  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.
