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In this work, we propose a new distributed, dynamic, and recursive planning approach able to consider the hierarchical nature of the holonic agent and the unpredictable evolution of its behaviour. For each new version of the holonic agent, introduced because of the agent members obtaining new roles to achieve new goals and adapt to the changing environment, the approach generates a new plan that can solve the new planning problem associated with this new version against which the plans, executed by the holonic agent, become obsolete. To do this, the approach starts by generating sub-plans capable of solving the planning subproblems associated with the groups of the holonic agent at its different levels. It then recursively links the sub-plans, according to their hierarchical and behavioural dependency, to obtain a global plan. To generate the sub-plans, the approach exploits the behavioural model of the holonic agent's groups, thereby minimising the computation rate imposed by other multi-agent planning methods. In our work, we have used a concrete case to show and illustrate the usefulness of our approach. [ABSTRACT FROM AUTHOR]

Featured Application: Fully automated and personalised radiotherapy treatment planning. Automated planning (AP) uses common protocols for all patients within a cancer site. This work investigated using machine learning to personalise AP protocols for fully individualised planning. A 'Pareto guided automated planning' (PGAP) solution was used to generate patient-specific AP protocols and gold standard Pareto navigated reference plans (MCOgs) for 40 prostate cancer patients. Anatomical features related to geometry were extracted and two ML approaches (clustering and regression) that predicted patient-specific planning goal weights were trained on patients 1–20. For validation, three plans were generated for patients 21–40 using a standard site-specific AP protocol based on averaged weights (PGAPstd) and patient-specific AP protocols generated via regression (PGAP-MLreg) and clustering (PGAP-MLclus). The three methods were compared to MCOgs in terms of weighting factors and plan dose metrics. Results demonstrated that at the population level PGAPstd, PGAP-MLreg and PGAP-MLclus provided excellent correspondence with MCOgs. Deviations were either not statistically significant (p ≥ 0.05), or of a small magnitude, with all coverage and hotspot dose metrics within 0.2 Gy of MCOgs and OAR metrics within 0.7% and 0.4 Gy for volume and dose metrics, respectively. When compared to PGAPstd, patient-specific protocols offered minimal advantage for this cancer site, with both approaches highly congruent with MCOgs. [ABSTRACT FROM AUTHOR]

Process understanding and process monitoring are of great importance in production in order to control processes and guarantee a high quality. Demanding customer requirements with an increasing number of variants pose an even greater challenge to the quality of the processes, as this must be maintained at the highest level even in the event of process changes. In addition, new regulations and standards require process data to be recorded and stored, especially in manufacturing environments for medical and safety equipment (e.g., surgical instruments, camera systems in the automotive industry). Continuous variations in production processes and changes to products and the production system mean that the planning effort required to implement process monitoring has become vast. This is where automated planning and decision support systems become important. They are able to manage the complexity arising from alternative solutions and present suitable alternatives to the user. This article deals with the computer-aided identification of assembly features, which influence process monitoring and the generation of production system-neutral tasks for process monitoring. Computer-aided feature recognition methods were used to derive features from three-dimensional models. Furthermore, a skill-based approach was used to formulate tasks for process monitoring. This publication thus aims at the automated and product-specific generation of processes for process monitoring. [ABSTRACT FROM AUTHOR]

Reinforcement Learning (RL) comes with the promise of automating network management. However, due to its trial-and-error learning approach, model-based RL (MBRL) is not applicable in some network management scenarios. This paper explores the potential of using Automated Planning (AP) to achieve this MBRL in the functional areas of network management. In addition, a comparison of several integration strategies of AP and RL is depicted. We also describe an architecture that realizes a cognitive management control loop by combining AP and RL. Our experiments evaluate on a simulated environment evidence that the combination proposed improves model-free RL but demonstrates lower performance than Deep RL regarding the reward and convergence time metrics. Nonetheless, AP-based MBRL is useful when the prediction model needs to be understood and when the high computational complexity of Deep RL can not be used. [ABSTRACT FROM AUTHOR]

Flexible control strategies are required in industrial scenarios to coordinate the actions of mobile manipulators (e.g., robots and humans). Temporal planning approaches can be used as such control strategies because they can generate those actions for the agents that must be executed to reach the goals, from any given state of the world. To deploy such approaches, planning models must be formulated. Although available in the literature, these models are not generic enough such that they can be easily transferred to new use cases. In this work, a generic industrial scenario is derived from real scenarios. For this scenario, a generic planning problem is developed. To demonstrate their generality, the two constructs are configured for a new scenario, where custom grippers are assembled. Lastly, a validation methodology is developed for the generic planning problem. The results show that the generic industrial scenario and the generic planning problem can be easily instantiated for new use cases, without any new modelling. Further, the proposed validation methodology guarantees that these planning problems are complete enough to be used in industrial use cases. The generic scenario, the planning problems, and the validation methodology are proposed as standards for use when deploying temporal planning in industrial scenarios with mobile manipulators. [ABSTRACT FROM AUTHOR]

