Mark Drummond

Total-order and partial-order planning: a comparative analysis

For many years, the intuitions underlying partial-order planning were largely taken for granted. Only in the past few years has there been renewed interest in the fundamental principles underlying this paradigm. In this paper, we present a rigorous comparative analysis of partial-order and total-order planning by focusing on two specific planners that can be directly compared. We show that there are some subtle assumptions that underly the wide-spread intuitions regarding the supposed efficiency of partial-order planning. For instance, the superiority of partial-order planning can depend critically upon the search strategy and the structure of the search space. Understanding the underlying assumptions is crucial for constructing efficient planners.

The entropy reduction engine: integrating planning, scheduling, and control

This paper describes the Entropy Reduction Engine, an architecture for the integration of planning, scheduling, and control. The architecture is motivated, presented, and analyzed in terms of its different components; namely, problem reduction, temporal projection, and situated control rule execution. Experience with this architecture has motivated the recent integration of learning, and this paper also describes the learning methods and their impact on architecture performance.

Reactive, Integrated Systems Pose New Problems for Machine Learning

Publisher Summary Most research on machine learning and planning has involved performance systems based on classical problem-solving algorithms (for example, STRIPS-Iike planners). AI problem solving has taken various divergent roads from these classical roots; two common current trends are reactive systems embedded in an environment and integrated multicomponent architectures. As performance engines, these advanced systems give rise to new learning problems—both in the sense of new opportunities and new difficulties. This chapter discusses new problems for machine learning. Classical problem-solving systems are typically consisted of a single component with a limited range of objectives and capabilities. Some current research efforts adopt a more holistic, synergistic approach involving integrated architectures with a broader scope of objectives and capabilities. These architectures integrate multiple performance components or multiple styles of reasoning. New issues arise within the context of integrated architectures, which engender new requirements and opportunities for machine learning.

Planning for control

The problems that lie at the boundary of planning and control are studied from an artificial intelligence (AI) planning perspective. An attempt is made to extend the tools of traditional AI planning to handle more complex domains. The capabilities of current plan generation and execution systems are outlined. A sample planning and control problem is defined and then used to show where traditional AI planning fails. The failure of traditional planning is used to motivate a discussion on aspects of a new approach to the relationship between plan generation and plan execution.<<ETX>>

Just-in-case scheduling for automatic telescopes

It is commonly acknowledged that there is a tradeoff between schedule quality and schedule robustness. In general terms, schedules that are of high quality tend to be of low robustness, and schedules that are robust tend to be of low quality. To better manage the robustness/quality tradeoff, we have developed an algorithm that implements what we call Just-In-Case scheduling; this algorithm explicitly considers the way in which scheduled actions might fail and how such failures can impact the executability of a schedule. Just-In-Case scheduling is able to build schedules that are robust and of high quality. The Just-In-Case algorithm is motivated in this paper by a specific telescope scheduling problem, and the paper presents the results of an experiment, carried out using real telescope scheduling data, that illustrates the performance improvement one can expect from using it.