We present the Bayesian Case Model (BCM), a general framework for Bayesian case-based reasoning (CBR) and prototype classification and clustering. BCM brings the intuitive power of CBR to a Bayesian generative framework. The BCM learns prototypes, the "quintessential" observations that best represent clusters in a dataset, by performing joint inference on cluster labels, prototypes and important features. Simultaneously, BCM pursues sparsity by learning subspaces, the sets of features that play important roles in the characterization of the prototypes. The prototype and subspace representation provides quantitative benefits in interpretability while preserving classification accuracy. Human subject experiments verify statistically significant improvements to participants' understanding when using explanations produced by BCM, compared to those given by prior art.

Buildings make a city’s landscape and are home to its people. The demand for smart buildings and housing is growing by the need for cities to make their buildings more efficient, green and livable. This emergent intelligence is underpinned by the use of Information and Communications Technology (ICT) linked by Pervasive Sensing and real-time data analytics. In a typical growth of smart buildings, Smart Campuses are going to be amazing community hubs which will be more sustainable, efficient and supportive of its inhabitants. In this regard, huge amount of useful and real-time generated data are being analyzed to help people and machines infer instant decisions in relation to energy efficiency. However, because of different terminologies used by different players, structural, representational and semantic heterogeneity constrain the interoperability between applications and misleads to adaptive and context-aware control behavior. In this paper, the focus is to alleviate the current problem by designing a semantic framework that represents the smart campus data and activities in an ontological model. Also, the framework is deepened by an Artificial Intelligent (AI) method using Weighted Case Based Reasoning (WCBR) for enabling context awareness. An illustration will be the elaboration of an adaptive and autonomous control of HVAC (Heating Ventilation and Air Conditioning) system, in this example the WCBR is discussed and case representation, case adaptation, and similarity computation are sketched in detail.

In either view, typical examples are closer where (only) positive examples ought to exceptional cases, case memory, analogy, to the prototype, and atypical examples and be.

In this short note, we discuss several aspects of "dimensions" and the related construct of "factors". We concentrate on those aspects that are relevant to articles in this special issue, especially those dealing with the analysis of the wild animal cases discussed in Berman and Hafner's 1993 ICAIL article. We review the basic ideas about dimensions, as used in HYPO, and point out differences with factors, as used in subsequent systems like CATO. Our goal is to correct certain misconceptions that have arisen over the years.

In this paper we discuss a hybrid approach combining Case-Based Reasoning (CBR) .

We discuss several aspects of legal arguments, primarily arguments about the meaning of statutes. First, we discuss how the requirements of argument guide the specification and selection of supporting cases and how an existing case base influences argument formation.

We focus on realistic, complex domains where the concepts, terms and predicates used by domain rules or by rule-based models are not well-defined. Often, in such inherently ill-defined domains the rules do not encompass all the situations they are asked or assumed to cover, admit tacit exceptions, or can be contradicted and annulled by other rules. Interpretation is therefore required of the terms and predicates used. The law is a prototypical example of such an area, where terms used in legal statutes are not completely defined by legal regulations. The use of case-based reasoning (CBR) to complement and supplement other types of reasoning involves many computational questions of system architecture and control. The key focus of this work is how and when to interleave CBR with other modes of reasoning in the context of applying a rule or model to a new set of facts in light of a corpus of cases of past application. The goal is to generate an explanation or argument as to how the new fact situation might be interpreted. In particular, we report on a system called CABARET (CAse-BAsed REasoning Tool), a hybrid architecture we have built to study and experiment with these issues.

The next project was Joel Moses's program SIN. See Moses, Symbolic Integration: The Stormy Decade, 14 Comm.

This paper presents a hybrid case-based reasoning (CBR) and information retrieval (IR) system, called SPIRE, that both retrieves documents from a full-text document corpus and from within individual documents, and locates passages likely to contain information about important problem-solving features of cases. SPIRE uses two case-bases, one containing past precedents, and one containing excerpts from past case texts. Both are used by SPIRE to automatically generate queries, which are then run by the INQUERY full-text retrieval engine on a large text collection in the case of document retrieval and on individual text documents for passage retrieval.

We discuss key ingredients of case-based reasoning, in general, and 3. A technique, the "claim lattice", for organizing the the correspondence of these to elements of HYPO.