Explanation-aware computing - A new software paradigm?

Explanation-aware Computing:
A new software paradigm?
Dr. Thomas Roth-Berghofer
University of Hildesheim, Germany

Erklärungsfähige
Softwaresysteme Kassel, Germany, 30 June 2011

Freitag, 1. Juli 2011

Thomas Roth-Berghofer

industry

academia

research

AG Erklärungsfähige Softwaresysteme Universität Kassel — 2011-06-30 Folie 2 Stiftung Universität Hildesheim


Software engineer, Consultant,
Manager Quality and Support
empolis GmbH
(now: Attensity Europe)

industry

academia

research



empolis GmbH

industry

academia

research
Senior researcher
German Research Center for
Artiﬁcial Intelligence (DFKI) GmbH



empolis GmbH

industry

Visiting Professor
University of Hildesheim
academia
Lecturer at University of
Kaiserslautern

research
Senior researcher



empolis GmbH

industry

Visiting Professor
Starting in September 2011:
University of Hildesheim
academia
Lecturer at University of
Kaiserslautern

research
Senior researcher



Characteristics of ,good‘ software




use Wit
to hnice
Easy use
r inte
rfac
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in ing Hel
pla pful
ex
S elf




use Wit
to hnice
Easy use
r inte
rfac
e
in ing Hel
pla User centred pful
ex
S elf




use Wit
to hnice
Easy use
r inte
rfac
e
in ing Hel
pla User centred pful
ex
S elf

All in all:
Great user experience expected



Overview
Motivating analogy:
Car wash tunnel

Explanation basics

Example 1: Semantic search
engine RadSem

Example 2: Open source case-
based reasoning tool myCBR

Wrap-up



Car Wash

http://www.feuerverzinken.com/uploads/media/Belob-Waschstrasse_01.jpg


Car wash
options

http://www.feuerverzinken.com/uploads/media/Belob-Waschstrasse2_01.jpg


Car wash
options
EXTERIOR WASH
SEALER WAX

TRIPLE FOAM POLISH
WHEEL BRITE
UNDERCARRIAGE WASH
DOUBLE BOND (With Teﬂon)
TIRE SHINE



Car wash Our exterior wash process uses only the
finest quality materials formulated especially

options
for today's automotive finishes. Our wash
water is filtered with a filtration system that
assures us of the cleanest and safest water
to clean automobiles. Our rinse water, is
water at its purest-fresh water filtered
EXTERIOR WASH through a series of media tanks that
removes any remaining minerals from the
SEALER WAX
water and produces a spot free rinse.

TRIPLE FOAM POLISH
WHEEL BRITE
UNDERCARRIAGE WASH
TIRE SHINE



Car wash
options
EXTERIOR WASH
SEALER WAX
Seals in the Shine: A polymer-fortified

TRIPLE FOAM POLISH sealer wax (liquid form) designed by
Simoniz for automatic car washes. This
WHEEL BRITE product is lightly scented and is applied in
UNDERCARRIAGE WASH the final rinse/waxing area of the wash
process. It's an added protection (against;
DOUBLE BOND (With Teflon) acid, rain, bird droppings, tree sap, etc.)
suitable for all automobile finishes, and it
TIRE SHINE will leave a high luster on your vehicle's
finish and will not smear windshields.



Car wash
options
EXTERIOR WASH
SEALER WAX

TRIPLE FOAM POLISH
WHEEL BRITE
UNDERCARRIAGE WASH
Three foam polish (Cleaner, Sealer, and
Protector) This process is actually an
TIRE SHINE application of three different products
manufactured by Simoniz. This application is
applied after the first wash cycle. It consists
of an application of three different colors
applied in conjunction with each other, to
create an exceptional Clear Coat finish
rejuvenator. This cleans, polishes, and
prepares the surface. Best results will come
when used in conjunction with Sealer Wax or
AG Erklärungsfähige Softwaresysteme Universität Kassel — 2011-06-30 Teflon Double Bond. 7
Folie Stiftung Universität Hildesheim


Concept explanations
Concept explanations enhance
the knowledge of
communication participants.
The goal of concept explanations is
to build links between unknown and known concepts.
Variations:
Deﬁnition: “What is a bicycle?” – “A bicycle is a land vehicle with
two wheels in line. Bicycles are a form of human powered vehicle.”
Theoretical proposition: “What is force?” – “Force is Mass times
Acceleration.”
Prototypical usage of individual things or actions: “What is a
bicycle?” – “The thing, that man over there just crashed with.”
Functional mapping: “What is a bicycle?” – “A bicycle serves as a
means of transport.”

