Sutanu Chakraborti

In a path-breaking work, Kahneman characterized human cognition as a result of two modes of operation, Fast Thinking and Slow Thinking. Fast thinking involves quick, intuitive decision making and slow thinking is deliberative conscious reasoning. In this paper, for the first time, we draw parallels between this dichotomous model of human cognition and decision making in Case-based Reasoning (CBR). We observe that fast thinking can be operationalized computationally as the fast decision making by a trained machine learning model, or a parsimonious CBR system that uses few attributes. On the other hand, a full-fledged CBR system may be seen as similar to the slow thinking process. We operationalize such computational models of fast and slow thinking and switching strategies, as Models 1 and 2. Further, we explore the adaptation process in CBR as a slow thinking manifestation, leading to Model 3. Through an extensive set of experiments on real-world datasets, we show that such realizations of fast and slow thinking are useful in practice, leading to improved accuracies in decision-making tasks.

A dichotomous Case-Based Reasoning (CBR) model is one in which two kinds of reasoning mechanisms are employed; these may be for realizing fast and slow problem-solving as demanded by the nature of the incoming query. Such dichotomous operation is inspired by Daniel Kahneman’s seminal work on the two modes of thinking observed in humans. In this paper, we present the following three directions of refinement for a dichotomous CBR model: selection of attributes for a fast thinking model based on parsimonious CBR, switching from fast to slow thinking based on constraints derived from domain knowledge and arriving at a complexity measure for evaluating dichotomous models. For all the three improvements identified, we discuss the results on real-world data sets and empirically analyse the effectiveness of the same.

When case-based recommender systems use preference-based feedback, we can learn user preferences by using the trade-off relations between the preferred product and the other products in the given domain. In this work, we propose a representation for trade-offs and motivate several mechanisms by which the identified trade-offs can be used in the process of recommendation. We empirically demonstrate the effectiveness of the proposed approaches in three recommendation domains.

Holographic Case-Based Reasoning is a framework developed to build cognitively appealing case-based reasoners with proactive and interconnected cases. Improved realizations of the Holographic CBR framework are developed using the principles of dynamic memory proposed by Roger Schank and tested on their cognitive appeal, efficiency, and solution quality compared to other relevant systems.

In this paper, we present a novel extension of CBR that allows cases to be more proactive at problem solving, by enriching case representations and facilitating richer interconnectedness between cases. We empirically study the improvements resulting from a holographic realization on experimental datasets. In addition to making CBR more cognitively appealing, the idea has the potential to lend itself as an elegant general CBR formalism of which diverse realizations of CBR can be viewed as instances.

Case-Based Reasoning relies on the underlying hypothesis that similar problems have similar solutions. The extent to which this hypothesis holds good has been used by CBR designers as a measure of case base complexity, which in turn gives insights on its generalization ability. Several local and global complexity measures have been proposed in the literature. However, the existing measures rely only on the similarity knowledge to compute complexity. We propose a new complexity measure called Reachability-Based Complexity Measure (RBCM) that goes beyond the similarity knowledge to include the effects of all knowledge containers in the reasoner. The proposed measure is evaluated on several realworld datasets and results suggest that RBCM correlates well with the generalization accuracy of the reasoner.

Case-Based Reasoning provides a framework for integrating domain knowledge with data in the form of four knowledge containers namely Case base, Vocabulary, Similarity, and Adaptation. It is a known fact in Case-Based Reasoning community that knowledge can be interchanged between the containers. However, the explicit interplay between them, and how this interchange is affected by the knowledge richness of the underlying domain is not yet fully understood. We attempt to bridge this gap by proposing footprint size reduction as a measure for quantifying knowledge tradeoffs between containers. The proposed measure is empirically evaluated on synthetic as well as real-world datasets. From a practical standpoint, footprint size reduction provides a unified way of estimating the impact of a given piece of knowledge in any knowledge container, and can also suggest ways of characterizing the nature of domains ranging from ill-defined to well-defined ones. Our study also makes evident the need for maintenance approaches that go beyond case base and competence to include other containers and performance objectives.