Recognizing goals and plans from complete or partial observations can be efficiently
achieved through automated planning techniques. In many applications, it is important to …

Throughout the years, extensive work has pointed out how important computing and
exploiting invariants is in planning. However, no recent studies about their empirical impact …

In cost-optimal planning we aim to find a sequence of operators that achieve a set of goals
with minimum cost. Symbolic search with Binary Decision Diagrams (BDDs) performs …

We define a probe to be a single action sequence computedgreedily from a given state that
either terminates in the goalor fails. We show that by designing these probes carefullyusing …

Heuristic search with reachability-based heuristics is arguably the most successful paradigm
in Automated Planning to date. In its earlier stages of development, heuristic search was …

Symbolic search allows saving large amounts of memory compared to regular explicit-state
search algorithms. This is crucial in optimal settings, in which common search algorithms …

The 11th International Conference on the Principles and Practice of Constraint Programming
(CP 2005) was held in Sitges (Barcelona), Spain, October 1–5, 2005. Information about the …

This paper describes a polynomially-solvable class of temporal planning problems.
Polynomiality follows from two assumptions. Firstly, by supposing that each sub-goal fluent …

Current state-of-the-art planners solve problems, easy and hard alike, by search, expanding
hundreds or thousands of nodes. Yet, given the ability of people to solve easy problems and …

In industrial domains such as manufacturing control, a trend away from centralized planning
and scheduling towards more flexible distributed agent-based approaches could be …