He holds a B.S. degree in Mathematics from Westminster College, and M.S. and Ph.D. degrees in Computer Science from the University of Pittsburgh. His research interests include constraint-based planning and scheduling, integration of predictive and reactive decision-making, distributed problem solving, temporal reasoning, machine learning, and knowledge-based production management. He has been a principal architect of several knowledge-based scheduling systems for complex manufacturing and space applications. Claude Le Pape is a visiting researcher in the Robotics Laboratory at Stanford University. He received a Ph.D. in Computer Science from University Paris XI in 1988.

A longstanding problem in the field of automated reasoning is designing systems that can describe a set of actions (or a plan) that can be expected to allow the system to reach a desired goal. Ideally, this set of actions is then passed to a robot, a manufacturing system, or some other form of effector, which can follow the plan and produce the desired result. The design of such planners has been with AI since its earliest days, and a large number of techniques have been introduced in progressively more ambitious systems over a long period. In addition, planning research has introduced many problems to the field of AI. Some examples are the representation and the reasoning about time, causality, and intentions; physical or other constraints on suitable solutions; uncertainty in the execution of plans; sensation and perception of the real world and the holding of beliefs about it; and multiple agents who might cooperate or interfere.

This article describes a challenging, real-world planning problem within the context of a NASA mission called LADEE (Lunar Atmospheric and Dust Environment Explorer). I present the approach taken to reduce the complexity of the activity-planning task in order to perform it effectively under the time pressures imposed by the mission requirements. One key aspect of this approach is the design of the activityplanning process based on principles of problem decomposition and planning abstraction levels. The second key aspect is the mixed-initiative system developed for this task, called LASS (LADEE Activity Scheduling System).

David McAllester and David Rosenblitt's paper, "Systematic Nonlinear Planning" (published in the Proceedings of the Ninth National Conference on Artificial Intelligence [AAAI-91]), won the AAAI-10 classic paper award. This commentary by Daniel S. Weld describes the two major impacts the paper had on the field of automated planning. Initially, researchers made many simplifying assumptions, defining the classical planning problem: produce a sequence of atomic actions that will achieve a logically specified goal in a completely known world where action effects are certain. David McAllester and David Rosenblitt's paper, "Systematic Nonlinear Planning" (McAllester and Rosenblitt 1991), presented 19 years ago at the Ninth National Conference on Artificial Intelligence (AAAI-91), had two major impacts on the field: (1) an elegant algorithm and (2) endorsement of the lifting technique. The paper's biggest impact stems from its extremely clear and simple presentation of a sound and complete algorithm (known as SNLP or POP) for classical planning.

The primary revolution in automated planning in the last decade has been the very impressive scaleup in planner performance. A large part of the credit for this can be attributed squarely to the invention and deployment of powerful reachability heuristics. Most, if not all, modern reachability heuristics are based on a remarkably extensible data structure called the planning graph, which made its debut as a bit player in the success of GraphPlan, but quickly grew in prominence to occupy the center stage. Planning graphs are a cheap means to obtain informative look-ahead heuristics for search and have become ubiquitous in state-of-the-art heuristic search planners. We present the foundations of planning graph heuristics in classical planning and explain how their flexibility lets them adapt to more expressive scenarios that consider action costs, goal utility, numeric resources, time, and uncertainty. Considerable work has been done in the last 40 years on modeling a wide variety of ...

In this article we review the 2011 International Planning Competition. We give an overview of the history of the competition, discussing how it has developed since its first edition in 1998. The 2011 competition was run in three main separate tracks: the deterministic (classical) track; the learning track; and the uncertainty track. Each track proposed its own distinct set of new challenges and the participants rose to these admirably, the results of each track showing promising progress in each area. The competition attracted a record number of participants this year, showing its continued and strong position as a major central pillar of the international planning research community.

Complex, real-world domains require rethinking traditional approaches to AI planning. Planning and executing the resulting plans in a dynamic environment implies a continual approach in which planning and execution are interleaved, uncertainty in the current and projected world state is recognized and handled appropriately, and replanning can be performed when the situation changes or planned actions fail. Furthermore, complex planning and execution problems may require multiple computational agents and human planners to collaborate on a solution. In this article, we describe a new paradigm for planning in complex, dynamic environments, which we term distributed, continual planning (DCP). We argue that developing DCP systems will be necessary for planning applications to be successful in these environments.

With regard to the first caveat, Moravec's estimates of animal equivalence are based solely on hardware complexity. It is often the case that hardware alone cannot deliver performance, but it also requires software sufficient to the task. Thus, projecting "human equivalence" in computer hardware does not mean that "human equivalent software" will be ready to run on the hardware. With regard to the second caveat, neuroscientists regularly adjust upward the estimate of human brain complexity. This upward estimation has the effect of extending the estimated time at which comput-Hans Moravec, Robot: Mere Machine to Transcendent Mind.

We stare intensely at the robot with one eye, keeping the other one out for any surprises. It looks for the door and slowly starts moving into the room. Our minds seem to be sharing the same thought--" YODA, don't fail us now." We decided that the Office Navigation event in the robot competition was to be our first milestone in working toward this goal. It would provide us a context in which to direct our efforts.

The economic viability of a manufacturing organization depends on its ability to maximize customer services; maintain efficient, low-cost operations; and minimize total investment. These objectives conflict with one another and, thus, are difficult to achieve on an operational basis. Much of the work in the area of automated scheduling systems recognizes this problem but does not address it effectively. The work presented by this Ph.D. dissertation was motivated by the desire to generate good, costeffective schedules in dynamic and stochastic manufacturing environments (Berry 1991). Experimental analysis is used to illustrate…the PCP approach within an advanced scheduling architecture.