HTN planning: Overview, comparison, and beyond

Hierarchies are one of the most common structures used to understand and conceptualise the world. Within the field of Artificial Intelligence (AI) planning, which deals with the automation of world-relevant problems, Hierarchical Task Network (HTN) planning is the branch that represents and handles hierarchies. In particular, the requirement for rich domain knowledge to characterise the world enables HTN planning to be very useful, and also to perform well. However, the history of almost 40 years obfuscates the current understanding of HTN planning in terms of accomplishments, planning models, similarities and differences among hierarchical planners, and its current and objective image. On top of these issues, the ability of hierarchical planning to truly cope with the requirements of real-world applications has been often questioned. As a remedy, we propose a framework-based approach where we first provide a basis for defining different formal models of hierarchical planning, and define two models that comprise a large portion of HTN planners. Second, we provide a set of concepts that helps in interpreting HTN planners from the aspect of their search space. Then, we analyse and compare the planners based on a variety of properties organised in five segments, namely domain authoring, expressiveness, competence, computation and applicability. Furthermore, we select Web service composition as a real-world and current application, and classify and compare the approaches that employ HTN planning to solve the problem of service composition. Finally, we conclude with our findings and present directions for future work. In summary, we provide a novel and comprehensive viewpoint on a core AI planning technique.<br/

Combining Activity Recognition and AI Planning for Energy-Saving Offices

Energy-saving offices require autonomous and optimised control of integrated devices and appliances with the objective of saving energy while the occupant comfort and productivity are preserved. We propose an approach that analyses and controls an office space and accounts for the objectives of energy-saving offices. The approach considers ontology-based occupant activity recognition using simple sensors to process the context information, and employs Artificial Intelligence planning to control appliances. The approach is evaluated in a semi-simulated setting. The activity recognition strategy is tested in an actual living lab and shows recognising accuracy of about 80%. The planning technique is able to cope efficiently under a simulated and increasing number of offices and recognised activities. The overall solution shows intriguing potential for energy saving in the order of 70%, given mostly sunny days and a provisional set of devices for experimentation.

Energy adaptive buildings:From sensor data to being aware of users

Energie besparen is fundamenteel voor het realiseren van een duurzame energievoorziening. Het besparen van energie draagt bij aan milieudoelstellingen, verbetert de zakelijke positie van landen, en levert werkgelegenheid. Er zijn tal van mogelijkheden voor het behalen van aanzienlijke energiebesparingen in gebouwen gezien individuen en bedrijven gebaat zijn bij energiebesparingen en daardoor zelf de verantwoordelijkheid nemen. Het is bewezen dat het gedrag van gebouwgebruikers een grote impact heeft op de verwarming en ventilatie van ruimtes, en op het energieverbruik van verlichting en huishoudelijke apparaten. Huidige gebouwautomatiseringssystemen kunnen niet overweg met veranderingen in het gedrag van gebruikers en zijn daardoor niet in staat om het energieverbruik terug te dringen met behoud van gebruikerscomfort. Mijn promotieonderzoek wordt gedreven door het doel om een dergelijk energy adaptive building te realiseren dat intelligent systemen aanstuurt en zich aanpast aan de gebruiker en gebruikersactiviteiten door deze te leren, terwijl energieverspilling wordt teruggedrongen. Mijn focus ligt op het ontwikkelen van een framework, beginnende bij de hardware infrastructuur voor sensoren en actuatoren, het verwerken en analyseren van de sensordata, en de nodige informatie over de omgeving en gebruikersactiviteiten verkrijgen zodat het gebouw aangestuurd kan worden. Onze oplossing kan 35% besparen op het totale energieverbruik van een gebouw. Als een succesverhaal, besparen de software systemen zelfs 80% op het energieverbruik van de verlichting in het restaurant van de Bernoulliborg. Wij commercialiseren de resultaten verkregen in ons onderzoek door het oprichten van de start-up SustainableBuildings, een spin-off bedrijf van onze universiteit, om onze oplossing aan te bieden aan kantoorgebouwen.Saving energy is the foundation for achieving a sustainable energy supply. Saving energy contributes to environmental objectives, improves the competitiveness of a country’s businesses, and boosts employment. There are numerous opportunities for achieving significant energy savings in buildings since individuals and businesses have an interest themselves in saving energy and will shoulder the responsibility for doing so.Occupant behaviour has shown to have large impact on space heating and cooling demand, energy consumption of lighting and appliances. Current building automation systems are unable to cope with changes caused by occupants’ behaviour and interaction with the environment, therefore they fail to reduce unnecessary energy consumption while preserving user comfort.My PhD research is driven by the aim of realising such energy adaptive buildings that facilitate intelligent control, that learn and adapt to the building users and their activities, while reducing energy waste. My particular focus is on a framework, going from the hardware infrastructure for sensing and actuating, to processing and analysing sensor data, providing necessary information about the environment and occupants’ activities for the system to produce adaptive control strategies, regulating the environment accordingly.Our solution can save 35% of energy for a single building. As a success story, the software system saves 80 percent on energy spent for lighting in the restaurant of the Bernoulliborg.We are commercialising the results of our research by creating the SustainableBuildings start-up, a spin-off from our university, to offer the solutions to non-residential buildings, first in the Netherlands, and later extending wider

Real-time Multi-scale Smart Energy Management and Optimisation (REMO) for buildings and their district

Energy management systems in buildings and their district today use automation systems and artificial intelligence (AI) solutions for smart energy management, but they fail to achieve the desired results due to the lack of holistic and optimised decision-making. A reason for this is the silo-oriented approach to the decision-making failing to consider cross-domain data. Ontologies, as a new way of processing domain knowledge, have been increasingly applied to different domains using formal and explicit knowledge representation to conduct smart decision-making. In this PhD research, Real-time Multiscale Smart Energy Management and Optimisation (REMO) ontology was developed, as a cross-domain knowledge-base, which consequently can be used to support holistic real-time energy management in districts considering both demand and supply side optimisation. The ontology here, is also presented as the core of a proposed framework which facilitates the running of AI solutions and automation systems, aiming to minimise energy use, emissions, and costs, while maintaining comfort for users. The state of the art AI solutions for prediction and optimisation were concluded through authors involvement in European Union research projects. The AI techniques were independently validated through action research and achieved about 30 - 40 % reduction in energy demand of the buildings, and 36% reduction in carbon emissions through optimisation of the generation mix in the district. The research here also concludes a smart way to capture the generic knowledge behind AI models in ontologies through rule axiom features, which also meant this knowledge can be used to replicate these AI models in future sites. Both semantic and syntactic validation were performed on the ontology before demonstrating how the ontology supports the various use cases of the framework for holistic energy management. Further development of the framework is recommended for the future which is needed for it to facilitate real-time energy management and optimisation in buildings and their district