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Leake, David


Applying the Case Difference Heuristic to Learn Adaptations from Deep Network Features

arXiv.org Artificial Intelligence

The case difference heuristic (CDH) approach is a knowledge-light method for learning case adaptation knowledge from the case base of a case-based reasoning system. Given a pair of cases, the CDH approach attributes the difference in their solutions to the difference in the problems they solve, and generates adaptation rules to adjust solutions accordingly when a retrieved case and new query have similar problem differences. As an alternative to learning adaptation rules, several researchers have applied neural networks to learn to predict solution differences from problem differences. Previous work on such approaches has assumed that the feature set describing problems is predefined. This paper investigates a two-phase process combining deep learning for feature extraction and neural network based adaptation learning from extracted features. Its performance is demonstrated in a regression task on an image data: predicting age given the image of a face. Results show that the combined process can successfully learn adaptation knowledge applicable to nonsymbolic differences in cases. The CBR system achieves slightly lower performance overall than a baseline deep network regressor, but better performance than the baseline on novel queries.


Knowledge-Based Morphological Classification of Galaxies from Vision Features

AAAI Conferences

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.


Adaptation-Guided Case Base Maintenance

AAAI Conferences

In case-based reasoning (CBR), problems are solved by retrieving prior cases and adapting their solutions to fit; learning occurs as new cases are stored. Controlling the growth of the case base is a fundamental problem, and research on case-base maintenance has developed methods for compacting case bases while maintaining system competence, primarily by competence-based deletion strategies assuming static case adaptation knowledge. This paper proposes adaptation-guided case-base maintenance (AGCBM), a case-base maintenance approach exploiting the ability to dynamically generate new adaptation knowledge from cases. In AGCBM, case retention decisions are based both on cases' value as base cases for solving problems and on their value for generating new adaptation rules. he paper illustrates the method for numerical prediction tasks (case-based regression) in which adaptation rules are generated automatically using the case difference heuristic. In comparisons of AGCBM to five alternative methods in four domains, for varying case base densities, AGCBM outperformed the alternatives in all domains, with greatest benefit at high compression.


Announcing the New App for AI Magazine

AI Magazine

I'm delighted to announce that this issue his spring, AI Magazine launched its digital edition, which inaugurates another major delivery advance, the launch of the AI Magazine app. The app delivers access to the magazine in a device-tailored form for the iPhone, iPad, Android smartphone, Android table, or Amazon Kindle Fire. In addition to providing easy interaction with the magazine's content, the app contains a library of issues (including all of 2013), which will enable reading the magazine anywhere, even offline in airplane mode. It supports searching within and across issues, saving content, and sharing by email or social media. Push notifications will inform users of new issues, and an RSS feed (coming soon) will inform readers of AAAI announcements.


Announcing the New App for AI Magazine

AI Magazine

This spring, AI Magazine launched its digital edition, which brought the magazine new interactivity and color throughout. I'm delighted to announce that this issue inaugurates another major delivery advance, the launch of the AI Magazine app.


Announcing the Digital Edition of AI Magazine

AI Magazine

I am delighted to announce that this project has come to fruition with the launch of the digital edition of AI Magazine. As each issue of the magazine is published, its digital edition will be delivered to subscribers by email. The digital edition is browser-based, making it accessible via the web, smartphone, or any modern web-enabled device. It provides the ability to quickly search, save, and share articles, as well as convenient options for navigating the magazine and seamlessly linking to other resources. The digital edition will enable substantial advances in the magazine's future design, such as the use of color throughout and the inclusion of embedded video, and over time the magazine will increasingly exploit this potential.


Customizing Question Selection in Conversational Case-Based Reasoning

AAAI Conferences

Conversational case-based reasoning systems use an interactive dialog to retrieve stored cases. Normally the ordering of questions in this dialog is chosen based only on their discriminativeness. However, because the user may not be able to answer all questions, even highly discriminative questions are not guaranteed to provide information. This paper presents a customization method CCBR systems can apply to adjust entropy-based discriminativeness considerations by predictions of user ability to answer questions. The method uses a naive Bayesian classifier to classify users into user groups based on the questions they answer, applies information from group profiles to predict which future questions they are likely to be able to answer, and selects the next questions to ask based on a combination of information gain and response likelihood. The method was evaluated for a mix of simulated user groups, each associated with particular probabilities for answering questions about each case indexing feature, in four sample domains. For simulated users with varying abilities to answer particular questions, results showed improvement in dialog length over a non-customized entropy-based approach in all test domains.


Enhancing Case Adaptation with Introspective Reasoning and Web Mining

AAAI Conferences

Case-based problem-solving systems reason by retrieving relevant prior cases and adapting their solutions to fit new circumstances. The ability of case-based reasoning (CBR) to reason from ungeneralized episodes can benefit knowledge acquisition, but acquiring the needed case adaptation knowledge has proven challenging. This paper presents a method for alleviating this problem with just-in-time gathering of case adaptation knowledge, based on introspective reasoning and mining of Web knowledge sources. The approach combines knowledge planning with introspective reasoning to guide recovery from case adaptation failures and reinforcement learning to guide selection of knowledge sources. The failure recovery and knowledge source selection methods have been tested in three highly different domains with encouraging results. The paper closes with a discussion of limitations and future steps.