AIBL: Augmented Instance-Based Learning with Structured Memory and Neural Embeddings

2026-10-02

AI Summary

This study addresses the limitations of traditional instance-based learning, which relies on handcrafted rules and struggles to generalize novel patterns under distribution shift and high-dimensional data. To overcome these challenges, this work proposes a neural instance-based learning framework that generalizes symbolic matching to neural embedding similarity, integrating structured memory mechanisms to optimize retrieval and utility fusion. Furthermore, it introduces a surprise memory to isolate weakly matched samples and designs an observation-driven graduation algorithm that promotes novel patterns into active memory, thereby enhancing drift resistance in sequential decision-making. Experimental results demonstrate that the proposed method improves accuracy by 6 to 17 percentage points across five task categories, significantly enhancing adaptability in cold-start, anomaly detection, and concept drift scenarios.

Abstract

Sequential learning systems often make decisions from accumulated experience while receiving high-dimensional inputs whose distribution may change over time. Instance-Based Learning Theory (IBLT) provides a principled case-based framework for such settings through stored situation-decision-utility instances, partial matching, activation, and blending. IBLT relies on symbolic knowledge representation in dictionary-like formats, but text, images, transaction vectors, and user-item histories often require learned similarity rather than hand-specified matching rules. In this paper, we introduce AIBL (Augmented Instance-Based Learning), an instance-learning model formulated in a learned vector space for high- dimensional sequential data. AIBL generalizes symbolic situation matching to neural embedding similarity while retaining instance storage, activation- weighted retrieval, and utility blending. The AIBL model organizes memory into active, forgotten, and surprise stores. Surprise memory separates weakly matched, possible out-of-distribution, or corner-case observations from active memory, reducing forced fitting to the nearest available cases. An observation-driven graduation algorithm promotes recurring surprise instances to active memory, allowing the memory to incorporate repeated novel patterns that may arise under concept drift. We evaluate the same implementation on five machine learning tasks and three controlled simulation tasks, comparing AIBL with classical IBLT variants and task-specific baselines where appropriate. AIBL improves accuracy by 6 to 17 percentage points. The results show where vector-space retrieval improves over symbolic matching and how the added memory mechanisms govern novelty detection, cold-start handling, drift adaptation, and reward learning under the tested protocols.

Problem

• Instance-Based Learning
• High-dimensional sequential data
• Concept drift
• Out-of-distribution detection
• Neural embeddings

Innovation

• Instance-Based Learning
• Neural Embeddings
• Structured Memory
• Concept Drift
• Novelty Detection