Timeline-based Planning and Execution with Uncertainty: Theory, Modeling Methodologies and Practice Artificial Intelligence

Automated Planning is one of the main research field of Artificial Intelligence since its beginnings. Research in Automated Planning aims at developing general reasoners (i.e., planners) capable of automatically solve complex problems. Broadly speaking, planners rely on a general model characterizing the possible states of the world and the actions that can be performed in order to change the status of the world. Given a model and an initial known state, the objective of a planner is to synthesize a set of actions needed to achieve a particular goal state. The classical approach to planning roughly corresponds to the description given above. The timeline-based approach is a particular planning paradigm capable of integrating causal and temporal reasoning within a unified solving process. This approach has been successfully applied in many real-world scenarios although a common interpretation of the related planning concepts is missing. Indeed, there are significant differences among the existing frameworks that apply this technique. Each framework relies on its own interpretation of timeline-based planning and therefore it is not easy to compare these systems. Thus, the objective of this work is to investigate the timeline-based approach to planning by addressing several aspects ranging from the semantics of the related planning concepts to the modeling and solving techniques. Specifically, the main contributions of this PhD work consist of: (i) the proposal of a formal characterization of the timeline-based approach capable of dealing with temporal uncertainty; (ii) the proposal of a hierarchical modeling and solving approach; (iii) the development of a general purpose framework for planning and execution with timelines; (iv) the validation{\dag}of this approach in real-world manufacturing scenarios.

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AI Magazine is an official publication of the Association for the Advancement of Artificial Intelligence (AAAI). It is published four times each year in fall, winter, spring, and summer issues, and is sent to all members of the Association and subscribed to by most research libraries. Back issues are available on-line (issues less than 18 months old are only available to AAAI members). The purpose of AI Magazine is to disseminate timely and informative expository articles that represent the current state of the art in AI and to keep its readers posted on AAAI-related matters. The articles are selected for appeal to readers engaged in research and applications across the broad spectrum of AI.

Panel Discussion: Artificial Intelligence in Sales February 27th at 10am PST by John Golden - SalesPOP!


The use of Artificial Intelligence in Sales has become a hot topic with a lot of hype and myth included. Join this expert panel who will discuss AI in Sales and cut through the noise bringing you the reality and the real benefit of AI for Sales. Join our host John Golden with guests Nikolaus Kimla, Adrian Davis and Rob Jolles on February 27th at 10am PST to as they discuss Artificial Intelligence in sales. Nikolaus Kimla – A 30-year veteran of the computer industry, Nikolaus has founded and run several software companies. He and his company uptime iTechnology are the developers of World-Check, a risk intelligence platform eventually sold to Thomson Reuters for $520 million.

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Special issue on "Governing artificial intelligence: ethical, legal and technical opportunities and challenges"


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#CogX 2018 Panel Discussion with 4 teens-future leaders


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