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.

Oversubscription planning (OSP) tackles the infeasibility of finding a plan that achieves all goals, due to a limited resource, typically a cost-bound. The objective is to discover a plan under this cost-bound that maximizes the utility. A leading-edge technique in satisficing OSP employs relaxed plans to estimate the cost of achieving goals and focuses planning efforts on goals deemed attainable based on this estimation. However, this approach faces two main challenges: the time required to calculate all estimations can result in no effective goal selection, and using relaxed plans often underestimates the real cost, leading to sets of oversubscribed goals. To address these challenges, our paper studies two solutions: computing the estimations only when needed and using real plans instead of relaxed plans to calculate cost estimations. Experiments show that real plans offer advantages in terms of initial plan utility, but the gap between both approaches narrows when more time is given for plan refinement.

This paper explores how incompletely described tie-break policies can invalidate the experimental results reported in papers on optimal bidirectional heuristic search (BiHS). Experiments usually use a single implementation of an algorithm with its specific tie-break policy. When the tie-breaks are insufficiently described, we show that the results can be irreproducible, vary dramatically under different implementations, and lead to misleading assessments of an algorithm’s performance. To ensure reproducible and representative results, papers should either provide a description of the algorithm’s implementation, i.e., the complete tie-break policy, or alternatively, give results as a summary statistic representative of all possible tie-break implementations. We developed a software tool for this purpose.

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.

The introduction of robots in the real world requires previous evaluation of the envisaged performance. Factors like usability, user experience, social acceptance, or societal impact, among others, have been taken into account in evaluation frameworks defined during the last years. However, one of the most important factors that need to be evaluated in any kind of interaction is whether all the users are able to work with the system with the same opportunities and easiness, and none of the current human–robot interaction (HRI) evaluation frameworks include this factor yet. This article proposes an extension of a popular HRI evaluation framework, including accessibility as a new evaluation factor. The proposed approach, named AUSUS, considers Accessibility, Usability, Social acceptance, User experience, and Societal impact. This article presents a use case of the framework, which is evaluated through a socially assistive robotic platform created to perform comprehensive geriatric assessment: the CLARC system. The details of the evaluation process in a hospital and a retirement home are reported, and the main difficulties and recommendations of using AUSUS are discussed.

In this paper we present techniques for reasoning natively with quantitative/qualitative interval constraints in statebased PDDL planners. While these are considered important in modeling and solving problems in timeline based planners; reasoning with these in PDDL planners has seen relatively little attention, yet is a crucial step towards making PDDL planners applicable in real-world scenarios, such as space missions. Our main contribution is to extend the planner OPTIC to reason natively with Allen interval constraints. We show that our approach outperforms both MTP, the only PDDL planner capable of handling similar constraints and a compilation to PDDL 2.1, by an order of magnitude. We go on to present initial results indicating that our approach is competitive with a timeline based planner on a Mars rover domain, showing the potential of PDDL planners in this setting.

The International Planning Competition is organized in the context of the International Conference on Automated Planning and Scheduling (ICAPS) and it is considered a reference source for the planning and scheduling community. The competition is typically organized every two years and deals with relevant issues for the community such as the definition of evaluation standards, the publication of benchmarks and the collection and dissemination of data about state-of-the-art planners. This paper focuses on the deterministic part, the longest-running part of the International Planning Competition. The paper describes its format, the participants, the selection of benchmarks and the generated results accompanied with analysis from different perspectives. The paper also examines the results of a brand new track created to explore the potential of planners that exploit the power of multi-core processors. Overall, the results of the competition indicate significant progress with respect to previous competitions, but they also reveal that some issues remain open and need further research, such as the coverage of temporal planners when concurrency is required and the performance in the multi-core track. As a novelty, all the data and the software generated for running the competition have been made publicly available allowing researchers to reproduce the competition and to carry out different analysis of the results.

In recent years, Automated Planning (AP) has experienced important advances. In this study we apply such advances to the field of Mobile Assistive Robots (MAR). In particular, we propose the use of AP to implement the deliberative step between observation and action execution in MAR. First, we analyze the requirements that allow a MAR to plan navigation and manipulation actions in near real time. The intention is to build the foundation for a planning module within the Simultaneous User Learning and TAsk executioN (SULTAN) architecture, allowing a MAR to perform Daily Life Activities (DLA) in humanlike environments. Second, we apply AP techniques in fully observable, deterministic and static simulated environments with a single MAR. In addition, we analyze and compare the best available satisficing automated planners. The selected planners participate in several experiments to obtain plans for a Planning Domain Definition Language (PDDL) based on the Tidybot domain. Finally, in order to know how competitive the selected planners are, we compare the experimental results in detail.