SAT based Enforcement of Domotic Effects in Smart Environments

The emergence of economically viable and efficient sensor technology provided impetus to the development of smart devices (or appliances). Modern smart environments are equipped with a multitude of smart devices and sensors, aimed at delivering intelligent services to the users of smart environments. The presence of these diverse smart devices has raised a major problem of managing environments. A rising solution to the problem is the modeling of user goals and intentions, and then interacting with the environments using user defined goals. `Domotic Effects' is a user goal modeling framework, which provides Ambient Intelligence (AmI) designers and integrators with an abstract layer that enables the definition of generic goals in a smart environment, in a declarative way, which can be used to design and develop intelligent applications. The high-level nature of domotic effects also allows the residents to program their personal space as they see fit: they can define different achievement criteria for a particular generic goal, e.g., by defining a combination of devices having some particular states, by using domain-specific custom operators. This paper describes an approach for the automatic enforcement of domotic effects in case of the Boolean application domain, suitable for intelligent monitoring and control in domotic environments. Effect enforcement is the ability to determine device configurations that can achieve a set of generic goals (domotic effects). The paper also presents an architecture to implement the enforcement of Boolean domotic effects, and results obtained from carried out experiments prove the feasibility of the proposed approach and highlight the responsiveness of the implemented effect enforcement architectur

Automated Service Composition Using AI Planning and Beyond

Automated Service Composition is one of the ``grand challenges'' in the area of Service-Oriented Computing. Mike Papazoglou was not only one of the first researchers who identified the importance of the problem, but was also one of the first proposers of formulating it as an AI planning problem. Unfortunately, classical planning algorithms were not sufficient and a number of extensions were needed, e.g., to support extended (rich) goal languages to capture the user intentions, to plan under uncertainty caused by the non-deterministic nature of services; issues that where formulated (and, partially addressed) by Mike, being one of his key contributions to the service community

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/