Coordinating services embedded everywhere via hierarchical planning

The spaces we live in are provided with different devices and technologies, such as sensors for recognising our presence. The aim of such spaces is to improve our comfort, productivity, and even reduce our energy bills. The problem with fulfilling the aim is that devices alone cannot do much to achieve such difficult goals. People would also have problems in manually searching for the best situation accomplishing their needs. A way to deal with this problem is to coordinate devices automatically. For example, our home can autonomously figure out that some lamps can be turned off because the living room has enough natural light and the activity we are currently doing requires a low light level. The benefits are improved comfort and a reasonable amount of energy saved. We therefore explore the possibilities of using a system based on automated planning. This planning produces a set of device services, such as turn off a lamp, that achieves a given goal. We use a method, called hierarchical planning, which enables us to organise the knowledge we have about spaces and devices in hierarchical forms. We show that planning is suitable for this kind of problems by using hierarchical planning to save energy in the Bernoulliborg building at the University of Groningen. The results show energy and money savings, and that people are satisfied with our system. We also improve the system and show that even more money can be saved without sacrificing the well-being of people if we can buy energy from several energy providers

Cloud Ready Applications Composed via HTN Planning

Modern software applications are increasingly deployed and distributed on infrastructures in the Cloud, and then offered as a service. Before the deployment process happens, these applications are being manually - or with some predefined scripts - composed from various smaller interdependent components. With the increase in demand for, and complexity of applications, the composition process becomes an arduous task often associated with errors and a suboptimal use of computer resources. To alleviate such a process, we introduce an approach that uses planning to automatically and dynamically compose applications ready for Cloud deployment. The industry may benefit from using automated planning in terms of support for product variability, sophisticated search in large spaces, fault tolerance, near-optimal deployment plans, etc. Our approach is based on Hierarchical Task Network (HTN) planning as it supports rich domain knowledge, component modularity, hierarchical representation of causality, and speed of computation. We describe a deployment using a formal component model for the Cloud, and we propose a way to define and solve an HTN planning problem from the deployment one. We employ an existing HTN planner to experimentally evaluate the feasibility of our approach

Fehlervermeidung und Prozessmonitoring in komplexen und dynamischen Großschadenslagen

In dieser Arbeit wird der Frage nachgegangen, inwieweit eine Computerunterstützung in komplexen, kritischen Arbeitsbereichen zu einer Harmonisierung der Handlungsabläufe und zur Verhinderung von Fehlern (z. B. Gedächtnisfehlern) beitragen kann. Als Beispiel eines komplexen und dynamischen Arbeitsbereiches wird das Agieren von Behörden und Organisationen mit Sicherheitsaufgaben (BOS) in Großschadenslagen betrachtet. Motiviert durch den großen Erfolg von Checklisten in der Luftfahrt und in der Intensivmedizin, wird in dieser Arbeit eine systematische Übertragung des Checklisten-Prinzips auf den deutschen BOS-Bereich vorgeschlagen. Hierfür wird ein Rahmenwerk eines intelligenten elektronischen Checklisten-Assistenzsystems erarbeitet, das auch für alternative, soziotechnische Arbeitssysteme mit dem BOS-Bereich ähnlichen Merkmalen hilfreich zur Fehlervermeidung eingesetzt werden kann

Personalized conciliation of clinical guidelines for comorbid patients through multi-agent planning

[EN] The conciliation of multiple single-disease guidelines for comorbid patients entails solving potential clinical interactions, discovering synergies in the diagnosis and the recommendations, and managing clinical equipoise situations. Personalized conciliation of multiple guidelines considering additionally patient preferences brings some further difficulties. Recently, several works have explored distinct techniques to come up with an automated process for the conciliation of clinical guidelines for comorbid patients but very little attention has been put in integrating the patient preferences into this process. In this work, a Multi-Agent Planning (MAP) framework that extends previous work on single-disease temporal Hierarchical Task Networks (HTN) is proposed for the automated conciliation of clinical guidelines with patient-centered preferences. Each agent encapsulates a single-disease Computer Interpretable Guideline (CIG) formalized as an HTN domain and conciliates the decision procedures that encode the clinical recommendations of its CIG with the decision procedures of the other agents' CIGs. During conciliation, drug-related interactions, scheduling constraints as well as redundant actions and multiple support interactions are solved by an automated planning process. Moreover, the simultaneous application of the patient preferences in multiple diseases may potentially bring about contradictory clinical decisions and more interactions. As a final step, the most adequate personalized treatment plan according to the patient preferences is selected by a Multi-Criteria Decision Making (MCDM) process. The MAP approach is tested on a case study that builds upon a simplified representation of two real clinical guidelines for Diabetes Mellitus and Arterial Hypertension.This work has been partially supported by Spanish Government Projects MINECO TIN2014-55637-C2-2-R and TIN2015-71618-R.Fernández-Olivares, J.; Onaindia De La Rivaherrera, E.; Castillo Vidal, L.; Jordán, J.; Cózar, J. (2019). Personalized conciliation of clinical guidelines for comorbid patients through multi-agent planning. Artificial Intelligence in Medicine. 96:167-186. https://doi.org/10.1016/j.artmed.2018.11.003S1671869

Temporal and Hierarchical Models for Planning and Acting in Robotics

The field of AI planning has seen rapid progress over the last decade and planners are now able to find plan with hundreds of actions in a matter of seconds. Despite those important progresses, robotic systems still tend to have a reactive architecture with very little deliberation on the course of the plan they might follow. In this thesis, we argue that a successful integration with a robotic system requires the planner to have capacities for both temporal and hierarchical reasoning. The former is indeed a universal resource central in many robot activities while the latter is a critical component for the integration of reasoning capabilities at different abstraction levels, typically starting with a high level view of an activity that is iteratively refined down to motion primitives. As a first step to carry out this vision, we present a model for temporal planning unifying the generative and hierarchical approaches. At the center of the model are temporal action templates, similar to those of PDDL complemented with a specification of the initial state as well as the expected evolution of the environment over time. In addition, our model allows for the specification of hierarchical knowledge possibly with a partial coverage. Consequently, our model generalizes the existing generative and HTN approaches together with an explicit time representation. In the second chapter, we introduce a planning procedure suitable for our planning model. In order to support hierarchical features, we extend the existing Partial-Order Causal Link approach used in many constraintbased planners, with the notions of task and decomposition. We implement it in FAPE (Flexible Acting and Planning Environment) together with automated problem analysis techniques used for search guidance. We show FAPE to have performance similar to state of the art temporal planners when used in a generative setting. The addition of hierarchical information leads to further performance gain and allows us to outperform traditional planners. In the third chapter, we study the usual methods used to reason on temporal uncertainty while planning. We relax the usual assumption of total observability and instead provide techniques to reason on the observations needed to maintain a plan dispatchable. We show how such needed observations can be detected at planning time and incrementally dealt with by considering the appropriate sensing actions. In a final chapter, we discuss the place of the proposed planning system as a central component for the control of a robotic actor. We demonstrate how the explicit time representation facilitates plan monitoring and action dispatching when dealing with contingent events that require observation. We take advantage of the constraint-based and hierarchical representation to facilitate both plan-repair procedures as well opportunistic plan refinement at acting time