We argue that many AI planning problems should be viewed as process-oriented, where the aim is to produce a policy or behavior strategy with no termination condition in mind, as opposed to goal-oriented. The full power of Markov decision models, adopted recently for AI planning, becomes apparent with process-oriented problems. The question of appropriate optimality criteria becomes more critica...

Path planning is often a high-dimensional computationallyexpensive planning problem as it requires reasoning about the kinodynamic constraints of the robot and collisions of the robot with the environment. However, large regions of the environment are typically benign enough that a much faster low-dimensional planning combined with a local path following controller suffice. Planning with Adapti...

Computationally tractable planning problems reported in the literature so far have almost exclusively been de ned by syntactical restrictions. To better exploit the inherent structure in problems, it is probably necessary to study also structural restrictions on the underlying state-transition graph. The exponential size of this graph, though, makes such restrictions costly to test. Hence, we p...

So far, tractable planning problems reported in the literature have been defined by syntactical restrictions. To better exploit the inherent structure in problems, however, it is probably necessary to study also structural restrictions on the state-transition graph. Such restrictions are typically computationally hard to test, though, since this graph is of exponential size. Hence, we take an i...

We consider the problem of optimal path planning in different homotopy classes in a given environment. Though important in applications to robotics, homotopy path-planning in applications usually focuses on subsets of the Euclidean plane. The problem of finding optimal trajectories in different homotopy classes in more general configuration spaces (or even characterizing the homotopy classes of...

In this paper we describe a planner that extends the TLPLAN system to enable planning with temporally extended preferences specified in PDDL3, a variant of PDDL that includes descriptions of temporal plan preferences. We do so by compiling preferences into nondeterministic finite state automata whose accepting conditions denote achievement of the preference described by the automata. Automata a...

Planning invariants are formulae that are true in every reachable state of a planning world. We describe a novel approach to the problem of discovering such invariants—by analyzing only a reachable state of the planning domain, and not its operators. Our system works by exploiting perceived patterns and anomalies in the state description: It hypothesizes that patterns that are very unlikely to ...

Planning with forced goal-ordering (FGO) constraints has been proposed many times over the years, but there are still major difficulties in realizing these FGOs in plan generation. In certain planning domains, all the FGOs exist in the initial state. No matter which approach is adopted to achieve a subgoal, all the subgoals should be achieved in a given sequence from the initial state. Otherwis...

East Asian countries have recorded large increases in per capita GDP over the last fifty years. Some observers have referred to this growth as an “East Asian Miracle.” One popular explanation attributes the rapid growth to state led industrial development planning. This paper critically assesses the arguments surrounding state development planning and East Asia’s growth. Whether the state can a...