neuralguided

Neuralguided is a term used to describe approaches that integrate neural networks with traditional, non-neural algorithms to guide their operation. In neuralguided systems, a neural component learns from data to produce guidance signals—such as scoring functions or priors—that steer an underlying algorithm like a search procedure, planner, or optimizer. The neural module provides learned heuristics, while the classical component supplies structure, guarantees, or efficiency.

Common patterns include using neural networks to propose promising branches in a search tree, to predict cost-to-go estimates in planning, or to bias sampling in probabilistic methods. These methods can be applied to program synthesis and automated reasoning, where neural scores guide symbolic search; to combinatorial optimization, where the network suggests promising variable assignments; and to structured prediction tasks in natural language processing or computer vision, where guidance improves decoding or inference.

Advantages include data-driven adaptability and potential efficiency gains by focusing resources on high-probability parts of the space. Limitations involve the need for representative training data, potential mismatch between learned guidance and problem instances, calibration challenges, and integration complexity with existing algorithms.

History and scope: As AI research progressed, researchers explored neuralguided variants of search, planning, and optimization, often framed as augmenting traditional pipelines with learned heuristics. The term neuralguided describes this class of approaches and is used across domains to contrast purely symbolic or purely neural methods.

See also: neural-symbolic integration, guided search, learning-to-search, deep reinforcement learning, neural planning.