Tag
Hubert Dreyfus's 1985 critique argued that symbolic AI was a degenerating research program due to unsolved commonsense knowledge problems and failure to model human understanding and embodiment.
This paper proposes a method to extract an executable Prolog program from a deep reinforcement learning policy, providing theoretical guarantees on return and fidelity, enabling interpretability and manual editing.
The author reflects on learning Lisp for symbolic AI 25 years ago and demonstrates how a modern AI agent loop can be implemented in just 100 lines of Common Lisp, emphasizing the elegance of recursion.
An interview with Claude Roux, maintainer of LispE and TAMGU, discussing his career in computational linguistics, symbolic AI, and the limitations of rule-based NLP systems.
BiNSGPS is a framework that introduces bidirectional interaction between a multimodal LLM adviser and a symbolic solver for geometry problem solving, allowing feedback from the solver to correct errors and generate auxiliary hypotheses. It achieves state-of-the-art performance of 90.5% on Geometry3K and 90.1% on PGPS9K benchmarks.
This paper argues that consensus-seeking in multi-agent LLM systems is insufficient for value-laden tasks, proposing a knowledge-representation layer that classifies agent reasoning-trace disagreements into four symbolic states to enable strategic routing in systems like content moderation.
This paper introduces gammaILP, a fully differentiable framework for learning first-order rules directly from image data without label leakage, addressing challenges in symbol grounding and predicate invention.