Object-Centric Case-Based Reasoning via Argumentation
We introduce Slot Attention Argumentation for Case-Based Reasoning (SAA-CBR), a novel neuro-symbolic pipeline for image classification that integrates object-centric learning via a neural Slot Attention (SA) component with symbolic reasoning conducted by Abstract Argumentation for Case-Based Reasoning (AA-CBR). We explore novel integrations of AA-CBR with the neural component, including feature combination strategies, casebase reduction via representative samples, novel count-based partial orders, a One-Vs-Rest strategy for extending AA-CBR to multi-class classification, and an application of Supported AA-CBR, a bipolar variant of AA-CBR. We demonstrate that SAA-CBR is an effective classifier on the CLEVR-Hans datasets, showing competitive performance against baseline models.
Code (0)
등록된 구현이 없습니다.
Tasks
Multi-class ClassificationImage ClassificationSimilar Papers 제목 키워드 기반
Explainable Automated Reasoning in Law using Probabilistic Epistemic Argumentation
Applying automated reasoning tools for decision support and analysis in law has the potential to make court decisions more transparent and objective. Since there is often uncertainty about the accuracy and relevance of e…
The Effect of Preferences in Abstract Argumentation Under a Claim-Centric View
In this paper, we study the effect of preferences in abstract argumentation under a claim-centric perspective. Recent work has revealed that semantical and computational properties can change when reasoning is performed …
Abstract ArgumentationPreference-Based Abstract Argumentation for Case-Based Reasoning (with Appendix)
In the pursuit of enhancing the efficacy and flexibility of interpretable, data-driven classification models, this work introduces a novel incorporation of user-defined preferences with Abstract Argumentation and Case-Ba…
Abstract ArgumentationInterpretable Machine LearningDeep Arguing
Deep learning has become the dominant approach for creating high capacity, scalable models across diverse data modalities. However, because these models rely on a large number of learned parameters, tightly couple featur…
Neuro-Argumentative Learning with Case-Based Reasoning
We introduce Gradual Abstract Argumentation for Case-Based Reasoning (Gradual AA-CBR), a data-driven, neurosymbolic classification model in which the outcome is determined by an argumentation debate structure that is lea…
Abstract ArgumentationMulti-class Classification