Distributed Cost-Optimal Planning

Automated planning is a field of artificial intelligence that aims at proposing methods to chose and order sets of actions with the objective of reaching a given goal. A sequence of actions solving a planning problem is usually called a plan. In many cases, one does not only have to find a plan but an optimal one. This notion of optimality can be defined by assigning costs to actions. An optimal plan is then a plan minimizing the sum of the costs of its actions. Planning problems are standardly solved using algorithms such as A* that search for minimum cost paths in graphs. In this document we focus on a particular approach to planning called factored planning or modular planning. The idea is to consider a decomposition of a planning problem into almost independent sub-problems (or components). One then searches for plans into each component and try to assemble these local plans into a global plan for the original planning problem. The main interest of this approach is that, for some classes of planning problems, the components considered can be planning problems much simpler to solve than the original one. The first part of this document proposes a study of the use of some message passing algorithms for factored planning. In this case the components of a problem are represented by weighted automata. This allows to handle all plans of a sub-problems, and permits to perform factored cost-optimal planning. Achieving cost-optimality of plans was not possible with previous factored planning methods. This approach is then extended by using approximate resolution techniques («turbo» algorithms) and by proposing another representation of components for handling actions which read-only in some components. The second part of this document describes another approach to factored planning: a distributed version of the famous A* algorithm. Each component is managed by an agent which is responsible for finding a local plan in it. For that, she uses information about her own component, but also information about the rest of the problem, transmitted by the other agents. The main difference between this approach and the previous one is that it is not only modular but also distributed.