Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches

By Agnar Aamodt, E. Plaza. 1994

Subfields: Knowledge Graphs & Representation

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Citations in the corpus

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “Case-based reasoning is a paradigm that involves solving a new problem using known solutions to similar past problems (Aamodt and Plaza, 1994).”
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification (2014, background): “Studies of human decision-making and cognition provided the key inspiration for artificial intelligence Case-Based Reasoning (CBR) approaches [2, 28].”
ConvNets and ImageNet Beyond Accuracy: Explanations, Bias Detection, Adversarial Examples and Model Criticism (2017, methodology): “They have extensively been used in case-based reasoning (CBR) to improve the interpretability of the models [1, 4, 25].”
Case-Based Planning and Execution for Real-Time Strategy Games (2007, methodology): “To achieve that task, the BG system uses two separate processes: case retrieval and case adaptation (that correspond to the ﬁrst two processes of the 4R CBR model [1]).”
On Interpretability of Artificial Neural Networks: A Survey (2020, background): “inference with fuzziness in the interval [0, 1].”
Artificial intelligence in recommender systems (2020, background): “Case-based reasoning uses previous cases to solve the current problem [25] and is a commonly used technique for knowledge-based systems.”
Design of a RFID case-based resource management system for warehouse operations (2005, background): “Aamodt and Plaza (1994) proposed that CBR operation is subdivided into four tasks: retrieve, reuse, revise and retain, and it forms a cycle which leads to continual improvement by storing more and more cases.”
Enabling Flexibility in Process-Aware Information Systems (2012, background): “CBR also contributes to incremental and sustained learning: every time a new problem is solved, information about it and its solution is retained, and therefore immediately made available for solving future problems [26].”
Transfer Learning for Reinforcement Learning Domains: A Survey (2009, methodology): “Two methods usecase based reasoning(CBR) (Aamodt and Plaza, 1994) to help match previously learned instances with new instances, and one uses linear programming (LP) to calculate a value function from a given model (as part of a dynamic programming routine).”
ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases (2017, methodology): “They have extensively been used in case-based reasoning (CBR) to improve the interpretability of the models [34,35,36].”
Rethinking Semantic Segmentation: A Prototype View (2022, background): “Cognitive psychological studies evidence that people use past cases as models when learning to solve problems [1, 87, 125].”
Case-based reasoning (1994, unknown): “Case-based reasoning DAVID B. LEAKE Computer Science Department, Indiana University, Bloomington, IN 47405, USA (email: Ieake@cs.indiana.edu)”