We present some techniques for planning in temporal domains specified with the recent standard languange PDDL2.1. These techniques are implemented in LPG, a fully-automated system that took part in the third International Planning Competition (Toulouse, 2002) showing excellent performance. The planner is based on a stochastic local search method and on a graph-based representation called “Tempo...

Many NASA planning problems are over-subscription problems – that is, there are a large number of possible goals of differing value, and the planning system must choose a subset that can be accomplished within the limited time and resources available. Examples include planning for telescopes like Hubble, SIRTF, and SOFIA; scheduling for the Deep Space Network; and planning science experiments f...

We introduce an approach to high-level conditional planning we call-safe planning. This probabilistic approach commits us to planning to meet some speciied goal with a probability of success of at least 1 ? for some user-supplied. We describe several algorithms for-safe planning based on conditional planners. The two conditional planners we discuss are Peot and Smith's non-linear conditional pl...

This paper presents a motion planning algorithm capable of exploiting the experience gained in previous path computations in the same static workspace. The algorithm takes advantage of a parallel approach to speed up computation and compile a graph retaining useful knowledge about the environment. Experimental results assess the performance improvement of the experience-based planner over the p...

In this paper we translate concurrent C/C++ code into PDDL. The system then runs heuristic search planners against the PDDL outcome to generate traces for locating programming bugs. These counter-examples result in an interactive debugging aid and exploit efficient planner in-built heuristics. Different aspects like parsing, generation of the dependency graph, slicing, abstraction, and property...

Motion-planning for autonomous vehicles involves a two-level planning hierarchy: a high-level task-planning algorithm and a lower-level trajectory generation algorithm. The task planner operates on a discrete structure, such as a graph, whereas the trajectory generator operates on a dynamical system with a continuous state space. The problem of ensuring “compatibility” between these two planner...

In this paper we will describe a software package , called DT-Planner, able to represent and solve nite-state Markov Decision Processes, by exploiting a novel graphical formalism, called Innuence View. An Innuence View is a directed acyclic graph that depicts the probabilistic relationships between the problems state variables in a generic time transition ; additional variables, called event va...

The treatment of exogenous events in planning is practically important in many realworld domains where the preconditions of certain plan actions are affected by such events. In this paper we focus on planning in temporal domains with exogenous events that happen at known times, imposing the constraint that certain actions in the plan must be executed during some predefined time windows. When ac...

Planning in hybrid systems has been gaining research interest in the Artificial Intelligence community in recent years. Hybrid systems allow for a more accurate representation of real world problems, though solving them is very challenging due to complex system dynamics and a large model feature set. In this paper we introduce DiNo, a new planner capable of tackling complex problems with non-li...