This thesis investigates the application of artificial intelligence and, specifically, Inductive Logic Programming (ILP) to uncover interpretable patterns of operational success within the long-haul air transport industry. Motivated by the persistent fragility and contested viability of the long-haul low-cost carrier (LHLCC) business model, the research aims to support more transparent, data-driven strategic insight in a sector characterised by complexity, volatility, and tight margins.

Using Revenue per Equivalent Block Hour (REB) as a unifying performance metric, the study analyses two major long-haul markets: Southeast Asia and the North Atlantic. It adopts a by-publication structure, comprising three papers. The first paper establishes the REB-based performance landscape of Southeast Asia, highlighting the limitations of conventional benchmarking. The second introduces ILP as a novel, symbolic AI method for aviation research, demonstrating its capacity to extract high-performing operational patterns that align with domain literature. The third paper conducts an REB analysis and applies ILP to the North Atlantic market over multiple years, validating its robustness across different strategic environments and introducing the hypothesis coverage subset (HCS) technique to enhance interpretability.

Together, these studies show that ILP can generate human-readable, relational rules that not only explain successful performance but offer a viable tool for strategic reasoning. Additionally, the REB analysis of the two largest LHLCC markets unveiled the disruptive nature LHLCCs have. LHLCC activity forces incumbents to adopt some of the same low-cost strategies to increase revenue efficiency in the face of lower fares. The research contributes to both air transport management and interpretable AI by providing a methodologically rigorous and context-sensitive framework for performance analysis. Practically, it equips industry stakeholders with a transparent approach to identifying high-leverage operational configurations. The thesis positions ILP as a promising decision-support tool for navigating complexity in aviation and offers a broader argument for integrating explainable AI into the strategic toolkit of transport analysts and researchers.