Statistical Relational Learning (SRL) relies on statistical and probabilistic modeling to represent, learn, and reason about domains with complex relational and rich probability structures. Although SRL techniques have succeeded in many real-world applications, they follow the same assumption as most ML techniques by assuming training and testing data have the same distribution and are sampled from the same feature space. Changes between these distributions might require training a new model using new data. Transfer Learning adapts knowledge already learned to other tasks and domains to help create new models, particularly in a low-data regime setting. Many recent works have succeeded in applying Transfer Learning to relational domains. However, most focus on what and how to transfer. When to transfer is still an open research problem as a pre-trained model is not guaranteed to help or improve performance for learning a new model. Besides, testing every possible pair of source and target domains to perform transference is costly. In this work, we focus on when by proposing a method that relies on probabilistic representations of relational databases and distributions learned by models to indicate the most suitable source domain for transferring. To evaluate our approach, we analyze the performances of two transfer learning-based algorithms given the most similar target domain to a source domain according to our proposal. In the experimental results, our method has succeeded as both algorithms reach their best performance when transferring between the most similar pair of source and target domains.
This study presents a review of applications of Inductive Logic Programming (ILP) for robotic systems. The aim of the paper is to demonstrate the different methods of applying ILP to a robotic system and to also highlight some of the limitations that already exist. ILP can aid in the development of explainable and trustworthy robotics systems.
Traditional machine learning methods heavily rely on large amounts of labelled data for effective generalisation, posing a challenge in few-shot learning scenarios. In many real-world applications, acquiring large amounts of training data can be difficult or impossible. This paper presents an efficient and explainable method for few-shot learning from images using inductive logic programming (ILP). ILP utilises logical representations and reasoning to capture complex relationships and generalise from sparse data. We demonstrate the effectiveness of our proposed ILP-based approach through an experimental evaluation focused on detecting neurodegenerative diseases from fundus images. By extending our previous work on neurodegenerative disease detection, including Alzheimer’s disease, Parkinson’s disease, and vascular dementia disease, we achieve improved explainability in identifying these diseases using fundus images collected from the UK Biobank dataset. The logical representation and reasoning inherent in ILP enhances the interpretability of the detection process. The results highlight the efficacy of ILP in few-shot learning scenarios, showcasing its remarkable generalisation performance compared to a range of other machine learning algorithms. This research contributes to the field of few-shot learning using ILP and paves the way for addressing challenging real-world problems.
Learning settings are crucial for most Inductive Logic Programming (ILP) systems to learn efficiently. Hypothesis spaces can be huge, and ILP systems take a long time to output solutions or even cannot terminate within time limits. Therefore, users must set suitable learning settings for each ILP task to bring the best performance of the system. However, most users struggle to set appropriate settings for the task they see for the first time. In this paper, we propose a method to make an ILP system more adaptable to tasks with weak learning biases. In particular, we attempt to learn efficient strategies for an ILP system using reinforcement learning (RL). We use Popper, a state-of-the-art ILP system that implements the concept of learning from failures (LFF). We introduce RL-Popper, which divides the hypothesis space into subspaces more minutely than Popper. RL is used to learn the efficient search order of the divided spaces. We provide the details of RL-Popper and show some empirical results.
Markov Logic Networks (MLNs) combine relational specifications with probabilistic learning and reasoning. Although they can be specified without reference to a particular domain, their performance across domains of different sizes is generally unfavorable. Domain-size Aware Markov Logic Networks (DA-MLNs) address this issue by scaling down weight parameters based on the domain size. This allows for faster learning by training models on a random sample of the original domain. DA-MLNs also enable transferring models between naturally occurring domains of different sizes. This study proposes a combination of functional gradient boosting and weight scaling for single-target structure learning on large domains. It also evaluates performance and runtime on two benchmark domains of contrasting sizes. The results demonstrate that training a DA-MLN from a sample can significantly reduce learning time for large domains with minor performance trade-offs, which decrease with the size of the original domain. Additionally, the study explores how scaling reacts to varying domain sizes in a synthetic social network domain. It is observed that DA-MLNs outperform unscaled MLNs when the number of connections between individuals grows with domain size, but perform worse when the number of connections remains constant. This justifies the use of unscaled MLNs when sampling isolated subcommunities in areas such as social sciences research.
