We describe some new preprocessing techniques that enable faster domain-independent planning. The first set of techniques is aimed at inferring state constraints from the structure of planning operators and the initial state. Our methods consist of generating hypothetical state constraints by inspection of operator effects and preconditions, and checking each hypothesis against all operators an...

In real world environments the state is almost never completely known. Exploration is often expensive. The application of planning in these environments is consequently more difficult and less robust. In this paper we present an approach for predicting new information about a partially-known state. The state is translated into a partially-known multigraph, which can then be extended using machi...

Planning is designing the behavior of an agent. The classical theory of planning assumes that the planner has perfect information, and needs to bring about a deenite result, by nding a series of actions that brings it about. These classical assumptions have been questioned, but are quite reasonable for many engineering applications. The problem is that traditional algorithms, while elegant, are...

This paper introduces a general planning approach with state trajectory constraints, a language extension that has recently been introduced to PDDL for running the Fifth International Planning Competition in 2006. In our approach PDDL inputs with state trajectory constraints are translated into ordinary (fully instantiated) PDDL. It exploits the automata-based interpretation of the constraints,...

There is increasing awareness that planning and model checking are closely related fields. Abstraction means to perform search in an over-approximation of the original problem instance, with a potentially much smaller state space. This is the most essential method in model checking. One would expect that it can also be made successful in planning. We show, however, that this is likely to not be...

Many architectures of autonomous agent have been proposed throughout AI research. The most common architectures, BDI, are procedural in that they do no planning, seriously curtailing an agent’s ability to cope with unforeseen events. In this paper, we explore the relationship between propositional planning systems and the process of means-ends reasoning used by BDI agents and define a mapping f...

We are interested in the nature of activity { structured behavior of nontrivial duration { in intelligent agents. We believe that the development of activity is a continual process in which simpler activities are composed, via planning, to form more sophisticated ones in a hierarchical fashion. The success or failure of a planner depends on its models of the environment, and its ability to impl...

Bounds of probability distributions are useful for many reasoning tasks, including resolving the qualitative ambiguities in qualitative probabilistic networks and searching the best path in stochastic transportation networks. This paper investigates a subclass of the state-space abstraction methods that are designed to approximately evaluate Bayesian networks. Taking advantage of particular sto...

In this paper, we present a novel motion-orientated path planning algorithm fo r real-time navigation of mobile agents. The algorithm works well in dynamical and un-configured environments, and is able to produce a collision-free, time -optimal motion trajectory in order to find a navigation path. In addition to the motion constraint path planning, our approach can deal with the unknown obstacl...

The majority of research in the field of automated planning focuses on the synthesis of plans for problems that are solvable. We propose an IPC track to focus on the important and understudied area of unplannibility: proving that a planning problem is unsolvable. We will focus on classical planning problems, as methods for determining whether or not unplannability can have wider applications fo...