
  customer, ...

17
The Net Labels (4) Products and Functionalities

• Labels at the edges
• Products P realizes functionality F
• total or partial realization (other parts needed
  ?)
• quality degrees according to listed utilities
• possible constraints
• Link to other products that satisfy F too

18
Language Levels (1)

• The language in order to describe products has
  two dimensions
• Product orientation or functionality orientation
• Level of details (see chapter 5)
• Product orientation
• What are the products, e.g. MS Word, MS
  Powerpoint, Framemaker,...
• Levels of details e.g. available fonts, ...
• Functionality orientation
• Produce letters,, overhead slides, animation,
  ....
• Levels of details e.g. Business letters,
  standard letters, documents with special
  graphics,...

19
Language Levels (2)

• There is no unique language level, the choice
  depends on the purpose
• internal uses of the supplier
• external uses for customers
• According to the demands and the level of
  experience of the customer different language are
  appropriate.
• Consequences
• Products should have a complete description with
  all details and a location in the hierarchies
• There should be selected suggestions for certain
  language levels which often occur.

20
Customization

• Many shops do not further support customers
  after the retrieval step One can only select a
  product.
• This excludes parameterizable or configurable
  products They are not directly available as
  demanded but could after some work (adaptation or
  configuration) still be delivered.This work is
  called Customization (we mainly use the term to
  denote adaptation).
• Customization takes always place with respect to
  the query and requires domain knowledge.
• Similarity based retrieval and customization have
  to be combined.

21
The Continuum of Products

• 1) Fixed Products
• Product customization impossible
• Examples books, CDs, integrated circuits, etc.
• 2) Parameterizeable Products
• Limited customization capabilities
• Examples color of products, date and duration of
  vacations
• 3) Configurable Products
• Various customization capabilities
• Example complex technical systems (e.g., PCs),
• financial or insurance products

Complexity of Customization
22
Approaches to Customization
Method
Products
Knowledge Needs
parametrizable productsfew variants few
dependencies
rule-based transformation (Bergmann Wilke, 1996)
products customization rules
parametrizable products few variants few
dependencies
operator-based transformation (Bergmann 1998
Schmitt Bergmann, 1999)
products customization operators
configurable products many variants many
dependencies
component knowledgedependenciescontrol knowledge
knowledge-based configuration (Günter et al.,
1995-1998)
configurable products many variants many
dependencies
component knowledgedependenciescontrol cases
analogy-based configuration(Pfitzner 1993
Schumacher, 1998)
configurable products many variants many
dependencies
component knowledgedependencies prototype
configurations
compositional adaptation (Smyth, 1998 Stahl
Bergmann, 2000)
products including variant descriptions
parametrizable products many variants many
dependencies
generalized cases (Bergmann Vollrath, 1999-2000)
23
Transformational ApproachesBasic Notions

• We define the terms problem, solution, and
  quality
• P problem space (queries, possibly infinite)
• S solution space (products, possibly infinite)
• Q P x S R total function, called quality
  function R totally ordered set of quality
  values (typically set of real numbers)
• No assumption about the structure of P or S.
• Q assigns a quality value to each (problem,
  solution) pair
• Quality can stand for appropriateness, utility,
  degree of correctness, ...
• Quality is a more fine-grained correctness
  measure than just correct / incorrect

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24
Problem Solving Task
solution s
Problem p
System

• Goal of problem solving given a problem p
  (customers wish) determine a solution sÎS such
  that holds.
• Q contains the whole specification of the problem
  solving task.
• Looks like an optimization task !
• But Q is not completely known !

