and SGPlan (two well-known classical planners), its performance compares quite favorably to that of MBP, one of the best-known planners for nondeterministic planning problems.

. This thesis presents a general method for automatically generating efficient domain-specific classical planners from formal specifications using a semi-automated software synthesis system called Kestrel Interactive Development System (KIDS, for short). To do this, a unifying theory for general

generalized plan, our overall approach is to incrementally increase the applicability of the plan by identifying a problem instance that it cannot solve, invoking a classical planner to solve that problem, generalizing the obtained solution and merging it back into the generalized plan. The main contributions

approach, which consists of cascading a planner and a scheduler. While other implementations of this framework have already been reported, our work aims at analyzing the structural properties of the scheduling problem which results from the planning component, focusing on the bias produced by different

obstructions requires geometric reasoning which is prohibitively expensive to precompute, with results that are difficult to represent efficiently at the level of a discrete planner. We propose a new approach that utilizes representation techniques from first-order logic and provides a method for synchronizing

efficiently using classical planners provided that the probability of a partially observed execution given a goal is defined in terms of the cost difference of achieving the goal under two conditions: complying with the observations, and not complying with them. This cost, and hence the posterior goal

, are intractable in the worst case when represented in compact form. Classical planning refers to the simplest form of plan-ning where goals are to be achieved by applying deterministic actions to a fully known initial situation. In this invited paper, I review the infer-ences performed by classical planners

are observable. The problem of deriving a controller from such models is converted into a conformant planning problem that is solved using classical planners, taking advantage of a complete translation introduced recently. The controllers derived in this way are ‘general ’ in the sense that they do not solve

In this paper, we introduce a new approach to conformant planning via classical planners. We view a conformant planning problem as a set of classical planning problems, called sub-problems, and solve it using a generate-and-complete algorithm. Key to this algorithm is a procedure which takes a

on the state sequences generated by finite plans. In this work, we take the next step and consider the computation of infinite plans for achieving arbitrary LTL goals. We show that infinite plans can also be obtained efficiently by calling a classical planner once over a classical planning encoding