AG Erklärungsfähige Softwaresysteme Universität Kassel — 2011-06-30 Folie Stiftung Universität Hildesheim
8

Process example:
Car wash tunnel



Process example:
Car wash tunnel

Drying
Care
Wash
Wheel Wash
Prewash
Conﬁgure
options / Pay



Process example:
Car wash tunnel

Drying

Care
Wash
Wheel Wash
Prewash



Process example:
Car wash tunnel

Drying

Care
Wash
Wheel Wash
Prewash
Paid: Basic fare
1. Prewash
2. Wash
3. Drying


Process example:
Car wash tunnel

Drying

Care
Wash
Wheel Wash
Wheel Wash
Prewash
Paid: Basic fare
1. Prewash
2. Wash
3. Drying


Action explanations
Action explanations explain the activities of the system.

• Action explanations: • Negative action
“Why was this seat post explanations: “Why was no
selected?” – “For the given carrier chosen?” – “A carrier
price, only one other seat is only available for touring
post was available. But this bikes. The user did not
was too short.” choose a touring bike.”

11

Communication scenario
of explanation

Explanation
Explainer knowledge
U
I
Originator
Reasoning
knowledge



Questions: What, how and why?
For successfully using a car
wash, we need to learn:
Concepts, terms, deﬁnitions -
the ,what‘
Processes, process steps -
the ,how‘
Dependencies - the ,why‘
From:
Parents, friends, car wash
personnel, experience, …



Some explanation basics



What are explanations?



What are explanations?

Explanations are
answers to questions.



When are questions being
asked?



When are questions being
asked?
Whenever expectations
are not met.



Mining and Analysis Continuum of Explaining

MACE
Martin Atzmüller and Thomas Roth-Berghofer, Ready for the MACE? The Mining and Analysis Continuum of Explaining uncovered,
Research Report RR-10-02, Deutsches Forschungszentrum für Künstliche Intelligenz DFKI GmbH, 2010, ISSN 0946-008X.


Privacy

Explanation Goals Kinds of explanation Level of Details Presentation styles

Conceptualisation Concept explanation

MACE
Transparency Why explanation

Justiﬁcation How explanation

Relevance Purpose explanation

Learning Action explanation

Ontological knowledge
Instance knowledge Pattern knowledge Context knowledge
(Vocabulary)

Knowledge containers



Example 1: Semantic search

Roth-Berghofer, Th. and Forcher, B. Improving the understandability of semantic search explanations. Int. J. Knowledge
Engineering and Data Mining, 1(3):216–234, 2011.


Semantic search process

21



Medico project:
Annotate and Search with RadSem
Goal: Support medical doctors (esp. radiologists) in annotating and
searching for medical images (and text) with controlled vocabulary.



Medico project:
Annotate and Search with RadSem
Goal: Support medical doctors (esp. radiologists) in annotating and
searching for medical images (and text) with controlled vocabulary.

Acknowledgement: RadSem was part of the MEDICO project
(funded by BMWi in the research programme THESEUS)



Medico ontology hierarchy



Foundational Model
of Anatomy (FMA)
Developed and maintained by
Structural Informatics Group at
University of Washington
Contains more than
70.000 anatomical
entities (classes)
More than 1.5 million
relations between entities
Most comprehensive
human ontology



International Classiﬁcation of
Diseases ICD-10
Established standard of WHO, but only available in semi-
structured formats
Formalisation approach: Crawler for online version of ICD-10
generates light-weight OWL ontology



„Region of
Interest“



Example search



for RadSem

User model
Explainer Frequency classes
U
I
Semantic
Originator
FMA
Query extended with Search ICD-10
ontology concepts
Ranking based on
path length between
searched for and
found concept



for RadSem
Dijkstra
algorithm
approximating
the semantic User model
search algorithm Explainer Frequency classes
U
I
Semantic
Originator
FMA
Query extended with Search ICD-10
ontology concepts
Ranking based on
path length between
searched for and
found concept