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25
Example

• Sales support for PC
• Problem Space set of possible combinations of
  requirements, e.g., P 2 Textprocessing, Games,
  Music,...
• Solution Space set of possible PC
  configurations,e.g., S 2 xyz-mainboard, 4.3GB
  Harddisk,...
• Quality function
• Note Q is not completely known, e.g. because of
• special offers
• special prices for pre-configured PCs

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26
Adaptation Operators (1)

• A case is a triple c(p,s,q) Î P x S x R C
• A case base is a finite set of cases
  CBc1,c2,...,cn Ì C
• A case c(p,s,q) is sound w.r.t. a quality
  function Q iff qQ(p,s) holds.
• Cases with different quality can be distinguished
• The change of quality through adaptation can be
  captured

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27
Adaptation Operators (2)

• An adaptation operator a is a partial function
  a C C, i.e., a transforms a case into a
  successor case.
• The adaptation changes the product and its
  quality .
• The adaptation container A is a set of adaptation
  operators A a1, a2 ,... .
• An adaptation operator a is sound w.r.t. Q iff
  for all c,cÎC holds if a(c)c and c is sound
  then c is sound.

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Example (cont.)

• Adaptation operator a1 If you add 4.3 GB hard
  disk space, then the PC will fulfill the
  requirements of database applications and the
  price will increase by 985 DM.a1(p,s,q) (p È
  DB-Applics, s È 4.3 GB hard disk, q-985)

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29
Adaptation Process

• Current problem p
• Retrieved case cR Î CB
• Adaptation transforms cR into the adapted case
  cA by applying adaptation operators from A cA
  an ... a1(cR)
• We write
• If the case base and the adaptation container
  are sound w.r.t. Q, then every adapted case is
  also sound w.r.t. Q.

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30
Correctness and Completeness

• A CBR system has only restricted knowledge about
  Q
• the case base CB
• adaptation container A.
• Correctness A CBR system always produces the
  best solutions it can produce w.r.t. its
  knowledge about Q
• Completeness A CBR system produces a solution
  to a problem if one exists on the basis of its
  knowledge about Q

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31
Discussion of Completeness and Correctness
Properties

• Correctness and completeness are desirable
  properties for a CBR system.
• For pure case retrieval systems we usually
  require completeness and correctness (retrieve
  most similar case).
• BUT impossible with adaptation deciding whether
  a solution exists w.r.t. to CB and A is
  undecideable in the general case.
• How to deal with this
• Find relaxed version of completeness and
  correctness, e.g. like in PAC learning.
• User-controlled adaptation

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32
What about Similarity ?

• Similarity measure should
• select adaptable cases and
• select the best case for adaptation
• All knowledge is already contained in CB and A !
• Similarity measure contains compiled knowledge
  from A.
• A similarity measure sim P x C 0..1 is
  correct iff
• there exists a bijective monotonous function f R
  0..1 and
• sim(p,c)

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33
Customization Operators

• Product customization process divided into units
  of changes
• Operator Unit of Change
• Customer chooses which changes to make

influences
influences
changes
34
Example for the Customization Process
Fishing Trip
Customized Fishing Trip
35
Adaptation Operator Representation

• Name
• Precondition
• Parameters
• Parameter Condition
• Action
• Reference Class

36
Application Area

• Product categories
• unchangeable products,
• e.g., ICs, etc.
• products with few changeable features,
• e.g., vacations, cars, etc.
• products with many changeable features,
• e.g., computers, networks, design tasks, etc.
• Adaptation Operators only for
• configurable products in general
• products without many possibilities for
  modifications
• products with low interaction

37
Example Domain Electro-Mechanical Components

• Testbed for the Customization Module
• The Electronic Product Catalog
• Catalog contents thousands of electro-mechanical
  components and devices
• floating and magnetic switches, level controls,
  liquid level indicators, moisture detectors for
  cooling ceilings, etc.
• Expert knowledge for product selection needed
• Catalog supported by CBR Technology
• ESPRIT Project SMARTSELL
• Many product variation possibilities
• length, diameters, (mandatory) accessories, etc.

