Holographic Case-Based Reasoning.
08 Jun 2020-pp 144-159
Abstract: 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.
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18 Apr 2021
TL;DR: This paper observes 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, and explores the adaptation process in CBR as a slow thinking manifestation, leading to Model 3.
Abstract: 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.
2 citations
Cites background from "Holographic Case-Based Reasoning."
...In a recent work, the idea of holographic CBR (Ganesan and Chakraborti 2020) was presented, where cases have localized knowledge containers and thereby are informed of their association with the rest of the case-base....
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13 Sep 2021
TL;DR: Holographic Case-Based Reasoning (HCBR) as mentioned in this paper is a framework developed to build cognitively appealing case-based reasoners with proactive and interconnected cases using dynamic memory.
Abstract: 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.
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TL;DR: This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them, and shows how to efficiently compute PageRank for large numbers of pages.
Abstract: The importance of a Web page is an inherently subjective matter, which depends on the readers interests, knowledge and attitudes. But there is still much that can be said objectively about the relative importance of Web pages. This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them. We compare PageRank to an idealized random Web surfer. We show how to efficiently compute PageRank for large numbers of pages. And, we show how to apply PageRank to search and to user navigation.
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01 Jan 2011TL;DR: Buku terlaris New York Times and The Economist tahun 2012 as mentioned in this paper, and dipilih oleh The NewYork Times Book Review sebagai salah satu dari sepuluh buku terbaik tahune 2011, Berpikir, Cepat and Lambat ditakdirkan menjadi klasik.
Abstract: Buku terlaris New York Times
Pemenang Penghargaan Buku Terbaik Akademi Sains Nasional pada tahun 2012
Dipilih oleh New York Times Book Review sebagai salah satu dari sepuluh buku terbaik tahun 2011
A Globe and Mail Judul Buku Terbaik Tahun 2011
Salah Satu Buku The Economist tahun 2011
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2013 Presidential Medal of Freedom Recipient
Pekerjaan Kahneman dengan Amos Tversky adalah subyek dari Proyek Undoing Michael Lewis: Persahabatan yang Mengubah Pikiran Kita
Dalam buku terlaris internasional, Berpikir, Cepat, dan Lambat, Daniel Kahneman, psikolog terkenal dan pemenang Hadiah Nobel dalam Ekonomi, membawa kita pada perjalanan pemikiran yang inovatif dan menjelaskan dua sistem yang mendorong cara kita berpikir. Sistem 1 cepat, intuitif, dan emosional; Sistem 2 lebih lambat, lebih deliberatif, dan lebih logis. Dampak dari terlalu percaya pada strategi perusahaan, kesulitan memprediksi apa yang akan membuat kita bahagia di masa depan, efek mendalam dari bias kognitif dalam segala hal mulai dari bermain pasar saham hingga merencanakan liburan kita berikutnya ― masing-masing dapat dipahami hanya dengan mengetahui bagaimana kedua sistem tersebut membentuk penilaian dan keputusan kami.
Melibatkan pembaca dalam percakapan yang hidup tentang bagaimana kita berpikir, Kahneman mengungkapkan di mana kita bisa dan tidak dapat mempercayai intuisi kita dan bagaimana kita dapat memanfaatkan manfaat dari pemikiran yang lambat. Dia menawarkan wawasan praktis dan mencerahkan tentang bagaimana pilihan dibuat baik dalam bisnis kita dan kehidupan pribadi kita ― dan bagaimana kita dapat menggunakan teknik yang berbeda untuk menjaga gangguan mental yang sering membawa kita ke dalam masalah. Pemenang Penghargaan Buku Terbaik Akademi Sains Nasional dan Hadiah Buku Los Angeles Times dan dipilih oleh The New York Times Book Review sebagai salah satu dari sepuluh buku terbaik tahun 2011, Berpikir, Cepat dan Lambat ditakdirkan menjadi klasik.
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TL;DR: Locally weighted regression as discussed by the authors is a way of estimating a regression surface through a multivariate smoothing procedure, fitting a function of the independent variables locally and in a moving fashion analogous to how a moving average is computed for a time series.
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