explanation capabilities of the open source case-based reasoning tool mycbr
DESCRIPTION
This paper describes the various explanation capabilities of the open source case-based reasoning tool myCBR. myCBR features conceptual explanations, which provide information about concepts of the application domain, backward explanations, which explain results of the retrieval process, and forward explanations, which support in the modelling of similarity measures. myCBR has been developed as a rapid prototyping tool with a general purpose interface as well as a similarity-based retrieval engine for easy integration in other applications where the explanations can be further adapted to the application’s requirements.TRANSCRIPT
Explanation Capabilities of the Open Source Case-Based Reasoning Tool myCBRThomas Roth-BerghoferSenior researcher, [email protected] German Research Centre for Artificial Intelligence DFKI GmbH
CAMBRIDGE, UK, 9 DEC 2008
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Basic explanation scenario
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„TRUST ME. I KNOW WHAT I AM DOING!“SLEDGE HAMMER
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„TRUST ME. I KNOW WHAT I AM DOING!“SLEDGE HAMMER
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What are explanations?
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What are explanations?
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What are explanations?Explanations are answers to questions.
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• 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
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Example domain: Used Cars
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Example domain: Used Cars
Body
Model
Color
Manufacturer
Mileage
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Local similarity measure
VISUAL FEEDBACK: COLOURING OF SIMILARITY VALUES
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Global similarity measure• Feature match:
local similarity measures
• Class similarity: aggregate local similarities• Weighted sum
• Maximum
• Minimum
• Euclidian distance
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Global similarity measure• Feature match:
local similarity measures
• Class similarity: aggregate local similarities• Weighted sum
• Maximum
• Minimum
• Euclidian distance
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Important attributes
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Important attributes
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Important attributes
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Searching for a car
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Searching for a car
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Searching for a car
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Searching for a car
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Searching for a car
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Questions
• about domain concepts
• about retrieval results
• during modelling and maintenance
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Question about domain concepts
• What is meant by this concept?
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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.”
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Knowledge sources
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Problem solving
knowledge
Knowledge sources
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Problem solving
knowledge
Explanation
knowledge
Knowledge sources
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Conceptual explanations
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Questions about retrieval results
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Questions about retrieval results
• How did the system come to the similarity assessment of a particular case?
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Questions about retrieval results
• How did the system come to the similarity assessment of a particular case?
Action explanations
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Questions about retrieval results
• How did the system come to the similarity assessment of a particular case?
• Which are the most similar aspects of a case? Which are the least?
Action explanations
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• 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.”
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• Conceptual Explanations provide links to known concepts.
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• Conceptual Explanations provide links to known concepts.
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• Conceptual Explanations provide links to known concepts.
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• Conceptual Explanations provide links to known concepts.
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• Conceptual Explanations provide links to known concepts.
• Action explanations provide information about the similarity calculation process.
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Most / least important aspects of a case
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Most / least important aspects of a case
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Explanation config panel
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Explanation config panel
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Questions during modelling and maintenance
• Are some problem types underrepresented in the case base?
• Is there an imbalance of cases in the case base?
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How many cases use a given attribute?
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How many cases use a given attribute?
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Distribution of cases regarding ‚manufacturer‘
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Distribution of cases regarding ‚manufacturer‘
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General system approach
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Take home messages
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• Explanations are important and need to be considered while developing a software system.
Take home messages
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• Explanations are important and need to be considered while developing a software system.
• Explanations are part of communication scenario
Take home messagesExplainer
Originator
User
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• Explanations are important and need to be considered while developing a software system.
• Explanations are part of communication scenario
• Conceptual explanations support domain understanding.
Take home messagesExplainer
Originator
User
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• Explanations are important and need to be considered while developing a software system.
• Explanations are part of communication scenario
• Conceptual explanations support domain understanding.
• Action explanations counteract encapsulation and information hiding.
Take home messagesExplainer
Originator
User
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• Explanations are important and need to be considered while developing a software system.
• Explanations are part of communication scenario
• Conceptual explanations support domain understanding.
• Action explanations counteract encapsulation and information hiding.
• Explanation manager provides access to conceptual and action explanations.
Take home messagesExplainer
Originator
User
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Thomas Roth-BerghoferSenior researcher, [email protected] 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
Samstag, 18. Juli 2009
Invitation to participate
• ExaCt mailing list:http://groups.yahoo.com/group/explanation-research/
• ExaCt 2009 @ IJCAI 2009 http://exact2009.workshop.hm
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Publications of ExaCt research group
2008
[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.
2007
[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.
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.
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