Explanation-aware computing - A new software paradigm?

ErklärungsfähigeSoftwaresysteme

Dr. Thomas Roth-BerghoferUniversity of Hildesheim, Germany

Kassel, Germany, 30 June 2011

Explanation-aware Computing:A new software paradigm?

Freitag, 1. Juli 2011

Universität Kassel — 2011-06-30AG Erklärungsfähige Softwaresysteme Stiftung Universität HildesheimFolie

Thomas Roth-Berghofer

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Senior researcherGerman Research Center for Artificial Intelligence (DFKI) GmbH

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

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industry


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Visiting ProfessorUniversity of Hildesheim

Lecturer at University of Kaiserslautern

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Visiting ProfessorUniversity of Hildesheim

Lecturer at University of Kaiserslautern

industry


research

academiaStarting in September 2011:





Explanation-aware Computing:A new software paradigm?



Characteristics of ,good‘ software

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Self explaining Helpful

Easy to use With nice user interface




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Self explaining HelpfulUser centred





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All in all: Great user experience expected

Self explaining HelpfulUser centred




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

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Car Wash


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Car wash options

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Car wash options

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EXTERIOR WASH

SEALER WAX

TRIPLE FOAM POLISH

WHEEL BRITE

UNDERCARRIAGE WASH

DOUBLE BOND (With Teflon)

TIRE SHINE

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Car wash options

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EXTERIOR WASH

SEALER WAX

TRIPLE FOAM POLISH

WHEEL BRITE

UNDERCARRIAGE WASH


TIRE SHINE

Our exterior wash process uses only the finest quality materials formulated especially 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 through a series of media tanks that

removes any remaining minerals from the water and produces a spot free rinse.

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Car wash options

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EXTERIOR WASH

SEALER WAX

TRIPLE FOAM POLISH

WHEEL BRITE

UNDERCARRIAGE WASH


TIRE SHINE

Seals in the Shine: A polymer-fortified sealer wax (liquid form) designed by

Simoniz for automatic car washes. This product is lightly scented and is applied in

the final rinse/waxing area of the wash process. It's an added protection (against; acid, rain, bird droppings, tree sap, etc.) suitable for all automobile finishes, and it will leave a high luster on your vehicle's finish and will not smear windshields.

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Car wash options

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EXTERIOR WASH

SEALER WAX

TRIPLE FOAM POLISH

WHEEL BRITE

UNDERCARRIAGE WASH


TIRE SHINE

Three foam polish (Cleaner, Sealer, and Protector) This process is actually an 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

Teflon Double Bond.

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Car wash options

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EXTERIOR WASH

SEALER WAX

TRIPLE FOAM POLISH

WHEEL BRITE

UNDERCARRIAGE WASH


TIRE SHINE

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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.”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.”

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Concept explanations enhance the knowledge of

communication participants.



Process example: Car wash tunnel

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Drying

Care

Wash

Wheel Wash

Prewash

Configure options / Pay




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Wash

Wheel Wash

Drying

Prewash

Care




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Wash

Wheel Wash

Drying

Prewash

Care

Paid: Basic fare1. Prewash2. Wash3. Drying




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Wash

Wheel Wash

Drying

Prewash

Care

PrewashPaid: Basic fare

1. Prewash2. Wash3. Drying




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Wash

Wheel Wash

Drying

Prewash

Care

Wheel Wash






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Wash

Wheel Wash

Drying

Prewash

Care

Wash

Wheel Wash






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Wash

Wheel Wash

Drying

Prewash

Care

Wash

Wheel Wash

Prewash

Care





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Wash

Wheel Wash

Drying

Prewash

Care

Wash

Wheel Wash

Drying

Prewash

Care




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

• Negative action explanations: “Why was no carrier chosen?” – “A carrier is only available for touring bikes. The user did not choose a touring bike.”

Action explanations

Action explanations explain the activities of the system.

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Communication scenario of explanation

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Explainer

Originator

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Explanationknowledge

Reasoningknowledge



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, …

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Some explanation basics

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What are explanations?

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What are explanations?

Explanations are answers to questions.

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When are questions being asked?

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When are questions being asked?

Whenever expectations are not met.

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MACEUniversität Kassel — 2011-06-30AG Erklärungsfähige Softwaresysteme Stiftung Universität HildesheimFolie

Mining and Analysis Continuum of Explaining

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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.




