Explanation Capabilities of the Open Source Case-Based Reasoning Tool myCBR

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Explanation Capabilities of the Open Source Case-Based Reasoning Tool myCBRThomas Roth-BerghoferSenior researcher, trb@dfki.de German Research Centre for Artificial Intelligence DFKI GmbH

CAMBRIDGE, UK, 9 DEC 2008

Samstag, 18. Juli 2009

Basic explanation scenario

„TRUST ME. I KNOW WHAT I AM DOING!“SLEDGE HAMMER

What are explanations?

What are explanations?Explanations are answers to questions.

• Plug-in for popular ontology editor Protégé (Version 3.x)

• Extensions provided by myCBR (Version 2.6.1):

• Easy import of raw data (csv-files)

• GUIs for modelling knowledge-intensive similarity measures

• Similarity-based retrieval functionality

• Export of domain model and similarity measures in XML format

• Stand-alone retrieval engine for separate use or integration with other systems

• Built-in explanation capabilities

MORE INFORMATION AND DOWNLOAD: HTTP://MYCBR-PROJECT.NET

Example domain: Used Cars

Manufacturer

Mileage

Local similarity measure

VISUAL FEEDBACK: COLOURING OF SIMILARITY VALUES

Global similarity measure• Feature match:

local similarity measures

• Class similarity: aggregate local similarities• Weighted sum

• Maximum

• Minimum

• Euclidian distance

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

Questions

• about domain concepts

• about retrieval results

• during modelling and maintenance

Question about domain concepts

• What is meant by this concept?

Conceptual Explanations

• The goal of conceptual 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.”

Knowledge sources

Problem solving

knowledge

Knowledge sources

Problem solving

knowledge

Explanation

knowledge

Knowledge sources

Conceptual explanations

Questions about retrieval results

• How did the system come to the similarity assessment of a particular case?

Action explanations

• Which are the most similar aspects of a case? Which are the least?

Action explanations

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

Action explanations

• Action explanations explain the activities of the respective system.

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

• Conceptual Explanations provide links to known concepts.

• Action explanations provide information about the similarity calculation process.

Most / least important aspects of a case

Explanation config panel

Questions during modelling and maintenance

• Are some problem types underrepresented in the case base?

• Is there an imbalance of cases in the case base?

How many cases use a given attribute?

Distribution of cases regarding ‚manufacturer‘

General system approach

Take home messages

• Explanations are important and need to be considered while developing a software system.

Take home messages

• Explanations are part of communication scenario

Take home messagesExplainer

Originator

• Conceptual explanations support domain understanding.

Originator

• Action explanations counteract encapsulation and information hiding.

Originator

• Action explanations counteract encapsulation and information hiding.

• Explanation manager provides access to conceptual and action explanations.

Originator

Thomas Roth-BerghoferSenior researcher, trb@dfki.de German Research Centre for Artificial Intelligence DFKI GmbH

Thank you!

Explanation Capabilities of the Open Source Case-Based Reasoning Tool myCBR

CAMBRIDGE, UK, 9 DEC 2008

Invitation to participate

• ExaCt mailing list:http://groups.yahoo.com/group/explanation-research/

• ExaCt 2009 @ IJCAI 2009 http://exact2009.workshop.hm

Publications of ExaCt research group

[Bahls, 2008] Bahls, D. (2008). Explanation support for the case-based reasoning tool myCBR. Project thesis, University of Kaiserslautern.

[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.

[Roth-Berghofer and Bahls, 2008] Roth-Berghofer, T. R. 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.

[Roth-Berghofer and Mittag, 2008] Roth-Berghofer, T. R. and Mittag, F. (2008). ReduxExp: A justification-based explanation-support server. Proceedings of AI-2008. the twenty-eighth SGAI international conference on artificial intelligence. In Petridis, M., Coenen, F., and Bramer, M., editors, Research and Development in Intelligent Systems XXV, London, UK. Springer Verlag.

[Roth-Berghofer and Richter, 2008a] Roth-Berghofer, T. R. and Richter, M. M., editors (2008a). Künstliche Intelligenz—Topic: Explanation, volume 22, Bremen. BöttcherIT Verlag.

[Roth-Berghofer and Richter, 2008b] Roth-Berghofer, T. R. and Richter, M. M. (2008b). On explanation. Künstliche Intelligenz, 22(2):5–7.

[Bahls and Roth-Berghofer, 2007] Bahls, D. and Roth-Berghofer, T. (2007). Explanation support for the case-based reasoning tool myCBR. In Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence. July 22–26, 2007, Vancouver, British Columbia, Canada., pages 1844–1845. The AAAI Press, Menlo Park, California.

[Eppert, 2007] Eppert, M. (2007). Generating provenance explanations for the gnowsis rebirth machine - a first pass. Pro ject thesis, University of Kaiserslautern.

2007 (contd.)

[Roth-Berghofer et al., 2007] Roth-Berghofer, T. R., Schulz, S., and Leake, D. B., editors (2007). Proceedings of the AAAI-07 workshop on Explanation-aware Computing ExaCt2007. AAAI Press. Technical Report WS-07-06.

[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.

[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.

[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.

Explanation Capabilities of the Open Source Case-Based Reasoning Tool myCBR

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