Designing a PDDL planning domain is an error-prone task, which can result in unsolvable planning tasks or unexpected plans. Existing domain repair methods either rely on a complete plan to identify unsatisfied preconditions or operate without any input plan by compiling the flawed planning task into a new planning task with self-repair actions. In contrast, learning approaches often benefit from a range of input observations to infer domain models. In this paper, we extend the self-repair compilation to also accept as input a variable number of action observations. Experimental results show improved domain repair quality and generally strong performance compared to previous domain repair and learning methods.

ABSTRACT Social Autonomous Robotics aims to deploy robots in scenarios that involve intensive and continuous interaction with humans. To control the behaviour of robotic platforms in such environments, the use of automated planning (AP) within a control architecture has been proposed as an effective mechanism. However, the design of AP models is time‐consuming and typically carried out by domain experts and engineers. A significant amount of knowledge must be acquired in order to properly define the use case description by specifying the different tasks performed by the robot. In this paper, we present DeVPlan , a framework for graphically designing robotic use cases and configuring the platform for the desired execution. DeVPlan provides an interface that allows domain experts, in collaboration with knowledge engineers, to use state transition diagrams to specify the tasks a robot can perform and define recovery strategies for exogenous events that disrupt normal execution. This graphical design is automatically translated into the standard Planning Domain Definition Language (PDDL). Additionally, to facilitate the integration of the AP model with the robot's control architecture, DeVPlan includes a module for generating the configuration files required to set up the control system. The proposed framework has been successfully used to design and deploy two different use cases in a real environment in a retirement home.

Automated Planning (AP) is a problem-solving technique applicable to a wide range of scenarios and goals. It typically requires a complete and accurate description of the planning task expressed in a formal language to generate a solution plan that achieves the goals. However, creating these descriptions can be time-consuming and error-prone, often resulting in unsolvable planning tasks. Planning systems often lack the ability to explain why a task is deemed unsolvable. In this work, we present an integrated knowledge engineering system that allows users to graphically depict AP use cases using transition diagrams, which are automatically converted into a formal language. To facilitate the debugging process, we propose connecting the system with large language models (LLMs) to explore their capabilities in assisting with flawed planning tasks, fixing the model and making the tasks solvable.