Reconstructive explanation
Explainer

Semantic
Originator
Search



Explainer

line of
reasoning

Semantic
Originator
Search



Explainer

line of
explanation

line of
reasoning

Semantic
Originator
Search



„Bridge concepts“
Search
concept

Bridge
concepts

Annotation
concept



Predict understandability of term
Hypothesis: The degree of medical knowledge correlates
with frequency classes.
Relation between a word
and most frequent word of a text
corpus.
Frequency class
Deﬁnition: Let C be a text corpus and let f(t) denote the frequency
of a term t ∈ C. The frequency class c(t) of a term t ∈ C is deﬁned
as , where t∗ denotes the most frequently
used term in C.
Examples: c(‘Brain‘) = c16, c(‘Encephalon‘) = c22

zu Eissen, S.M. and Stein, B. (2006) ‘Intrinsic plagiarism detection’, in ECIR, pp.565–569.



Users of RadSem



Users of RadSem

Medical IT specialists

Medical doctors

Patients and Citizens



When do RadSem users
need explanations?



need explanations?
Test whether the Search Engine works correctly.
Test whether the ontologies are correctly
modelled.



need explanations?
Test whether the Search Engine works correctly.
Test whether the ontologies are correctly
modelled.

Learn about the medical domain.
Justify results in order to increase trust.
Learn how to use the engine
concerning ontologies.
Patients and Citizens


Even doctors have the need for
explanations from time to time!

Reassurance:
Activate passive knowledge.

Medical doctors



Example 2: myCBR



Open-source case-based reasoning tool
Main features (Version 3.x):
Easy import of raw data (csv-ﬁles)
GUIs for modelling knowledge-intensive similarity measures
Similarity-based retrieval functionality
myCBR Software Development Kit (SDK) for easy integration
with other systems
Built-in explanation capabilities

More information and download: http://mycbr-project.net



Case-Based Reasoning (CBR) is a
problem solving approach.
In short: New problems are
solved based on the
solutions of similar past
problems.

Basic assumption:
Similar problems have similar
solutions.

Agnar Aamodt and Enric Plaza. Case-based reasoning: Foundational issues, methodological
variations, and system approaches. AI Communications, 7(1):39–59, 1994.
37


How do you compare cars?



How do you compare cars?

Body

Model

Color

Mileage

Manufacturer



Local similarity measure



Global similarity measure
Feature match: local
similarity measures
Class similarity:
aggregate local
similarities
Weighted sum
Maximum
Minimum
Euclidian distance



Important attributes



Searching for a car



Concept explanations



➡ Concept explanations provide links
to known concepts.



➡ Concept explanations provide links
to known concepts.
➡ Action explanations provide
information about the similarity
calculation process.




MACE



Explanation Goals

Conceptualisation

MACE
Transparency

Justiﬁcation

Relevance

Learning




Explanation Goals Kinds of explanation


MACE







Explanation Goals Kinds of explanation Level of Details


MACE









MACE









MACE




(Vocabulary)




Privacy



MACE




(Vocabulary)




Take home message



Take home message
Explanation-aware Computing is a
user-centred way of looking at
software design and development.



Take home message
user-centred way of looking at U
Explainer

software design and development. I
Originator
An ExaCt system consists of
explainer and originator with
different but overlapping
knowledge bases.



Take home message
Explainer

Originator
knowledge bases.
The annotation and search tool
RadSem and myCBR are
examples of such ExaCt design.



Take home message
Explainer

Originator
knowledge bases.
The annotation and search tool
RadSem and myCBR are
examples of such ExaCt design.
More resources and slides:
http://on-explanation.net


Explanation-aware
Computing: A new
software paradigm?
Dr. Thomas Roth-Berghofer
University of Hildesheim, Germany

Erklärungsfähige
Softwaresysteme Kassel, Germany, 30 June 2011


Workshop series
„Explanation-aware Computing“
ICJAI-11 workshop, Barcelona, Spain,
July 2011, http://exact2011.workshop.hm
ECAI-10 workshop, Lisbon, Portugal, July 2010,
http://exact2010.workshop.hm
IJCAI-09 workshop, Pasadena, California, USA
July 2009, http://exact2009.workshop.hm
ECAI-08 workshop, Patras, Greece, July 2008,
AAAI-07 workshop, Vancouver, Canada, July 2007,
AAAI Fall Symposium, Washington, DC, USA, November
2005, http://exact2005.workshop.hm


http://on-explanation.net



Explanation-aware computing - A new software paradigm?

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