The Difficulties of Learning Logic Programs with Cut
F. Bergadano, D. Gunetti, U. Trinchero
Abstract
As real logic programmers normally use cut (!), an effective learning procedure for logic programs should be able to deal with it. Because the cut predicate has only a procedural meaning, clauses containing cut cannot be learned using an extensional evaluation method, as is done in most learning systems. On the other hand, searching a space of possible programs (instead of a space of independent clauses) is unfeasible. An alternative solution is to generate first a candidate base program which covers the positive examples, and then make it consistent by inserting cut where appropriate. The problem of learning programs with cut has not been investigated before and this seems to be a natural and reasonable approach. We generalize this scheme and investigate the difficulties that arise. Some of the major shortcomings are actually caused, in general, by the need for intensional evaluation. As a conclusion, the analysis of this paper suggests, on precise and technical grounds, that learning cut is difficult, and current induction techniques should probably be restricted to purely declarative logic languages.
Create a lesson
Related papers
BEAKER: An Expert-Curated Benchmark for Embodied Brains in Self-Driving Chemical Laboratories
Fei Lin, Tengchao Zhang, Ziyang Gong et al.
FrontierPhysics: Evaluating Agents for End-to-End Physics Research
Bingran You, Wenjun Ke, Ria Rosenauer et al.
Dull, Dirty, Dangerous? Working Conditions and the Limits of Occupational AI Exposure
Siyi Yu
Learning to Decode Linear Codes with Generalized Belief Propagation Network
Samira Malek
On Turn-Level Best-of-N Sampling for Scaling LLM Agents
Jalaj Bhandari, Dhawal Gupta, Hongli Zhan et al.
CompressionBench: How can your coding agents compress LLMs?
Xinrui Zhong, Zhifeng Li, Zhiming Wang et al.