Automated planning is a well-established field of artificial intelligence (AI), with applications in route finding, robotics and operational research, among others. The task of developing a plan is often solved by finding a path in a graph representing the search domain; a robust plan consists of numerous paths that can be chosen if the execution of the best (optimal) one fails. While robust planning for a single entity is rather simple, development of a robust plan for multiple entities in a common environment can lead to combinatorial explosion. This paper proposes a novel hybrid approach, joining heuristic search and the wavefront algorithm to provide a plan featuring robustness in areas where it is needed, while maintaining a low level of computational complexity. [ABSTRACT FROM AUTHOR]

Simple Summary: Stereotactic radiosurgery (SRS) and hypofractionated stereotactic radiotherapy (SRT) are promising treatment options for patients with multiple brain metastases in the current era of personalized medicine. Recent international guidelines propose SRS also in patients with more than three brain metastases with low-volume disease. Optimal treatment quality with sparing of healthy brain tissue is essential to avoid SRS/SRT complications such as brain necrosis. The aim of this study was to compare linac (linear accelerator)-based SRS/SRT plan quality of automated planning, intensity modulated radiotherapy (IMRT), volumetric modulated arc radiotherapy (VMAT) and manually planned dynamic conformal arc (DCA) plans as well as single- and multiple-isocenter techniques. We found that automated planning with DCA or IMRT can make linac-based SRS/SRT plan quality with single isocenter comparable with a manually planned DCA plan with a separate isocenter for each metastasis. The purpose was to compare linac-based stereotactic radiosurgery and hypofractionated radiotherapy plan quality of automated planning, intensity modulated radiotherapy (IMRT) and manual dynamic conformal arc (DCA) plans as well as single- and multiple-isocenter techniques for multiple brain metastases (BM). For twelve patients with four to ten BM, seven non-coplanar linac-based plans were created: a manually planned DCA plan with a separate isocenter for each metastasis, a single-isocenter dynamic IMRT plan, an automatically generated single-isocenter volumetric modulated arc radiotherapy (VMAT) plan, four automatically generated single-isocenter DCA plans with three or five couch angles, with high or low sparing of normal tissue. Paddick conformity index, gradient index (GI), mean dose, total V12Gy and V5Gy of uninvolved brain, number of monitor units (MUs), irradiation time and pass rate were compared. The GI was significantly higher for VMAT than for separate-isocenter, IMRT, and all automatically generated plans. The number of MUs was lowest for VMAT, followed by automatically generated DCA and IMRT plans and highest for manual DCA plans. Irradiation time was the shortest for automatically planned DCA plans. Automatically generated linac-based single-isocenter plans for multiple BM reduce the number of MUs and irradiation time with at least comparable GI and V5Gy relative to the reference separate-isocenter DCA plans. [ABSTRACT FROM AUTHOR]

As systems grow more automatized, the human operator is all too often overlooked. Although human-robot interaction (HRI) can be quite demanding in terms of cognitive resources, the mental states (MS) of the operators are not yet taken into account by existing systems. As humans are no providential agents, this lack can lead to hazardous situations. The growing number of neurophysiology and machine learning tools now allows for efficient operators' MS monitoring. Sending feedback on MS in a closed-loop solution is therefore at hand. Involving a consistent automated planning technique to handle such a process could be a significant asset. This perspective article was meant to provide the reader with a synthesis of the significant literature with a view to implementing systems that adapt to the operator's MS to improve human-robot operations' safety and performance. First of all, the need for this approach is detailed regarding remote operation, an example of HRI. Then, several MS identified as crucial for this type of HRI are defined, along with relevant electrophysiological markers. A focus is made on prime degraded MS linked to time-on-task and task demands, as well as collateral MS linked to system outputs (i.e., feedback and alarms). Lastly, the principle of symbiotic HRI is detailed and one solution is proposed to include the operator state vector into the system using a mixed-initiative decisional framework to drive such an interaction. [ABSTRACT FROM AUTHOR]

Using Automated Planning for the high level control of robotic architectures is becoming very popular thanks mainly to its capability to define the tasks to perform in a declarative way. However, classical planning tasks, even in its basic standard Planning Domain Definition Language (PDDL) format, are still very hard to formalize for non expert engineers when the use case to model is complex. Human Robot Interaction (HRI) is one of those complex environments. This manuscript describes the rationale followed to design a planning model able to control social autonomous robots interacting with humans. It is the result of the authors' experience in modeling use cases for Social Assistive Robotics (SAR) in two areas related to healthcare: Comprehensive Geriatric Assessment (CGA) and non-contact rehabilitation therapies for patients with physical impairments. In this work a general definition of these two use cases in a unique planning domain is proposed, which favors the management and integration with the software robotic architecture, as well as the addition of new use cases. Results show that the model is able to capture all the relevant aspects of the Human-Robot interaction in those scenarios, allowing the robot to autonomously perform the tasks by using a standard planning-execution architecture. [ABSTRACT FROM AUTHOR]