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Purpose explanation

How explanation

Why explanation

Concept explanation

Action explanation

Kinds of explanation

Knowledge containers

Ontological knowledge (Vocabulary) Pattern knowledgeInstance knowledge Context knowledge

Transparency

Justification

Relevance

Learning

Explanation Goals

Conceptualisation

Level of Details Presentation styles

Privacy




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.

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Semantic search process

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Medico project: Annotate and Search with RadSem

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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

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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)


Universität Kassel — 2011-06-30AG Erklärungsfähige Softwaresysteme Stiftung Universität HildesheimFolie 23

Medico ontology hierarchy



Foundational Model of Anatomy (FMA)

Developed and maintained byStructural 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


http://sig.biostr.washington.edu/index.html

http://sig.biostr.washington.edu/index.html

http://www.washington.edu/

http://www.washington.edu/


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

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Communication scenario for RadSem

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Explainer

Originator

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Semantic SearchQuery extended with

ontology concepts

Ranking based on path length between searched for and found concept

User model

Frequency classes

FMA

ICD-10



Communication scenario for RadSem

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Explainer

Originator

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Semantic SearchQuery extended with

ontology concepts

Ranking based on path length between searched for and found concept

Dijkstra algorithm approximating the semantic search algorithm

User model

Frequency classes

FMA

ICD-10



Reconstructive explanation

U

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Explainer

OriginatorSemantic Search




U

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Explainer


line of reasoning




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Explainer


line of reasoning

line of explanation



„Bridge concepts“

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Searchconcept

Annotationconcept

Bridgeconcepts



Predict understandability of term

Hypothesis: The degree of medical knowledge correlates with frequency classes.

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

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

Relation between a word and most frequent word of a text

corpus.



Users of RadSem

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Users of RadSem

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Medical IT specialists

Medical doctors

Patients and Citizens



When do RadSem usersneed explanations?

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Test whether the Search Engine works correctly.

Test whether the ontologies are correctly modelled.





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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!

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Reassurance: Activate passive knowledge.

Medical doctors



Example 2: myCBR

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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

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http://mycbr-project.net

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.

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How do you compare cars?

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How do you compare cars?

Body

Model

Color

Manufacturer

Mileage

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Local similarity measure

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Global similarity measureFeature match: local similarity measures

Class similarity: aggregate local similarities

Weighted sumMaximum

MinimumEuclidian distance

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Important attributes

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Searching for a car

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Concept explanations

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➡Concept explanations provide links to known concepts.

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➡Concept explanations provide links to known concepts.

➡Action explanations provide information about the similarity calculation process.

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Transparency

Justification

Relevance

Learning

Explanation Goals

Conceptualisation





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Purpose explanation

How explanation

Why explanation

Concept explanation

Action explanation


Transparency

Justification

Relevance

Learning

Explanation Goals

Conceptualisation





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Purpose explanation

How explanation

Why explanation

Concept explanation

Action explanation


Transparency

Justification

Relevance

Learning

Explanation Goals

Conceptualisation

Level of Details





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Purpose explanation

How explanation

Why explanation

Concept explanation

Action explanation


Transparency

Justification

Relevance

Learning

Explanation Goals

Conceptualisation






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Purpose explanation

How explanation

Why explanation

Concept explanation

Action explanation



Transparency

Justification

Relevance

Learning

Explanation Goals

Conceptualisation






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Purpose explanation

How explanation

Why explanation

Concept explanation

Action explanation




Transparency

Justification

Relevance

Learning

Explanation Goals

Conceptualisation






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Purpose explanation

How explanation

Why explanation

Concept explanation

Action explanation




Transparency

Justification

Relevance

Learning

Explanation Goals

Conceptualisation


Privacy




Take home message

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Take home message

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

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Take home message


An ExaCt system consists of explainer and originator with different but overlapping knowledge bases.

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U

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Explainer

Originator



Take home message



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

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U

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Explainer

Originator



Take home message



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

More resources and slides:http://on-explanation.net

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U

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Explainer

Originator


http://on-explanation.net





Explanation-aware Computing: A new

software paradigm?



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, http://exact2008.workshop.hm

AAAI-07 workshop, Vancouver, Canada, July 2007, http://exact2007.workshop.hm

AAAI Fall Symposium, Washington, DC, USA, November 2005, http://exact2005.workshop.hm

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http://exact2011.workshop.hm














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Explanation-aware computing - A new software paradigm?

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