The ability to plan is crucial in both intelligent and autonomous systems. In Artificial Intelligence (AI), AI planning focuses on the automated generation of plans in terms of actions that need to be executed to achieve a given user goal. Considering the central role of AI planning in AI and its prominence in research and industry, the development of AI planning software and its integration into production architectures are becoming important. However, building and managing planning applications is a complex process and requires expertise. On the one hand, significant engineering challenges exist that relate to the planning domain model design, the system design, development, deployment, integration, and system performance. On the other hand, no methodology or lifecycle currently exists that encompasses all phases relevant to the development process to ensure applications based on AI planning have high quality and industrial strength. In this paper, we propose a lifecycle for developing AI planning software that consists of ten phases relevant to the design, development, integration, and operation of AI planning applications. We describe each phase, including the available suitable approaches and open research challenges. As a result, the lifecycle can be used to support the development of future AI planning applications and as a basis for discussion among relevant communities and foster future research.
Mon 15 MayDisplayed time zone: Hobart change
20:45 - 22:15 | Poster - OnlinePosters / Papers at Virtual - Zoom for CAIN Chair(s): Mona Rahimi Northern Illinois University, Karthik Vaidhyanathan IIIT Hyderabad Click here to Join us over zoomClick Here to watch the session recording on YouTube | ||
20:45 6mPoster | AI Living Lab: Quality Assurance for AI-based Health systems Posters | ||
20:51 6mPoster | AI Planning Software Development Lifecycle Posters Ilche Georgievski University of Stuttgart, Germany File Attached | ||
20:57 6mPoster | Algorithm Debt: Challenges and Future Paths Posters Emmanuel Iko-Ojo Simon Australian National University, Melina Vidoni Australian National University, Fatemeh Hendijani Fard University of British Columbia | ||
21:03 6mPoster | Enabling Machine Learning in Software Architecture Frameworks Posters Armin Moin University of California, Santa Barbara, Atta Badii University of Reading, United Kingdom, Stephan G¨unnemann School of Computation, Information and Technology, Technical University of Munich, Munich, Germany, Moharram Challenger University of Antwerp DOI Pre-print | ||
21:09 6mPoster | Extensible Modeling Framework for Reliable Machine Learning System Analysis Posters Jati Hiliamsyah Husen Waseda University, Hironori Washizaki Waseda University, Hnin Thandar Tun Waseda University, Japan, Nobukazu Yoshioka Waseda University, Japan, Yoshiaki Fukazawa Waseda University, Hironori Takeuchi Musashi University, Hiroshi Tanaka Fujitsu Limited, Tokyo, Japan, Kazuki Munakata Fujitsu Limited, Tokyo, Japan | ||
21:15 6mPoster | How Federated Machine Learning Helps Increase the Mutual Benefit of Data-Sharing Ecosystems Posters Iva Krasteva Sofia University, GATE Institute, Boris Kraychev GATE Institute, Ensiye Kiyamousavi GATE Institute | ||
21:21 6mPoster | Maintaining and Monitoring AIOps Models Against Concept Drift Posters Lorena Poenaru-Olaru TU Delft, Luís Cruz Delft University of Technology, Jan S. Rellermeyer Leibniz University Hannover, Arie van Deursen Delft University of Technology | ||
21:27 6mPoster | Reproducibility Requires Consolidated Artifacts Posters Iordanis Fostiropoulos University of Southern California, USA, Bowman Brown University of Southern California, USA, Laurent Itti University of Southern California, USA | ||
21:33 6mPoster | Tenet: A Flexible Framework for Machine Learning-based Vulnerability Detection Posters Eduard Costel Pinconschi Instituto Superior Técnico, University of Lisboa & INESC-ID, Sofia Reis Instituto Superior Técnico, U. Lisboa & INESC-ID, Chi Zhang , Rui Abreu Faculty of Engineering, University of Porto, Hakan Erdogmus Carnegie Mellon University, Limin Jia Carnegie Mellon University | ||
21:39 6mPoster | Towards Understanding Machine Learning Testing in Practise Posters Arumoy Shome Delft University of Technology, Luís Cruz Delft University of Technology, Arie van Deursen Delft University of Technology Pre-print | ||
21:45 30mBreak | Break Out Session - Online Papers | ||
