Neuro-Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal (2302.01242v2)

Abstract: We introduce Neuro-Symbolic Continual Learning, where a model has to solve a sequence of neuro-symbolic tasks, that is, it has to map sub-symbolic inputs to high-level concepts and compute predictions by reasoning consistently with prior knowledge. Our key observation is that neuro-symbolic tasks, although different, often share concepts whose semantics remains stable over time. Traditional approaches fall short: existing continual strategies ignore knowledge altogether, while stock neuro-symbolic architectures suffer from catastrophic forgetting. We show that leveraging prior knowledge by combining neuro-symbolic architectures with continual strategies does help avoid catastrophic forgetting, but also that doing so can yield models affected by reasoning shortcuts. These undermine the semantics of the acquired concepts, even when detailed prior knowledge is provided upfront and inference is exact, and in turn continual performance. To overcome these issues, we introduce COOL, a COncept-level cOntinual Learning strategy tailored for neuro-symbolic continual problems that acquires high-quality concepts and remembers them over time. Our experiments on three novel benchmarks highlights how COOL attains sustained high performance on neuro-symbolic continual learning tasks in which other strategies fail.

• The paper introduces cool, a concept-level continual learning strategy that mitigates catastrophic forgetting and preserves semantic stability.
• It employs minimal concept supervision and a rehearsal penalty to prevent reasoning shortcuts and maintain high concept accuracy across tasks.
• Empirical evaluations across three benchmarks demonstrate improved forward and backward knowledge transfer, highlighting cool’s practical benefits.

Neuro-Symbolic Continual Learning: Overview and Insights

The paper "Neuro-Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal" presents an exploration of the interplay between neuro-symbolic tasks and continual learning methodologies. The authors investigate the challenge of Neuro-Symbolic Continual Learning (NeSy-CL), where a model needs to solve a series of tasks that necessitate the mapping of sub-symbolic inputs to high-level symbolic concepts and the computation of predictions that are consistent with prior knowledge.

Key Insights

The paper starts with the assertion that traditional continual learning approaches fail to leverage prior knowledge efficiently and often succumb to catastrophic forgetting, a condition where previously learned information is lost upon learning new tasks. Neuro-symbolic architectures, while adept at reasoning with high-level concepts, also face challenges as they often rely on stability in the semantics of these concepts across tasks.

Challenges in NeSy-CL

The crux of the problem identified by the authors is twofold:

1. Catastrophic Forgetting and Reasoning Shortcuts: Even when neuro-symbolic models are augmented with continual learning strategies, they suffer from reasoning shortcuts. This refers to models leveraging spurious correlations that appear in training data to make predictions that technically satisfy the knowledge, but eventually distort the true semantics of the concepts.
2. Stable Semantic Concepts: The paper postulates that while concepts in neuro-symbolic tasks differ, the semantics of these concepts should remain consistent. This stability is often undermined in continual learning scenarios where data distributions change across tasks.

Proposed Solution: cool

To address these challenges, the authors propose "cool", a COncept-level cOntinual Learning strategy. cool is designed to acquire high-quality concepts and retain their semantics over time by:

1. Minimal Concept Supervision: Introducing a small number of task examples with concept supervision to quickly zero in on high-quality concepts.
2. Concept Rehearsal: Implementing a rehearsal strategy that maintains the integrity of learned concepts across tasks, thus avoiding reasoning shortcuts. This strategy utilizes a penalty term in the training loss that enforces the stability of concept distributions across tasks.
3. Continual Strategy Compatibility: cool can be integrated with various neuro-symbolic architectures, making it a versatile tool in continual learning scenarios.

Empirical Evaluation

The authors test cool across three novel benchmarks specifically designed for NeSy-CL. Their experiments highlight the advantages of cool in:

• Facilitating High Concept and Label Accuracy: By preserving the intended semantics of concepts better than state-of-the-art methods.
• Enhancing Forward and Backward Transfer Performance: Showing substantial improvement in retaining knowledge from past tasks and transferring learned concepts to new tasks.

Implications and Future Directions

Practically, cool implies that minimal concept annotation can substantially alleviate the challenges of catastrophic forgetting and reasoning shortcuts. Theoretically, it offers a new perspective on integrating knowledge consistency with continual learning architectures. Future research could extend this work by exploring how cool can be adapted to even more diverse types of neuro-symbolic models and how it scales with more complex reasoning tasks.

In conclusion, the paper sheds light on the intricate dynamics of neuro-symbolic continual learning tasks and proposes an effective solution to mitigate common pitfalls associated with task-specific reasoning shortcuts and concept drift, offering a potentially impactful method for continual learning applications in AI.