Explanation Aware Design And Computing 2009 09 11

Explanation-aware System
Design and Computing
Thomas Roth-Berghofer
Senior researcher, trb@dfki.de
German Research Center for Artiﬁcial Intelligence
DFKI GmbH, Kaiserslautern, Germany


Freitag, 11. September 2009 1

Traditional view on
software systems
User

U
I Software System



„Traditional“ behaviour
of software systems
„Trust me. I
know what I am
doing!“

SLEDGE HAMMER Thomas Roth-Berghofer


Overview

• Explanation-aware view on software
design: communication scenario
• Aspects of explanation-aware design
• Example: coTag — Code tagging and
similarity-based retrieval



Communication
participants
User Software System

Explainer
U
I
Originator



The user
communicates by way
of a user interface Explainer
U
(UI) with the whole I
software system and Originator

is the recipient of
explanations.



Originator
Explainer
U
I

The originator is the tool
Originator
the user works with to
perform tasks and solve
problems.



Explainer
Explainer The explainer can be seen
U as another tool that helps
I
understanding how the
originator works and
what knowledge the
originator uses.

Originator


Explanation knowledge

• concept explanations
Explainer • templates
•…

Originator



Problem solving
knowledge

Explainer

• results
Originator • concepts
• workﬂows
•…


Reasoning information

Explainer

• intermediate results
• context snapshots
• …

Originator



What are
explanations?



What are
explanations?
Explanations are
answers to
questions.


Cognitive aspects of
explanations
• „Explanations are the most common method used
by humans to support decision making.“
(Roger Schank, 1986)

• Main purpose:
•Explain a solution.
•How was the solution derived?
•How does a system work?
•How to handle a system
•Explain failures.


Computational aspects
of explanations
• Backward explanations:
•Explain result and how it was obtained.

• Forward explanations:
• Explain (indirectly) by
showing different ways to
further optimise a given result.
• Open up possibilities for
exploratory use.



EXAMPLE:

Code-tagging and similarity-
based retrieval with myCBR
Roth-Berghofer, Th. and Bahls, D. (2008). Code tagging and retrieval with
myCBR. In Petridis, M., Coenen, F., and Bramer, M., editors, Research and
Development in Intelligent Systems XXV, London, UK. Springer Verlag.


Programmer‘s dilemma



Typical questions of
programmers

• Where is the code fragment I used to solve a
similar problem with in the past?
• Is this piece of code still available?
• Is it worth the effort to search for it?
• If so, what would be the right search term?



Personalised approach
• Personal
vocabulary: tags
• Linking tags



Linking tags
GridBag

Similar!

PatternLayout



Personalised approach
• Personal
vocabulary: tags
• Linking tags
• Work context
• Social dimension:
tag exchange
• Similarity-based
retrieval



Case-Based
Reasoning cycle

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



Design decisions /
constraints
• Integration in IDE eclipse
• Storage of code snippets and tags separately from
code

• Queries = Search text plus work context
• Community repository for experience exchange



Code snippet & context
Java code snippet Work context
• java.net.URL
• java.net.URLConnection
• java.io.InputStream
• java.lang.StringBuffer
• java.io.BufferedReader
• java.lang.String
• java.lang.Exception


Case structure
Attribute Value type category
Tags String (multiple) Problem description
Context items String (multiple) Problem description
Code snippet String Solution
Document type String Provenance
Project name String Provenance
File path String Provenance
Author ID String Provenance
Creation date Long Provenance
Rating Float Maintenance
Rating count Integer Maintenance



Case structure Set by user
Set by coTag

Attribute Value type category
Tags String (multiple) Problem description
Context items String (multiple) Problem description
Code snippet String Solution
Document type String Provenance
Project name String Provenance
File path String Provenance
Author ID String Provenance
Creation date Long Provenance
Rating Float Maintenance
Rating count Integer Maintenance



Acquiring case



Query view

• Search for tags: init,
logging conﬁg
• Include context
=> regard currently
selected code



Retrieval

• Result for: init, logging,
conﬁg
• Ranked list of code
snippets



Presentation of cases



Situations in which
explanations play a role

• Instructing explanations:
• Novice users want to know about how tagging and (similarity-based)
retrieval works.

• Convincing explanations:
• Regular users want to check when the retrieval does not meet their
expectations.

• Improving explanations
• Regular users want to correct coTag‘s behaviour.



Explanation of matching

• Search terms:
• init, logging, conﬁg
• Case tags:
• init, Logger



Graphical explanation of
trigram matching

• Syntactical similarity
• Typos
• Stemming



Similarity customisation
• Tag similarities:
unsimilar 0%
partly similar 25%
similar 50%
very similar 75%
identical 100%
• Updates personal and
community similarity
measure



Three levels of similarity
calculation

Personal

Imported

Trigram



Customised (personal)
and imported similarity



Client-side architecture



Tag and exchange code
snippets



Summary
• Re-ﬁnding information is a quite
typical task in knowledge-work.
• Tagging is a helpful and well-
known technique.
• Similarity-based retrieval can
improve searches.
• Explanation-aware design and
development of applications helps
dealing with increased complexity
of similarity-based retrieval.



