"At the highest level of generality, a general CBR cycle may be described by the following four processes:
– Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Agnar Aamodt & Enric Plaza. AI Communications. IOS Press, Vol. 7: 1, pp. 39-59.
The notion of twin systems is proposed to address the eXplainable AI (XAI) problem, where an uninterpretable black-box system is mapped to a white-box 'twin' that is more interpretable. In this short paper, we overview very recent work that advances a generic solution to the XAI problem, the so called twin system approach. The most popular twinning in the literature is that between an Artificial Neural Networks (ANN ) as a black box and Case Based Reasoning (CBR) system as a white-box, where the latter acts as an interpretable proxy for the former. We outline how recent work reviving this idea has applied it to deep learning methods. Furthermore, we detail the many fruitful directions in which this work may be taken; such as, determining the most (i) accurate feature-weighting methods to be used, (ii) appropriate deployments for explanatory cases, (iii) useful cases of explanatory value to users.
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.
Doctors often rely on their past experience in order to diagnose patients. For a doctor with enough experience, almost every patient would have similarities to key cases seen in the past, and each new patient could be viewed as a mixture of these key past cases. Because doctors often tend to reason this way, an efficient computationally aided diagnostic tool that thinks in the same way might be helpful in locating key past cases of interest that could assist with diagnosis. This article develops a novel mathematical model to mimic the type of logical thinking that physicians use when considering past cases. The proposed model can also provide physicians with explanations that would be similar to the way they would naturally reason about cases. The proposed method is designed to yield predictive accuracy, computational efficiency, and insight into medical data; the key element is the insight into medical data, in some sense we are automating a complicated process that physicians might perform manually. We finally implemented the result of this work on two publicly available healthcare datasets, for heart disease prediction and breast cancer prediction.
This paper discusses the state of the art in CBR ontologies from the perspective of one developing an improved system for case-based legal reasoning. The paper proposes three specific roles for a CBR ontology and illustrates them in the context of the intended output of the new system: a legal classroom discussion of how to decide a case featuring hypothetical reasoning and abstract analogies. The paper distills the ontological requirements for modeling the example's case-based arguments and assesses whether current research can meet those requirements. The concrete example helps to focus on and define goals for improving CBR ontologies.
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.
It was held on Sunday, 28 July 2002. The workshop papers are available as a technical report from AAAI Press. After a welcome and introductions, David Martin of SRI International started the day's presentations with an overview of the The objective of the language is to enable automated software agents to easily accomplish real-world planning tasks by discovering related services, selecting the most appropriate among them, composing them into effective plans, and invoking them to execute these plans and accomplish their tasks. The ServiceModel specification is aimed at supporting service invocation, composition, and monitoring and consists of a workflow model describing how the service is accomplished in terms of atomic and composite processes and their data and control dependencies. Finally, the ServiceGrounding specification specifies the implementation-specific details of service invocation, related to protocols, message formatting, and type serialization.
Researchers and technology developers from the National Aeronautics and Space Administration (NASA), other government agencies, academia, and industry recently met in Pasadena, California, to take stock of past and current work and future challenges in the application of AI to highly autonomous systems. In our lifetime, through the eyes of simple robots, grand vistas on other worlds have been unveiled for the first time. Enigmatic questions compel us to go further, to touch these distant landscapes and learn the secrets of the solar system. However, in trying, we find our reach wanting, limited by the link to Earth on which our probes depend. We are learning that to explore further, these probes must go alone, and to go alone, they must become much more intelligent.
Originally founded in 1987 as a conference to promote and advance AI within the state of Florida, over the years, FLAIRS has attracted national and international participation--56 percent of this year's papers had international authors. After a period of eight years, the Fifteenth International Conference of the Florida Artificial Intelligence Research Society (FLAIRS 2002) returned to the emerald coast of Pensacola Beach, Florida. John Kolen (UWF-IHMC) was the conference general chair, and Susan Haller (University of Wisconsin at Parkside) and Gene Simmons (University of South Alabama) were the program cochairs. FLAIRS is a general conference for reporting AI research, and the 104 papers presented at FLAIRS-2002 covered a broad spectrum of research areas. The conference consisted of 3 parallel sessions of 21 tracks, including 14 special tracks highlighting specific themes.
In this article, we first survey the three major types of computer music systems based on AI techniques: (1) compositional, (2) improvisational, and (3) performance systems. Representative examples of each type are briefly described. Then, we look in more detail at the problem of endowing the resulting performances with the expressiveness that characterizes human-generated music. This is one of the most challenging aspects of computer music that has been addressed just recently. The main problem in modeling expressiveness is to grasp the performer's "touch," that is, the knowledge applied when performing a score.