38
Example Domain Level Measure Device
TSK
MWU
DisplayInstrument
Transducer
screw-in nippel
Customer canchoose length
float
SKG
Liquid LevelTransmitter
Switching Unitfor Signals
39
System Architecture
CLIENT
Customization GUI
Customization Client
XML
XML
TCP/IP
TCP/IP
Internet Server 2
Controlling Service with Cases
SERVER
Customization Server
Server GUI
Internet Server 1
Operator DB
Operator DB Service
Authoring Tool
XML
40
Communication between the Components
Controlling Service
System
OOCCL

• OOCCL ORENGE Operator Customization Client
  Language
• OOCSL ORENGE Operator Customization Service
  Language
• OOOML ORENGE Operator Object Markup Language

Customization Client
OOCSL
Operator DB
OOOML
Customization Server
41
Example Domain Customization Activation
?
?
?
42
Compositional Adaptation (1)

• Configuration problems can be described through a
  decomposition problem

Overall problem (e.g., the configuration of a PC)
Complex sub-problems (e.g., the configuration of
a sub-system)
Atomic sub-problems (e.g., the selection of a
component)
Dependencies between sub-problems (e.g.,
compatibility between components)
Goal Effective solving of such a problem
43
Compositional Adaptation (2)

• 1) Retrieve a solution for the complete
  configuration problem
• 2) Determine still unsolved sub-problem
• 3) Retrieve suitable sub-solution
• 4) Try to integrate this sub-solution into the
  overall solution
• 5) Repeat 2) - 4) until a solution for the
  overall
• problem is obtained
• Basic idea Implement product customization
  (resp. adaptation) of highly structured products
  (resp. cases) again similarity-based!

44
Compositional Adaptation (3)
Problem
Solution
Final Solution
45
Compositional Adaptation for Product
Configuration (I)

• Basic assumptions
• Existence of two different bases
• 1) pre-configured base-products
• 2) single components
• Availability of a constraint propagation system
• used for the definition of domain dependent
  constraints
• is able to validate the consistency of a
  generated solution
• The configuration problem (i.e. the query) is
  described by an incomplete product description

46
Compositional Adaptation Product Configuration
(II)
Constraints
Adaptation Cycle
47
Example PC Configuration
Query
Case
Component Exchange
48
The Customization Problem

• The customization problem can be characterized as
  a combination of two different problems
• 1) Constraint Satisfaction Problem
• Finding of a consistent product, i.e., a product
  that fulfills all
• technical restrictions
• 2) Optimization Problem
• Finding of a product that matches the customer
  demands as
• well as possible
• Goal Maximization of the similarity between
  the product
• and the query

49
Controlling the Adaptation Process

• The adaptation cycle realizes a hillclimbing
  approach
• every adaptation step navigates in the solution
  space
• the similarity measure represents a kind of
  quality function
• the adaptation order plays an important role
• Two basic approaches
• 1) Preservation of consistency
• The result of every adaptation step is a
  consistent product
• 2) Temporary loss of consistency
• Constraint violations between adaptation steps
  are allowed
• better configuration results
• but significant higher computation effort

50
Implementation
51
Generalized Cases

• Motivated by design reuse applications
• Cases are descriptions of previous designs
• Nowadays EE designs are made for a range of
  problems
• Example design of an n-bit adder unit, with n
  1...64

traditionalcases
generalizedcases
Solution
Solution
Extensionof view
Problem
Problem
52
Definition of Generalized Cases
Solution Space
Problem Space

• Definitions
• A traditional (point) case
• A generalized case

Semantics gc is equivalent to the set of
individual cases i.e.,

• Remarks
• gc is an abbreviation for a set of closely
  related cases
• gc should have a compact representation

53
Different Kinds of Generalized Cases

• Constant Solution GCSet of problems P with same
  solution s, i.e.,
• Functional Solution GCSet of problems
  with solution f(p), i.e.,
• Independent Alternative Solution GCSet of
  problems P with a set of alternative solutions
  Si.e.,

S
P
P
54
Similarity Measures

• Assumption
• Available similarity measure sim for point
  cases
• Definition
• Canonical extension of sim for generalized
  cases sim

