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. The main conference track featured 19 research paper presentations, 16 posters, and two invited speakers. The papers and posters reflected the state of the art of case-based reasoning, dealing both with open problems at the core of casebased reasoning (especially in similarity assessment, case adaptation, and case-based maintenance), as well as trending applications of CBR. Minor, Goethe University, Germany, and Emmanuel The first invited speaker, Tony Veale from University Nauer, LORIA, France.

We propose an approach that allows a robot to evaluate its trustworthiness and adapt its behavior accordingly. The The addition of a robot to a team can be difficult if trust estimate, which we refer to as an inverse trust estimate, the human teammates do not trust the robot. This differs from traditional computational trust metrics in that it can result in underutilization or disuse of the robot, measures how much trust other agents have in the robot rather even if the robot has skills or abilities that are necessary than how much trust the robot has in other agents. Since the to achieve team goals or reduce risk. To robot can only use observable information and not information help a robot integrate itself with a human team, we that is internal to the teammates' reasoning, the inverse present an agent algorithm that allows a robot to estimate trust estimate relies on evaluating the standard interactions its trustworthiness and adapt its behavior accordingly.

The performance of trust models highly depend on the characteristics of the environments where they are applied. Thus, it becomes challenging to choose a suitable trust model for a given e-marketplace environment, especially when ground truth about the agent (buyer and seller) behavior is unknown (called unknown environment). We propose a case-based reasoning framework to choose suitable trust models for unknown environments, based on the intuition that if a trust model performs well in one environment, it will do so in another similar environment. Firstly, we build a case base with a number of simulated environments (with known ground truth) along with the trust models most suitable for each of them. Given an unknown environment, case-based retrieval algorithms retrieve the most similar case(s), and the trust model of the most similar case(s) is chosen as the most suitable model for the unknown environment. Evaluation results confirm the effectiveness of our framework in choosing suitable trust models for different e-marketplace environments.

Identifying a patient’s important medical problems requires broad and deep medical expertise, as well as significant time to gather all the relevant facts from the patient’s medical record and assess the clinical importance of the facts in reaching the final conclusion. A patient’s medical problem list is by far the most critical information that a physician uses in treatment and care of a patient. In spite of its critical role, its curation, manual or automated, has been an unmet need in clinical practice. We developed a machine learning technique in IBM Watson to automatically generate a patient’s medical problem list. The machine learning model uses lexical and medical features extracted from a patient’s record using NLP techniques. We show that the automated method achieves 70% recall and 67% precision based on the gold standard that medical experts created on a set of de-identified patient records from a major hospital system in the US. To the best of our knowledge this is the first successful machine learning/NLP method of extracting an open-ended patient’s medical problems from an Electronic Medical Record (EMR). This paper also contributes a methodology for assessing accuracy of a medical problem list generation technique.

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.

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

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.