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Hierarchies are the most common structure used to understand the world better. In galaxies, for instance, multiple-star systems are organised in a hierarchical system. Then, governmental and company organisations are structured using a hierarchy, while the Internet, which is used on a daily basis, has a space of domain names arranged hierarchically. Since Artificial Intelligence (AI) planning portrays information about the world and reasons to solve some of world's problems, Hierarchical TaskarXiv:1403.7426v1 fatcat:alqqtduit5gyxodbqgbjnvsoue
more »... Hierarchical Task Network (HTN) planning has been introduced almost 40 years ago to represent and deal with hierarchies. Its requirement for rich domain knowledge to characterise the world enables HTN planning to be very useful, but 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, attention attracts the ability of hierarchical planning to truly cope with the requirements of applications from the real world. We propose a framework-based approach to remedy this situation. First, we 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 to interpret 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, performance 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.
Artificial Intelligence (AI) planning is a flourishing research and development discipline that provides powerful tools for searching a course of action that achieves some user goal. While these planning tools show excellent performance on benchmark planning problems, they represent challenging software systems when it comes to their use and integration in real-world applications. In fact, even in-depth understanding of their internal mechanisms does not guarantee that one can successfully setarXiv:2102.10985v1 fatcat:cg6kszockjd3batnjw56tyxyui
more »... n successfully set up, use and manipulate existing planning tools. We contribute toward alleviating this situation by proposing a service-oriented planning architecture to be at the core of the ability to design, develop and use next-generation AI planning systems. We collect and classify common planning capabilities to form the building blocks of the planning architecture. We incorporate software design principles and patterns into the architecture to allow for usability, interoperability and reusability of the planning capabilities. Our prototype planning system demonstrates the potential of our approach for rapid prototyping and flexibility of system composition. Finally, we provide insight into the qualitative advantages of our approach when compared to a typical planning tool.
Our description is based on the paper in which we introduced the HTN planning approach to solving deployment (Georgievski et al. 2017 ). ... Finally, HTN planning problems with varying difficulty can be generate automatically by manipulating the states and ports of components, as described in (Georgievski et al. 2017) . ). ...arXiv:2104.10027v2 fatcat:k2xtuvgbvbevrp4a23qtpzuhhy
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. Todoi:10.1109/soca.2017.19 dblp:conf/soca/GeorgievskiNL017 fatcat:2bbaqrlycnhr3nwqzmbfrxgotq
more »... sources. 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. Proposed solution As a necessary direction to automate the composition of Cloud applications ready for deployment, it appears natural to resort to automated planning  . Planning provides powerful methods for searching in large and complex Cloud infrastructures to find "good" compositions of Cloud ready applications. Applications are composed dynamically, thus services need not to be fixed in advance in scripts and always available (the same holds for the servers of the Cloud). Additionally,
Proceedings of ICT for Sustainability 2016
I. EXTENDED ABSTRACT Cities are significant contributors to global emission of CO 2 . Among city entities, buildings are the largest energy consuming sector and are therefore an important source of CO 2 emissions. A key issue in buildings is that their current energy systems fail to use energy in a smart and sustainable way. That is, energy-use policies (e.g., with regard to lights in public spaces) and individuals energy behaviours (e.g., switching lights on/off in personal spaces) are oftendoi:10.2991/ict4s-16.2016.34 fatcat:egjvuyso25djfcqhprpz5too7y
more »... spaces) are often inefficient. In the context of the BeijIng Groningen Smart energy cities (BIGS) project, 1 we aim to optimise building energy systems through the automation of certain aspects of the building energy system (phase 1 of project, 2015-2016), while using this platform to engage end-users by interfacing them with the automated solution (phase 2 of the project, 2017-2018). The goal of the BIGS project is to create efficient and sustainable future smart energy systems through the design and implementation of an intelligent ICT platform that includes sensors, actuators and techniques for context reasoning and automation. Importantly, instead of just silently taking over energy decisions through automation, our approach focuses on how to get users involved as well. Therefore, we aim to understand people's energy behaviours and to create policies that improve people's behaviours and sustainability by using appropriate ICT solutions. This necessitates a holistic approach that takes into account knowledge on information technology and reasoning, human decision making (motivations, values, incentives) and human behaviour. In the first phase of BIGS, we focus on testing an ICT platform with automation in a real environment, and understanding people's awareness of and attitudes toward the use of automation. For this purpose, we use the restaurant of the Bernoulliborg building at the Zernike campus of the University of Groningen, The Netherlands, to model and control the lamps automatically. The restaurant covers an area of 251.50 m 2 with a capacity of 200 sitting places. The restaurant has glass walls from three sides, enabling a significant amount of natural light to come through when the weather conditions allow for it. The restaurant area is used for lunch and, outside 1 http://jstp-bigs.com/ lunch hours, the area is used by staff, students or other visitors for working, meeting, or other purposes. We use movement sensors, a natural light sensor and the physical properties of the restaurant to model and interpret the context. We then employ an artificial intelligence planning technique to automatically coordinate the use of lamps given the current context and the goal to keep user comfortable under constraints of energy efficiency. The control of lamps is enabled by actuators attached to each lamp. We deployed and run the solution for several months. When compared to the previous way of controlling the lamps in the restaurant, which involves turning on and off lamps manually at fixed points in the morning and evening of each day, our solution achieves savings in the order of 80% of energy  . Within this context, we also performed preliminary user studies. These studies indicated that most restaurant occupants self-reported that they are aware of sustainability issues and engage in environmentally friendly behaviours. In addition, most of those restaurant occupants indicated that they would accept an intelligent system as the one we proposed and that they would be satisfied with such a solution. However, with regard to the actual solution, only about one third of the occupants indicated being aware of the solution, and their involvement in and influence on the solution was low. We believe this lack of awareness and involvement is a commonly missed opportunity, considering that behavioural research indicated that making people aware of their involvement in sustainable behaviours (e.g., their acceptance and contribution to a solution like this) predict future pro-environmental behaviours and attitudes, particularly for those who indicate to value the environment ,  . In the second phase of BIGS, we will address this issue and focus on identifying energy-related preferences, needs and behaviours of individuals, and look at how interfacing ICT solutions with end-users could motivate and empower people in their everyday use of energy. Moreover, we will work on centering automated techniques around individuals by taking into account their needs, preferences, and motivations, which is essential for the creation of successful and smart energy systems that truly empower end-users. This will enable us to understand the needs of people during the design and develop-4th International Conference on ICT for Sustainability (ICT4S 2016)
Ilche Georgievski for his advice during critical stages of my thesis, providing me resources and giving clear remarks and feedback during the implementation and report writing. ... Rule based Model This next paper from the authors Georgievski et al. ...doi:10.18419/opus-10410 fatcat:ucleklk3irhfth23rrvtakeda4
I really enjoyed the joint work with my colleagues Viktoriya Degeler, Ilche Georgievski, Alexander Lazovik, and Tuan Anh Nguyen; it was really fun to work together on a bigger project and mess up with ...doi:10.1016/j.physa.2015.12.080 fatcat:mxed7e27gbdghhfpv4uqot3o7a