# Predicate Invention by Learning From Failures

arXiv: https://arxiv.org/abs/2104.14426 · HTML: https://mlsanity.com/paper/2104.14426/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 45 references that are in the map.
- First posted on arXiv 29 Apr 2021.

## Builds on (most cited first, 11 of 11)

- [Learning Explanatory Rules from Noisy Data](https://mlsanity.com/paper/1711.04574/) (2017)
- [Multi-shot ASP solving with clingo](https://mlsanity.com/paper/1705.09811/) (2017)
- [Learning programs by learning from failures](https://mlsanity.com/paper/2005.02259/) (2020)
- [Turning 30: New Ideas in Inductive Logic Programming](https://mlsanity.com/paper/2002.11002/) (2020)
- [Making sense of sensory input](https://mlsanity.com/paper/1910.02227/) (2019)
- [Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation](https://mlsanity.com/paper/2006.04296/) (2020)
- [Logical reduction of metarules](https://mlsanity.com/paper/1907.10952/) (2019)
- [Learning Relational Representations with Auto-encoding Logic Programs](https://mlsanity.com/paper/1903.12577/) (2019)
- [Best-Effort Inductive Logic Programming via Fine-grained Cost-based Hypothesis Generation](https://mlsanity.com/paper/1707.02729/) (2017)
- [Playgol: learning programs through play](https://mlsanity.com/paper/1904.08993/) (2019)
- [Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation](https://mlsanity.com/paper/1606.08658/) (2016)

## Built on by (most cited first, 9 of 9)

- [CrossBeam: Learning to Search in Bottom-Up Program Synthesis](https://mlsanity.com/paper/2203.10452/) (2022)
- [Learning Higher-Order Programs without Meta-Interpretive Learning](https://mlsanity.com/paper/2112.14603/) (2021)
- [Learning logic programs by combining programs](https://mlsanity.com/paper/2206.01614/) (2022)
- [EXPIL: Explanatory Predicate Invention for Learning in Games](https://mlsanity.com/paper/2406.06107/) (2024)
- [Learning programs with magic values](https://mlsanity.com/paper/2208.03238/) (2022)
- [Predicate Renaming via Large Language Models](https://mlsanity.com/paper/2510.25517/) (2025)
- [Learning logic programs through divide, constrain, and conquer](https://mlsanity.com/paper/2109.07818/) (2021)
- [Learning logic programs by finding minimal unsatisfiable subprograms](https://mlsanity.com/paper/2401.16383/) (2024)
- [Parallel Constraint-Driven Inductive Logic Programming](https://mlsanity.com/paper/2109.07132/) (2021)

## Abstract (arXiv)

Discovering novel high-level concepts is one of the most important steps needed for human-level AI. In inductive logic programming (ILP), discovering novel high-level concepts is known as predicate invention (PI). Although seen as crucial since the founding of ILP, PI is notoriously difficult and most ILP systems do not support it. In this paper, we introduce POPPI, an ILP system that formulates the PI problem as an answer set programming problem. Our experiments show that (i) PI can drastically improve learning performance when useful, (ii) PI is not too costly when unnecessary, and (iii) POPPI can substantially outperform existing ILP systems.