Many software applications consist of a number of interdependent steps and are executed under the supervision of a human administrator or operator. The administrator responds to error conditions and checks to see that the dependencies between steps has been satisfied before allowing the application to continue. AI planning systems, because they have the capability of responding to a dynamically...

Most current-day AI planning systems operate by iteratively refining a partial plan until it meets the goal requirements. In the past five years, significant progress has been made in our understanding of the spectrum and capabilities of such refinement planners. In this talk, I will summarize this understanding in terms of a unified framework for refinement planning and discuss several current...

Derivation replay was first proposed by Carbonell as a method of transferring guidance from a previous problemsolving episode to a new one. Subsequent implementations have used state-space planning as the underlying methodology. This paper is motivated by the acknowledged superiority of partial-order (PO) planners in plan generation, and is an attempt to bring derivation replay into the realm o...

This paper presents three approaches to generate learning designs using existing domain-independent planners. All of the approaches compile a course defined in a standard e-learning language into a planning domain, and a file containing student’s learning information into a planning problem. The learning designs are automatically generated from the plans that solve the problems. The approaches ...

We consider the problem of temporal planning in which a given goal is reached by taking a number of actions which may temporally overlap and interfere, and the interference may be essential for reaching the goals. We formalize a general temporal planning problem, show that its plan existence problem is EXPSPACE-complete, and give conditions under which it is reducible to classical planning and ...

In deep brain stimulation surgeries, stimulating electrodes are placed at specific targets in the deep brain to treat neurological disorders. Reaching these targets safely requires avoiding critical structures in the brain. Meticulous planning is required to find a safe path from the cortical surface to the intended target. Choosing a trajectory automatically is difficult because there is littl...

AI planning techniques are beginning to find use in a number of practical planning domains. However, the backward-chaining and partial-order-planning control strategies traditionally used in AI planning systems are not necessarily the best ones to use for practical planning problems. In this paper, we discuss some of the difficulties that can result from the use of backward chaining and partial...

There has recently been an increased emphasis on reducing energy consumption in manufacturing, driven by the fluctuations in energy costs and the growing importance given to environmental impact of manufactured goods. Lots of attention has been given to the reduction of machine tools energy consumption, as they require large amounts of energy to perform manufacturing tasks. One area that has re...

Advances in information technology and telecommunications, together with ever growing amounts of data, offer opportunities for transportation companies to improve the quality of the service that they provide to their customers. This paper explores this issue in the context of a dynamic and stochastic routing problem in which a single, uncapacitated vehicle serves a set of known customers locati...

Most current-day AI planning systems operate by iteratively refining a partial plan until it meets the goal requirements. In the past five years, significant progress has been made in our understanding of the spectrum and capabilities of such refinement planners. In this talk, I will summarize this understanding in terms of a unified framework for refinement planning and discuss several current...