State agnostic planning graphs: deterministic, non-deterministic, and probabilistic planning

Planning graphs have been shown to be a rich source of heuristic information for many kinds of planners. In many cases, planners must compute a planning graph for each element of a set of states, and the naive technique enumerates the graphs individually. This is equivalent to solving a multiple-source shortest path problem by iterating a single-source algorithm over each source. We introduce a data-structure, the state agnostic planning graph, that directly solves the multiple-source problem for the relaxation introduced by planning graphs. The technique can also be characterized as exploiting the overlap present in sets of planning graphs. For the purpose of exposition, we first present the technique in deterministic planning to capture a set of planning graphs used in forward chaining search. A more prominent application of this technique is in belief state space planning, where each search node utilizes a set of planning graphs; an optimization to exploit state overlap between be...

• Type: Journal
• Year: 2011
• Where: AI
• Authors: Daniel Bryce, William Cushing, Subbarao Kambhampati
• Keywords: AI 2011, Artificial Intelligence, Belief State, Planning, Planning Graphs