Browsing by Author "Six, Daniel"

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    Towards advancing modular AI planning systems : insights from the PlanX toolbox redesign
    (2025) Six, Daniel
    As ai planning systems grow in complexity and are deployed in increasingly diverse application contexts, existing frameworks often struggle with modularity, reusability, and tool interoperability. These limitations hinder the integration of heterogeneous planning tools and reduce the adaptability of planning systems to evolving requirements. This thesis addresses these issues by redesigning the PlanX Toolbox, an open-source framework for composing ai planning functionalities based on soa and cbse principles. Through a detailed architectural analysis of the original PlanX system, several constraints were identified, including rigid service interactions, limited extensibility, and tightly coupled parsing, converting, and planning components. In response, a redesigned architecture was developed featuring a modular Generation Unit that unifies conversion and plan generation while maintaining parsing as an independent component. This restructuring improves the separation of concerns and supports more flexible composition of planning workflows. The redesign also introduces standardized message structures and dynamic planner selection, simplifying the integration of new components. The evaluation includes the integration of the Fast Downward planner, which revealed both the potential and the limitations of reusing standardized input formats like pddl across diverse planning backends. While conversion and generation could be modularized effectively, parser reusability remained constrained by tool-specific assumptions and preprocessing steps. These findings highlight key insights into the practical challenges of building modular ai planning systems: true composability requires not only standardized interfaces but also architectural awareness of planner internals. By proposing and validating a refined system architecture, this thesis provides actionable design patterns for enhancing maintainability, extensibility, and integration in ai planning systems. The findings offer both theoretical and practical contributions to advancing modular ai planning frameworks.