Answer Set Programming Energised! End-to-End Neurosymbolic Reasoning and Learning with ASP and Energy Based Models

2026-07-09 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Mathematics & Computational Sciences · Depth: Expert, quick

Summary

A new neurosymbolic reasoning and learning methodology integrates Answer Set Programming (ASP) with an Energy Based Model (EBM) substrate for end-to-end training. This approach supports joint optimization in a continuous latent space by fully incorporating ASP-based declarative semantics, including background knowledge, constraints, and non-monotonic inference. It also provides a generalized model and practical platform for ASP-centric, robust training, advancing works at the interface of answer sets, probabilistic logic, and answer set modulo theories. This methodology is designed for applications in dynamic domains like perception and interaction. A practical implementation is demonstrated with MNIST and evaluated using the visual question-answering benchmark Clevr and the multi-object tracking benchmark MOT.

Key takeaway

For AI Scientists developing robust reasoning systems, this neurosymbolic methodology offers a path to integrate declarative knowledge with continuous learning. You can achieve end-to-end training in dynamic environments by combining ASP with EBMs. Consider applying this approach to complex perception tasks like visual question-answering or multi-object tracking to leverage explicit background knowledge and non-monotonic inference.

Key insights

End-to-end neurosymbolic reasoning integrates Answer Set Programming with Energy Based Models for robust learning in dynamic domains.

Principles

• Joint optimization uses ASP declarative semantics.
• Background knowledge and non-monotonic inference are incorporated.
• Generalizes probabilistic logic and answer set modulo theories.

Method

Integrates Answer Set Programming (ASP) with an Energy Based Model (EBM) substrate to enable joint optimization in continuous latent spaces.

In practice

• Apply to visual question-answering (Clevr).
• Use for multi-object tracking (MOT).
• Demonstrate with MNIST dataset.

Topics

• Neurosymbolic AI
• Answer Set Programming
• Energy Based Models
• Visual Question Answering
• Multi-Object Tracking
• Declarative Reasoning

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.