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/

Automated context aware composition of Advanced Telecom Services for environmental early warnings

This paper presents one of the main components of a framework for automated composition of Advanced Telecom Services for environmental early Warnings. The framework, called AUTO, is composed by three main modules: a request processing module that transforms natural language and context information into a planning instance; the automated planning module, based on PELEA, an architecture for planning and execution; and the Service Execution Environment Advance Telecom Services. This paper focuses on the description of the translation of the user request in natural language and his context into planning instances. These planning instances represent service composition tasks based on Automated Planning. The advantages of this approach, like the automatic inclusion of context and user preferences in the composition of services, will be presented. Also, the current implementation will be described and some experimentation will prove the viability of AUTO

College of San Mateo Mathematics and Science Teacher Education Program: A Bay Area Collaborative for Excellence in Teacher Preparation with San Jose State University and San Francisco State University

The College of San Mateo (CSM), a community college serving the San Mateo County area of California, is part of a collaborative effort in the San Francisco Bay Area to improve mathematics and science teacher preparation. With funding mainly through the National Science Foundation, the project is locally referred to as the MASTEP Project (Math and Science Teacher Education Program). MASTEP partners include two California State Universities (San Jose State University and San Francisco State University), four community colleges (College of San Mateo, City College of San Francisco, Evergreen Valley Community College, and San Jose City College), selected K-12 schools, and a number of informal educational institutions and local industries. Activities at CSM include recruitment of future math and science teachers through an active future teachers club; tutoring, mentoring and advising through the activities of an integrated science center; and professional development activities and financial support for science and math faculty resulting in their significant involvement in curriculum reform. As a community college, CSM plays a major role in identifying and supporting future teachers and providing these students with courses that are models of effective teaching