S
S
gc1
c1
CanonicalExtension
gc2
sim
sim
c2
P
P
q
q
55
Equivalence
CBR System withTraditional Cases
CBR System withGeneralized Cases
Case Base
Case Base
Similarity Measure
Similarity Measure

• Case with same solution
• is retrieved

q
q
56
Selecting Solutions
Generalizedcase
Pointcase
Retrieve
Select
Reuse

• Definition
• Canonical selection of point cases
• from generalized case gc
• for a query q and
• similarity measure sim
• Select most similar point cases from generalized
  case

Select step is a kind of adaptation
57
Representing Generalized Cases

• Compact representation is essential for
  generalized cases
• (Just enumerating the elements is not useful)
• Goal
• compression of the representation of the case set
  
• encode case specific adaptation knowledge
• simplify storage of generalized cases
• enable similarity assessment
• Basic assumption Attribute-Value-Representation
• Problem Space n problem attributes
• Solution Space m solution attributes
• Query
• Case

58
Hyperrectangles Representation

• Representing cases as nm dimensional
  hyperrectangles
• Very simple representation independent set for
  each attrib.
• Selection not necessary
• Simple and efficient similarity measures
• the canonical extension ofis

Subsets,Intervals
Subsets,Intervals
Local Similarity Measures
Monotonic aggregation
Canonical extension of local similarity
59
Representation with Constraints

• Extended representation as follows
• Constraints between problem and solution
  variables
• Example

auxiliary vars
Set of Constraints
Bounding Box
P2
d
3
c
Constraints Area
b
1
Bounding Box
P1
a
3
1
60
Similarity Assessment
P2
sim ³ sim ³ sim

• Using the bounding box for similarity assessment
• Similarity to bounding box is easy to compute(
  Hyperrectangles Rep.)
• Only estimation of similarity!!

sim
q
gc
P1

• Constraint satisfaction techniques in special
  situations
• Assign query values to problem space variables in
  constraints
• Apply CS to find consistent values for auxiliary
  and solution variables
• If Successful canonical selection performed by
  CS, exact similarity can be determined
• Works only if query completely falls in the
  generalized case !

61
Application Domain IP Reuse

• Intellectual Properties (IPs) in Electronic
  Engineering
• Skill of the designer
• Redesign would consume significant time
• Reuse avoids reinventing the wheel
• IPs developed for a design space rather than for
  a fixed design
• Reuse difficult No Intelligent Support

• IP Representation
• Measures describe general IP properties
• gate count, power dissipation, latency, maximum
  clock frequency
• Application specific parameters describe the IP
  function
• e.g. frequency, bus width, ...
• Design space parameters unique to a specific
  implementation
• e.g. number of pipeline stages, degree of
  parallelism, ...
• Design Design Synthesis Information Parameters
• e.g. reference technology, scripts for synthesis
  tools, ...

62
Example IP

• Example Discrete Cosine Transformation (e.g.
  used for MPEG-2)
• Design Design Synthesis information scripts
  for synthesis tool
• Parameters f frequency a area
  w width s subword
• Constraints

63
Design Space of the example IP
64
Representation of IPs

• Generalized Cases
• Combine all parameter combinations to one single
  case
• Constraints
• Express the complex dependencies between the
  design parameters
• Constraints represent case specific knowledge
• Store constraints within the case

65
A Tool to Support Interactive Selection
66
Advantages of Generalized Cases

• Representation of experience that naturally
  covers a space
• Turn several traditional cases into a single
  generalized case
• Means for representing case-specific adaptation
  knowledgeTraditional case Adaptation Knowledge
  Generalized case
• Integration with traditional cases in a single
  case base

67
Summary

• The representation issues of objects are
  concerned with products, functionalities and the
  relations between them.
• Adaptation is done by adaptation operators are
  composed to an adaptation process.
• Product configuration needs also operators.
• The algorithms make use of monotonic aggregation
  functions and perform a local optimization.
• Generalized cases combine many cases which then
  can be treated in a uniform way.
• Major examples are complex technical devices.