[Sometimes called Case-Based Reasoning or CBR]
"At the highest level of generality, a general CBR cycle may be described by the following four processes: 1. RETRIEVE the most similar case or cases. 2. REUSE the information and knowledge in that case to solve the problem. 3. REVISE the proposed solution. 4. RETAIN the parts of this experience likely to be useful for future problem solving "– from Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. By A. Aamodt and E. Plaza. (1994)
This paper proposes a theoretical analysis of one approach to the eXplainable AI (XAI) problem, using post-hoc explanation-by-example, that relies on the twinning of artificial neural networks (ANNs) with case-based reasoning (CBR) systems; so-called ANN-CBR twins. It surveys these systems to advance a new theoretical interpretation of previous work and define a road map for CBR's further role in XAI. A systematic survey of 1102 papers was conducted to identify a fragmented literature on this topic and trace its influence to more recent work involving deep neural networks (DNNs). The twin-system approach is advanced as one possible coherent, generic solution to the XAI problem. The paper concludes by road-mapping future directions for this XAI solution, considering (i) further tests of feature-weighting techniques, (ii) how explanatory cases might be deployed (e.g., in counterfactuals, a fortori cases), and (iii) the unwelcome, much-ignored issue of user evaluation.
In this paper we propose the application of techniques from the field of creativity research to machine learned models within the domain of games. This application allows for the creation of new, distinct models without additional training data. The techniques in question are combinatorial creativity techniques, defined as techniques that combine two sets of input to create novel output sets. We present a survey of prior work in this area and a case study applying some of these techniques to pre-trained machine learned models of game level design.
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Case-based reasoning addresses new problems by remembering and adapting solutions previously used to solve similar problems. Pulled by the increasing number of applications and pushed by a growing interest in memory intensive techniques, research on case-based reasoning appears to be gaining momentum. In this article, we briefly summarize recent developments in research on case-based reasoning based partly on the recent Twenty Fourth International Conference on Case-Based Reasoning.
I'm pleased to announce that the IBM Watson IoT Platform now has the ability to add Internet of Things (IoT) data, to Blockchain transactions. Leveraging blockchain for your IoT data opens up new ways of automating business processes amongst your partners without setting up an expensive centralized IT infrastructure. This gives you the potential to improve business networks, reduce costs, increase trust and open up new markets. A solution brief provides an overview of the capabilities and use cases being announced. The new features within the Watson IoT Platform are available through two new Lab Services Offerings.
We get asked a lot of questions about how to start building with Watson, so we decided to compile our top 4 Frequently Asked Questions. You can use this as a guide to learn more about the technology, receive inspiration from use cases, get valuable resources, and ultimately begin building with the technology. Cognitive technology's strength lies in its ability to draw insights from unstructured data sets. Structured data is found in a spreadsheet, whereas unstructured data is text such as tweets, medical journals, etc. Today 80% of data is unstructured, so tools such as cognitive computing are becoming more important in helping humans understand what's inside that data.
In cooperation with the Association for the Advancement of Artificial Intelligence (AAAI), the Twenty-Second International Conference on Case-Based Reasoning (ICCBR), the premier international meeting on research and applications in case-based reasoning (CBR), was held from Monday September 29 to Wednesday October 1, 2014, in Cork, Ireland. ICCBR is the annual meeting of the CBR community and the leading conference on this topic. Started in 1993 as the European Conference on CBR and 1995 as ICCBR, the two conferences alternated biennially until their merger in 2010.
This paper surveys significant research on the problem of rendering expressive music by means of AI techniques with an emphasis on Case-Based Reasoning. Following a brief overview discussing why we prefer listening to expressive music instead of lifeless synthesized music, we examine a representative selection of well-known approaches to expressive computer music performance with an emphasis on AI-related approaches. In the main part of the paper we focus on the existing CBR approaches to the problem of synthesizing expressive music, and particularly on TempoExpress, a case-based reasoning system developed at our Institute, for applying musically acceptable tempo transformations to monophonic audio recordings of musical performances. Finally we briefly describe an ongoing extension of our previous work consisting on complementing audio information with information of the gestures of the musician. Music is played through our bodies, therefore capturing the gesture of the performer is a fundamental aspect that has to be taken into account in future expressive music renderings. This paper is based on the “2011 Robert S. Engelmore Memorial Lecture” given by the first author at AAAI/IAAI 2011.
The European Conference on Case-Based Reasoning (CBR) in 2008 marked 15 years of international and European CBR conferences where almost seven hundred research papers were published. In this report we review the research themes covered in these papers and identify the topics that are active at the moment. The main mechanism for this analysis is a clustering of the research papers based on both co-citation links and text similarity. It is interesting to note that the core set of papers has attracted citations from almost three thousand papers outside the conference collection so it is clear that the CBR conferences are a sub-part of a much larger whole. It is remarkable that the research themes revealed by this analysis do not map directly to the sub-topics of CBR that might appear in a textbook. Instead they reflect the applications-oriented focus of CBR research, and cover the promising application areas and research challenges that are faced.
This position paper presents open issues for using self-models to guide introspective learning, focusing on five key types of areas to explore: (1) broadening the range of learning focuses and the range of learning tools which may be brought to bear, (2) learning for self-understanding as well as self-repair, (3) making model-based approaches more sensitive to processing characteristics, instead of only outcomes, (4) making model application more flexible and robust, and (5) increasing support for self-explanation and user interaction with the metalevel.