Resources

Below are some tangible outputs of my research, including software, conceptual models, domain models and datasets, and lecture notes.

Software

• SH Planning System
  • Purpose: SH is an AI planning system based on state-based Hierarchical Task Network (HTN) planning. As a domain-independent planner, it can handle various planning problem descriptions without requiring modifications to its underlying algorithms.
  • Usage: SH can be used by researchers, practitioners, students, and anyone needing a modern and robust HTN planning system. Its versatility makes it suitable for a wide range of planning problems across multiple domains.
  • [Code] [Documentation] [Paper]
• PlanX Toolbox
  • Purpose: PlanX Toolbox is a collection of planning services for building and integrating AI planning systems. It includes one messaging component and multiple planning services, such as modelling, visualisation, system monitoring, parsing, converting, solving, and plan validation.
  • Usage: The toolbox is aimed at researchers and developers who need a simplified development of advanced AI planning systems. It is well-suited for composing AI planning systems that need to be integrated into larger software architectures.
  • [Code] [Documentation] [Demo] [Paper]
• P-Space
  • Purpose: P-Space is a centralised platform that aggregates AI planning software, such as planners and domain modellers. It allows users to search for planning tools and share information developed software. The platform aims at fostering collaboration among AI planning researchers and practitioners.
  • Usage: P-Space is designed for researchers, practitioners, and students looking for planning software suited for their specific tasks, or wishing to share information about available tools.
  • [Code] [Documentation] [Paper]
• D-Space
  • Purpose: D-Space is a centralised platform for storing, validating, and viewing planning domain models. Its provides a framework for documenting domain models through value cards, enabling transparency and standardisation across the planning community.
  • Usage: D-Space supports researchers, practitioners, and students in searching for or sharing planning domain models. The platform stores and validates domain models specified in PDDL, HPDL, and HDDL.
  • [Code] [Documentation] [Paper]

Conceptual Models

• PlanXFlow
  • Purpose: PlanXFlow is a software development life cycle designed for engineering AI planning systems. PlanXFlow provides a structured, waterfall-like yet iterative approach tailored to the unique challenges in AI planning.
  • Features: This life cycle consists of ten phases, guiding practitioners and researchers from the conceptualisation of AI planning systems to their deployment and beyond. It cores features include adaptability, scaling, transparency, and future-proof.
  • [Model] [Paper]
• D2F
  • Purpose: D2F is conceptual framework characteristing features of planning domain models. It can impact the design, development, and applicability of AI planning systems in real-world settings.
  • Features: The framework provides a common terminology, categories of features, and a broad range of planning features suitable for real-world planning domains.
  • [Model] [Paper]
• Planomics
  • Purpose: Planomics is a framework for planning functionalities. These functionalities are basic, distinct blocks that, when interconnected, form an ecosystem, that is, an advanced AI planning system.
  • Features: Planomics include a wide spectrum of functionalities, including modelling and parsing planning problems, plan generation and execution, data management, system management, and system monitoring. Furthermore, Planomics categorises planning functionalities into classes, indicating their role within the ecosystem. It also positions these functionality classes in terms of their contribution to the capability and evolution of AI planning system capability.
  • [Model] [Paper]