Explaining
Semantic Search Results of
Medical Images in MEDICO

Forcher, B., Möller, M., Sintek, M., and Roth-Berghofer, Th. Explanation of semantic search
results of medical images in Medico. In Th. Roth-Berghofer, N. Tintarev, and D. B. Leake,
editors, Workshop 10@IJCAI-09: Explanation-aware Computing (ExaCt 2009), pages 13–24, 2009.



Goal of Medico Project
Development of
• intelligent
• robust and
• scalable
semantic search engine
for medical images



Reconstructive explanations
Explainer

Originator
line of explanation
line ofThomas Roth-Berghofer
reasoning


RadSem

• Exploration interface with concept explanations
support domain understanding.
• Justiﬁcation interface provides action explanations,
which counteract encapsulation and information hiding.


Good explanations
• Relevant OPEN QUESTION:
• Innovative OPERATIONALISATION
• Convincing OF THOSE CRITERIA
• Short and easy to overlook
• Provide different perspectives
and follow-up questions

Kinds Explainer
Goals
• Concept • Transparency
• Why • Justiﬁcation
• How • Relevance
• Action • Conceptualisation
explanations Originator
• Learning



Kinds of explanations

• Concept explanations
• Action explanations
• Why- and How-explanations



Concept Explanations
• 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.”
• Functional mapping: “What is a bicycle?” – “A bicycle serves
as a means of transport.”
• Prototypical usage of individual things or actions:
“What is a bicycle?” – “The thing, that man over there just crashed
with.”
• …


Action explanations
• Action explanations explain the activities of
the respective system (originator).

Action explanations:
“Why was this seat post selected?” –
“For the given price, only one other seat
post was available. But this was too
short.

• In RadSem: Reconstructive explanations based
on search concepts and found concepts.


Why-explanations

• Why-explanations provide causes or justiﬁcations for
facts or events.

• Examples:
• Justiﬁcation: “Why does the universe expand?” – “Because we
can observe a red shift of the light emitted by other galaxies.”
• Cause: “Because the whole matter was concentrated at one
point of the universe and because the whole matter moves away
from each other



Explanation goals
Transparency How did the system reach an answer?

Justiﬁcation Why is the answer a good answer?

Relevance Why is the question relevant?

Conceptualisation What is the meaning of a concept?

Learning Teach the user about the given domain.

Sørmo, F., Cassens, J., Aamodt, A.: Explanation in
Case-Based Reasoning – Perspectives and Goals, 2005.


When are explanations
good explanations?
• Short and easy to overlook
• Innovative
• Relevant
• Convincing
• Different perspectives and
follow-up questions
• Natural
W. R. Swartout and J. D. Moore. Explanation in second generation expert systems.
In J. David, J. Krivine, and R. Simmons, editors, Second Generation Expert
Systems, pages 543–585, Berlin, 1993. Springer Verlag.


Take home messages
• The ability to explain reasoning processes and results
can substantially affect the usability and acceptance of
a software system.

• Basic explanation scenario Explainer

helps identifying communication
U
I

partners and knowledge bases.
Originator

• Explanation goals and kinds further help structuring
knowledge acquisition and use in software design and
computing.



Thank
you!

