Stephen Muggleton

IJCAI Conference 2021 Conference Paper

Abductive Knowledge Induction from Raw Data

• Wang-Zhou Dai
• Stephen Muggleton

For many reasoning-heavy tasks with raw inputs, it is challenging to design an appropriate end-to-end pipeline to formulate the problem-solving process. Some modern AI systems, e. g. , Neuro-Symbolic Learning, divide the pipeline into sub-symbolic perception and symbolic reasoning, trying to utilise data-driven machine learning and knowledge-driven problem-solving simultaneously. However, these systems suffer from the exponential computational complexity caused by the interface between the two components, where the sub-symbolic learning model lacks direct supervision, and the symbolic model lacks accurate input facts. Hence, they usually focus on learning the sub-symbolic model with a complete symbolic knowledge base while avoiding a crucial problem: where does the knowledge come from? In this paper, we present Abductive Meta-Interpretive Learning (MetaAbd) that unites abduction and induction to learn neural networks and logic theories jointly from raw data. Experimental results demonstrate that MetaAbd not only outperforms the compared systems in predictive accuracy and data efficiency but also induces logic programs that can be re-used as background knowledge in subsequent learning tasks. To the best of our knowledge, MetaAbd is the first system that can jointly learn neural networks from scratch and induce recursive first-order logic theories with predicate invention.

IJCAI Conference 2021 Conference Paper

Abductive Learning with Ground Knowledge Base

• Le-Wen Cai
• Wang-Zhou Dai
• Yu-Xuan Huang
• Yu-Feng Li
• Stephen Muggleton
• Yuan Jiang

Abductive Learning is a framework that combines machine learning with first-order logical reasoning. It allows machine learning models to exploit complex symbolic domain knowledge represented by first-order logic rules. However, it is challenging to obtain or express the ground-truth domain knowledge explicitly as first-order logic rules in many applications. The only accessible knowledge base is implicitly represented by groundings, i. e. , propositions or atomic formulas without variables. This paper proposes Grounded Abductive Learning (GABL) to enhance machine learning models with abductive reasoning in a ground domain knowledge base, which offers inexact supervision through a set of logic propositions. We apply GABL on two weakly supervised learning problems and found that the model's initial accuracy plays a crucial role in learning. The results on a real-world OCR task show that GABL can significantly reduce the effort of data labeling than the compared methods.

IJCAI Conference 2013 Conference Paper

Meta-Interpretive Learning of Higher-Order Dyadic Datalog: Predicate Invention Revisited

• Stephen Muggleton
• Dianhuan Lin

In recent years Predicate Invention has been underexplored within Inductive Logic Programming due to difficulties in formulating efficient search mechanisms. However, a recent paper demonstrated that both predicate invention and the learning of recursion can be efficiently implemented for regular and context-free grammars, by way of abduction with respect to a meta-interpreter. New predicate symbols are introduced as constants representing existentially quantified higher-order variables. In this paper we generalise the approach of Meta-Interpretive Learning (MIL) to that of learning higher-order dyadic datalog programs. We show that with an infinite signature the higher-order dyadic datalog class H2 2 has universal Turing expressivity though H2 2 is decidable given a finite signature. Additionally we show that Knuth-Bendix ordering of the hypothesis space together with logarithmic clause bounding allows our Dyadic MIL implementation MetagolD to PAC-learn minimal cardinailty H2 2 definitions. This result is consistent with our experiments which indicate that MetagolD efficiently learns compact H2 2 definitions involving predicate invention for robotic strategies and higher-order concepts in the NELL language learning domain.

AIJ Journal 1999 Journal Article

Inductive Logic Programming: Issues, results and the challenge of Learning Language in Logic

• Stephen Muggleton

Inductive Logic Programming (ILP) is the area of AI which deals with the induction of hypothesised predicate definitions from examples and background knowledge. Logic programs are used as a single representation for examples, background knowledge and hypotheses. ILP is differentiated from most other forms of Machine Learning (ML) both by its use of an expressive representation language and its ability to make use of logically encoded background knowledge. This has allowed successful applications of ILP in areas such as molecular biology and natural language which both have rich sources of background knowledge and both benefit from the use of an expressive concept representation languages. For instance, the ILP system Progol has recently been used to generate comprehensible descriptions of the 23 most populated fold classes of proteins, where no such descriptions had previously been formulated manually. In the natural language area ILP has not only been shown to have higher accuracies than various other ML approaches in learning the past tense of English but also shown to be capable of learning accurate grammars which translate sentences into deductive database queries. The area of Learning Language in Logic (LLL) is producing a number of challenges to existing ILP theory and implementations. In particular, language applications of ILP require revision and extension of a hierarchically defined set of predicates in which the examples are typically only provided for predicates at the top of the hierarchy. New predicates often need to be invented, and complex recursion is usually involved. Advances in ILP theory and implementation related to the challenges of LLL are already producing beneficial advances in other sequence-oriented applications of ILP. In addition LLL is starting to develop its own character as a sub-discipline of AI involving the confluence of computational linguistics, machine learning and logic programming.

IJCAI Conference 1995 Conference Paper

Inductive Logic Programming: Inverse Resolution and Beyond

• Stephen Muggleton
• Oxford University Computing Laboratory

IJCAI Conference 1987 Conference Paper

An Oracle-based Approach to Constructive Induction

• Stephen Muggleton