This paper will present a knowledge layer used by a hierarchical on-road driving planner that represents a road network as a discrete set of attributed road states. This knowledge layer facilitates the construction of a planning graph by providing simulation and prediction services to the planning system. These services allow the determination of possible spatial transitions along a road networ...

We present in this paper a hybrid planning systemwhich combines constraint satisfaction techniques and planning heuristics to produce optimal sequential plans. It integrates its own consistency rules and filtering and decomposition mechanisms suitable for planning. Given a fixed bound on the plan length, our planner works directly on a structure related to Graphplan’s planning graph. This struc...

Many real-world planning problems require sophisticated reasoning about numeric resources, including sharing of resources by different actions. Constraintbased planners offer machinery to represent complex constraints and dependencies between actions. However, such planners usually consider a single partial plan at a time, and limit themselves with finding a feasible solution. Alternatively, pl...

Many planning and scheduling applications require the ability to deal with uncertainty. Often this uncertainty can be characterized in terms of probability distributions on the initial conditions and on the outcomes of actions. These distributions can be used to guide a planner towards the most likely plan for achieving the goals. This work is focused on developing domain-independent heuristics...

In previous work we have shown that grounding, while used by most (if not all) modern state-of-the-art planners, is not necessary and is sometimes even undesirable. In this paper we extend this work and present a novel forward-chaining planner that does not require grounding and can solve problem instances that are too large for current planners to handle. We achieve this by exploiting equivale...

Our Breadth-First Heuristic Search Planner (BFHSP) is a domain-independent STRIPS planner that finds sequential plans that are optimal with respect to the number of actions it takes to reach a goal. We developed BFHSP as part of our research on space-efficient graph search. It uses breadth-first search since we found that breadth-first search is more efficient than best-first search when divide...

This paper studies the quasi-static motion of large legged robots that have many degrees of freedom. While gaited walking may suffice on easy ground, rough and steep terrain requires unique sequences of footsteps and postural adjustments specifically adapted to the terrain’s local geometric and physical properties. This paper presents a planner that computes these motions by combining graph sea...

Planning with hybrid domains modelled in PDDL+ has been gaining research interest in the Automated Planning community in recent years. Hybrid domain models capture a more accurate representation of real world problems that involve continuous processes than is possible using discrete systems. However, solving problems represented as PDDL+ domains is very challenging due to the construction of co...

and Kambhampati 2000). This heuristic, along with the problem specification, and the set of ground actions in the final action level of the planning graph structure are fed to a regression state search planner. The regression planner code is adapted from HSP-R (Bonet and Geffner 1999) because it provides an optimized state search engine. The crux of controlling the regression search involves pr...