Explanation-aware System
Design and Computing
Senior researcher, trb@dfki.de



Publications of ExaCt
research group
2009 [Roth-Berghofer and Bahls, 2008] Roth-Berghofer, T. R. and Bahls, D.
[Roth-Berghofer, Tintarev, Leake, 2009] Roth-Berghofer, Th., Tintarev, N., (2008). Code tagging and retrieval with myCBR. In Petridis, M., Coenen, F.,
and Leake, D.B., editors. Proceedings of the IJCAI-09 workshop on and Bramer, M., editors, Research and Development in Intelligent Systems
Explanation-aware Computing (ExaCt 2009), July 2009. XXV, London, UK. Springer Verlag.
[Adrian et al., 2009] Adrian, B., Forcher, B., Roth-Berghofer, Th., and [Roth-Berghofer and Mittag, 2008] Roth-Berghofer, T. R. and Mittag, F.
Dengel, A. Explaining ontology-based information extraction in the (2008). ReduxExp: A justification-based explanation-support server.
NEPOMUK semantic desktop. In Thomas R. Roth-Berghofer, Nava Tintarev, Proceedings of AI-2008. the twenty-eighth SGAI international conference
and David B. Leake, editors, Workshop 10@IJCAI-09: Explanation-aware on artificial intelligence. In Petridis, M., Coenen, F., and Bramer, M., editors,
Computing (ExaCt 2009), pages 94–101, 2009. Research and Development in Intelligent Systems XXV, London, UK.
Springer Verlag.
[Forcher et al., 2009] Forcher, B., Möller, M., Sintek, M., and Roth-
Berghofer, Th. Explanation of semantic search results of medical images in [Roth-Berghofer and Richter, 2008a] Roth-Berghofer, T. R. and Richter, M.
medico. In Thomas R. Roth-Berghofer, Nava Tintarev, and David B. Leake, M., editors (2008a). Künstliche Intelligenz—Topic: Explanation, volume 22,
editors, Workshop 10@IJCAI-09: Explanation-aware Computing (ExaCt Bremen. BöttcherIT Verlag.
2009), pages 13–24, 2009. [Roth-Berghofer and Richter, 2008b] Roth-Berghofer, T. R. and Richter, M.
[Stahl, Roth-Berghofer, 2009] Stahl, A. and Roth-Berghofer, Th. Rapid M. (2008b). On explanation. Künstliche Intelligenz, 22(2):5–7.
Prototyping of CBR applications with the Open Source Tool myCBR. 2007
Künstliche Intelligenz, 23(1):34–37, March 2009. [Bahls and Roth-Berghofer, 2007] Bahls, D. and Roth-Berghofer, T. (2007).
2008 Explanation support for the case-based reasoning tool myCBR. In
[Roth-Berghofer et al., 2008] Roth-Berghofer, Th., Schulz, S., Bahls, D., and Proceedings of the Twenty-Second AAAI Conference on Artificial
Leake, D.B., editors. Proceedings of the ECAI-08 workshop on Intelligence. July 22–26, 2007, Vancouver, British Columbia, Canada.,
Explanation-aware Computing ExaCt2008. University of Patras, July 2008. pages 1844–1845. The AAAI Press, Menlo Park, California.
http://ceur- ws.org/Vol- 391. [Eppert, 2007] Eppert, M. (2007). Generating provenance explanations for
[Forcher, Adrian, Roth-Berghofer, 2008] Forcher, B., Adrian, B., and Roth- the gnowsis rebirth machine - a first pass. Pro ject thesis, University of
Berghofer, Th.. Explanation styles in iDocument. ExaCt 2008, ECAI-08 Kaiserslautern.
Workshop. [Roth-Berghofer et al., 2007] Roth-Berghofer, T. R., Schulz, S., and Leake,
[Bahls, 2008] Bahls, D. (2008). Explanation support for the case-based D. B., editors (2007). Proceedings of the AAAI-07 workshop on
reasoning tool myCBR. Project thesis, University of Kaiserslautern. Explanation-aware Computing ExaCt2007. AAAI Press. Technical Report
WS-07-06.
[Forcher et al., 2008] Forcher, B., Adrian, B., and Roth-Berghofer, T. (2008).
Explanations in the information extraction system iDocument. Künstliche
Intelligenz, 22(2).
[Mittag, 2008] Mittag, F. (2008). ReduxExp: A justification-based
explanation-support server. Project thesis, University of Kaiserslautern.


Publications of ExaCt
research group
2006
[Richter et al., 2006] Richter, M. M., Roth-Berghofer, T., and Schulz, S.,
editors (2006). Explanation-aware Computing, volume 25. SAP - Slovak
Academic Press Ltd., Bratislava.
2005
[Roth-Berghofer et al., 2005a] Roth-Berghofer, T., Cassens, J., and Sørmo,
F. (2005a). Goals and kinds of explanations in case-based reasoning. In
Althoff, K.-D., Dengel, A., Bergmann, R., Nick, M., and Roth-Berghofer, T.,
editors, WM 2005: Professional Knowledge Management, pages 264–268,
Kaiserslautern, Germany. DFKI GmbH.
[Roth-Berghofer and Cassens, 2005] Roth-Berghofer, T. R. and Cassens, J.
(2005). Mapping goals and kinds of explanations to the knowledge
containers of case-based reasoning systems. In Muñoz-Avila, H. and Ricci,
F., editors, Case-Based Reasoning Research and Developmen, pages 451–
464, Heidelberg. Springer Verlag.
[Roth-Berghofer et al., 2005b] Roth-Berghofer, T. R., Schulz, S., and
Woody, A., editors (2005b). Proceedings of the AAAI Fal l Symposium on
Explanation-aware Computing ExaCt2005. AAAI Press. Technical Report
FS-05-04.
2004
[Roth-Berghofer, 2004] Roth-Berghofer, T. R. (2004). Explanations and
Case-Based Reasoning: Foundational issues. In Funk, P. and González-
Calero, P. A., editors, Advances in Case-Based Reasoning, pages 389–403.
Springer-Verlag.
[Memmel, Roth-Berghofer, 2004] Memmel, M. and Roth-Berghofer, Th.
Explanation and e-learning: A ﬁrst pass. In Klaus-Peter F¨ahnrich, Klaus P.
Jantke, and Wolfgang S. Wittig, editors, Von e-Learning bis e-Payment.
Das Internet als sicherer Marktplatz, pages 255–263. Akademische
Verlagsgesellschaft Aka, 2004.


Invitation to participate
HTTP://ON-EXPLANATION.NET

• Manifesto
• Mailing list
• Workshop series
• …


Explanation Aware Design And Computing 2009 09 11

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