Case-based reasoning (CBR) systems have tight connections with machine learning and knowledge discovery and often incorporate diverse knowledge discovery functionalities and algorithms. This article presents themes identified in work presented at recent workshops on synergies between CBR and knowledge discovery. Among the main themes appear Big Data, with cases involving signals, images, texts, and other complex types of data; similarity metric discovery, in the form of weight spaces, feature weights, and feature selection; adaptation knowledge; explainability and transparency; and user centeredness and interactivity. Researchers highlight the advantages of case-based reasoning in terms of its lazy learning, explainability, user centeredness, and interactivity when performing knowledge discovery, as well as how diverse knowledge discovery methods can improve CBR.

Humans seek to gain knowledge and structure data by many means including both bottom-up and top-down methods. But often, people have a specific purpose to their activity that drives the process, that is, they have particular questions that need answering in support of some broader investigation. These questions often change as answers point in various directions during an investigation, whether the investigation is formal (e.g., scientific, legal, journalistic, or military) or simply an informal browsing of the internet. Here we take a mixed-initiative approach to knowledge discovery, and we present a system called Kyudo that supports the process using a conversational case-based reasoning process. Cases in Kyudo are sequences of knowledge goals or questions that form arcs through a multidimensional knowledge space and that form the core activity in a dialogue between the user and system. As the system gains more experience and therefore more cases, it is able to detect similarity in knowledge goals and prompt the user with additional relevant goals that can short circuit the human reasoning process to minimize tangents or false starts. In this paper we present a distance-based mechanism that reduces the total length of a goal trajectory through guidance that accelerates the human reasoning process and aids effective knowledge discovery.

The kidnapping of Tennessee teen Elizabeth Thomas allegedly by his ex-teacher Tad Cummins will be the point of discussion at Friday's 20/20 show. The show will focus on Cummins' months on the run and what will happen next to the 50-year-old, who was arrested mid-April. The 15-year-old former student of Cummins went missing in March and was found in a remote part of northern California, more than a month after her disappearance. Elizabeth was reunited with her family, while Cummins was arrested. He has been charged with kidnapping and transporting a minor across state lines with the intention to engage in sexual activity.

We consider machine learning in a comparison-based setting where we are given a set of points in a metric space, but we have no access to the actual distances between the points. Instead, we can only ask an oracle whether the distance between two points $i$ and $j$ is smaller than the distance between the points $i$ and $k$. We are concerned with data structures and algorithms to find nearest neighbors based on such comparisons. We focus on a simple yet effective algorithm that recursively splits the space by first selecting two random pivot points and then assigning all other points to the closer of the two (comparison tree). We prove that if the metric space satisfies certain expansion conditions, then with high probability the height of the comparison tree is logarithmic in the number of points, leading to efficient search performance. We also provide an upper bound for the failure probability to return the true nearest neighbor. Experiments show that the comparison tree is competitive with algorithms that have access to the actual distance values, and needs less triplet comparisons than other competitors.

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.

Hashing has been proven a promising technique for fast nearest neighbor search over massive databases. In many practical tasks it usually builds multiple hash tables for a desired level of recall performance. However, existing multi-table hashing methods suffer from the heavy table redundancy, without strong table complementarity and effective hash code learning. To address the problem, this paper proposes a multi-table learning method which pursues a specified number of complementary and informative hash tables from a perspective of ensemble learning. By regarding each hash table as a neighbor prediction model, the multi-table search procedure boils down to a linear assembly of predictions stemming from multiple tables. Therefore, a sequential updating and learning framework is naturally established in a boosting mechanism, theoretically guaranteeing the table complementarity and algorithmic convergence. Furthermore, each boosting round pursues the discriminative hash functions for each table by a discrete optimization in the binary code space. Extensive experiments carried out on two popular tasks including Euclidean and semantic nearest neighbor search demonstrate that the proposed boosted complementary hash-tables method enjoys the strong table complementarity and significantly outperforms the state-of-the-arts.

We introduce a novel supervised metric learning algorithm named parameter free large margin nearest neighbor (PFLMNN) which can be seen as an improvement of the classical large margin nearest neighbor (LMNN) algorithm. The contributions of our work consist of two aspects. First, our method discards the costterm which shrinks the distances between inquiry input and its k target neighbors (the k nearest neighbors with same labels as inquiry input) in LMNN, and only focuses on improving the action to push the imposters (the samples with different labels form the inquiry input) apart out of the neighborhood of inquiry. As a result, our method does not have the parameter needed to tune on the validating set, which makes it more convenient to use. Second, by leveraging the geometry information of the imposters, we construct a novel cost function to penalize the smalldistances between each inquiry and its imposters. Different from LMNN considering every imposter located in the neighborhood of each inquiry, our method only takes care of the nearest imposters. Because when the nearest imposter is pushed out of the neighborhood of its inquiry, other imposters would be all out. In this way, the constraints in our model are much less than that of LMNN, which makes our method much easier to find the optimal distance metric. Consequently, our method not only learns a better distance metric than LMNN, but also runs faster than LMNN. Extensive experiments on different data sets with various sizes and difficulties are conducted, and the results have shown that, compared with LMNN, PFLMNN achieves better classification results.

This paper presents an approach to automatically extracting and representing narrative information from stories written in natural language. Specifically, we present our results in extracting story graphs, a formalism that captures the entities (e.g., characters, props, locations) and their interactions in a story. The long-term goal of this research is to automatically extract this narrative information in order to use it in computational narrative systems such as story generators or interactive fiction systems. Our approach combines narrative domain knowledge and off-the-shelf natural language processing (NLP) tools into a machine learning framework to build story graphs by automatically identifying entities, actions, and narrative roles. We report the performance of our fully automated system in a corpus of 21 stories and provide examples of the extracted story graphs and their uses in computational narrative systems.

In goal recognition, the basic problem domain consists of the following: Recent advances in robotics and artificial intelligence have brought a variety of assistive robots designed to help humans - a set E of environment fluents; accomplish their goals. However, many have limited autonomy and lack the ability to seamlessly integrate with - a state S that is a value assignment to those fluents; human teams. One capability that can facilitate such humanrobot - a set A of actions that describe potential transitions between teaming is the robot's ability to recognize its teammates' states (with preconditions and effects defined over goals, and react appropriately. This function permits E, and parameterized over a set of environment objects the robot to actively assist the team and avoid performing O); and redundant or counterproductive actions.

This paper presents a knowledge-based approach to the task of learning and identifying galaxies from their images. To this effect, we propose a crowd-sourced pipeline approach that employs two systems - case based and rule based systems. First, the approach extracts morphological features i.e. features describing the structure of the galaxy such as its shape, central characteristics e.g., has a bar or bulge at its center)etc., using computer vision techniques. Then it employs a case based reasoning system and a rule based system to perform the classification task. Our initial results show that this pipeline is effective in learning reasonably accurate models on this complex task.