1. Advanced Embodiment Design 26 March 2015
Intelligence in products, services and software Principles, techniques and applications of knowledge and information management, intelligent algorithms and artificial intelligence
Advanced Embodiment Design
26 March 2015
Van der Grintenzaal
Wilfred van der Vegte, Zoltan Rusak
Computer-Aided Design Engineering

2. Overview
Apparent intelligence in artefacts is typically based on embedding knowledge in it.
artefact = product, software, service.
particularly interesting type of software:
design support  modelling, simulation, programming
data  information  knowledge  wisdom
increasing level of meaningfulness and utility 
knowledge is supposedly the highest level that can be embedded in artefacts
November 17, 2018

3. Types of knowledge and information
According to availability to machines and humans
Knowledge Conversion: process of transforming data/info/knowledge to
a form in which it can be used
(Nonaka & Takeuchi)
explicit
reproducible, recordable
textual
description
in a book
programmed
instruction
for a machine
explicit
informal
knowledge
formal
knowledge
explicit
formal
knowledge
not processable
informal
formal
processable
a person’s skills
in accomplishing
a task
implicit
informal
knowledge
tacit
knowledge Ø
not (directly)
reproducible, recordable
implicit (tacit)
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4. Processing knowledge and information
designers
use by people
use by machines
design support
end users
products
store
formalize
companies
services
make explicit
explicit
informal
knowledge
formal
knowledge
tacit
knowledge Ø
Processing knowledge and information
November 17, 2018

5. Knowledge technologies
Formalization by humans, storage and use by machines: Knowledge-based systems, Knowledge-based engineering but also: conventional embedded software in products
Storage of explicit knowledge by machines: Knowledge bases, Knowledge and data warehousing
Formalization by machines: Soft computing, Machine learning, Knowledge discovery and Data mining
Making explicit knowledge stored in machines available to people: Search engines and Knowledge portals
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6. Formalization by humans, storage and use by machines: Knowledge-based systems/engineering
Knowledge is prepared by experts (‘knowledge authors’) as rules IF material(part_A OR part_B) = thermoplastic AND no_extra_part_allowed = TRUE | THEN connection_type = snap_fit
Fuzzy logic: allows rules to deal with imprecise inputs ‘large’, ‘old’, ...
Rules are stored in a knowledge base (KB), an “inference engine’” extracts knowledge / finds appropriate rules in KB
KBS typically focuses on specific applications, e.g.:
Design support: calculating dimensions for snap-fit design; ‘Automated design’ of elevators, escalators, conveyor belts, etc.
Services: Expert systems to assist in maintenance, to guide workers in call centers, etc.
Products:
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7. Formalization by machines
Soft computing: using inexact solutions to find exact answers (which may be correct, or correct within margins). Example: dealing with linguistic input containing imprecise terminology “serve one cold beer” (how cold is cold?)
Machine learning: machines learn from data with known properties. Example: face recognition in cameras
Data mining: discovering new patterns in large data sets (data with unknown properties) Example: identifying a segment of supermarket customers with similar needs and preferences SC DM ML
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8. artificial neural nets association rule learning machine learning
umbrella terms
methods/techniques
...
fuzzy logic
soft computing
chaos theory
bayesian networks
genetic algorithms
artificial neural nets
association rule learning
machine learning
decision trees
cluster analysis
classification
inductive logic programming
sparse dictionary learning
data mining
regression analysis
...
factor analysis
outlier detection
...
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9. Automated knowledge formalization: data mining
Main objective: creating knowledge from data (explicit informal knowledge)
Data can be from various digital/analog sources: internet, office documents, video, images, sound (e.g., MP3 file)
Mostly automatic – no knowledge authors Application areas are different: usually applied to analyse massive amounts of data, i.e. big data
Typically computation-intensive  not real-time but off-line extraction for services towards companies, consumers or both
November 17, 2018

10. Automated knowledge formalization: data mining
Three steps (can appear independently):
data acquisition for instance: digitizing documents or audio recordings
clustering and structuring Example: Carrot2 clusters results from various search engines.
representing knowledge in an explicit form to make generalization or prediction possible.
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11. Commercial applications of data mining: recommender systems
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12. Commercial applications of data mining: identifying segments of supermarket customers with similar needs and preferences
This is my bonuskaart (Albert Heijn supermarket loyalty card).
What is going to happen if you will all copy it from Blackboard and use it while shopping at Albert Heijn?
Will Albert Heijn be pleased?
November 17, 2018

13. Automated knowledge formalization: data mining Possible applications in design support
Unlike KBE, data mining does not present a solution to a prepared design problem. any available data ↓ knowledge that might be relevant for a problem the designer could have. Advantage: outcomes can be beyond the obvious Disadvantage: outcomes might not be useful at all
It might find relationships where deterministic models fail: e.g., predicting consumer taste or user behaviour
Examples: image search, shape search
November 17, 2018