On May 17th 1999, NASA activated for the first time an AI-based planner/scheduler running on the flight processor of a spacecraft. This was part of the Remote Agent Experiment (RAX), a demonstration of closedloop planning and execution, and model-based state inference and failure recovery. This paper describes the RAX Planner/Scheduler (RAX-PS), both in terms of the underlying planning framework and in terms of the fielded planner. RAX-PS plans are networks of constraints, built incrementally by consulting a model of the dynamics of the spacecraft. The RAX-PS planning procedure is formally well defined and can be proved to be complete. RAX-PS generates plans that are temporally flexible, allowing the execution system to adjust to actual plan execution conditions without breaking the plan. The practical aspect, developing a mission critical application, required paying attention to important engineering issues such as the design of methods for programmable search contr...

yQSS Group Inc. zQSS Group Inc. xRIACS experiment is assigned a scientific value). Different ob-servations and experiments take differing amounts of time and consume differing amounts of power and data storage.There are, in general, a number of constraints that govern the rovers activities: ffl There are time, power, data storage, and posi-tioning constraints for performing different activities. Time constraints often result from illuminationrequirement--that is, experiments may require that a target rock or sample be illuminated with a certain in-tensity, or from a certain angle.

We present an algorithm for planning with time and resources based on heuristic search. The algorithm minimizes makespan using an admissible heuristic derived automatically from the problem instance. Estimators for resource consumption are derived in the same way. The goals are twofold: To show the flexibility of the heuristic search approach to planning and to develop a planner that combines expressivity and performance. The two main issues are the definition of regression in the temporal setting and the definition of the heuristic for estimating completion time. A number of experiments are presented for assessing the performance of the resulting planner. 1

We are interested in solving real-world planning problems and, to that end, argue for the use of domain knowledge in planning. We believe that the field must develop methods capable of using rich knowledge models in order to make planning tools useful for complex problems. We discuss the suitability of current planning paradigms for solving these problems. In particular, we compare knowledge-rich approaches such as hierarchical task network (HTN) planning to minimal-knowledge methods such as STRIPS-based planners and disjunctive planners (DPs). We argue that the former methods have advantages such as scalability, expressiveness, continuous plan modification during execution, and the ability to interact with humans. However, these planners also have limitations, such as requiring complete domain models and failing to model uncertainty, that often make them inadequate for real-world problems. In this paper, we define the terms knowledge-based (KB) and primitive-action (PA) planning, and argue for the use of KB planning as a paradigm for solving real-world problems. We next summarize some of the characteristics of real-world problems that we are interested in addressing. Several current real-world planning applications are described, focusing on the ways in which knowledge is brought to bear on the planning problem. We describe some existing KB approaches, and then discuss additional capabilities, beyond those available in existing systems, that are needed. Finally, we draw an analogy from the current focus of the planning community on disjunctive planners to the experiences of the machine learning community over the past decade.

In most real-world reasoning problems, planning and scheduling phases are loosely coupled. For example, in project planning, the user comes up with a task list and schedules it with a scheduling tool like Microsoft Project. One can view automated planning in a similar way in which there is an action selection phase where actions are selected and ordered to reach the desired goals, and a resource allocation phase where enough resources are assigned to ensure the successful execution of the chosen actions. On the other hand, most existing automated planners studied in Artificial Intelligence do not exploit this loose-coupling and perform both action selection and resource assignment employing the same algorithm. The current work shows that the above strategy severely curtails the scale-up potential of existing state of the art planners which can be overcome by leveraging the loose coupling. Specifically, a novel planning framework called RealPlan is developed in which resource allocatio...

Abstract — Distributed real-time embedded (DRE) systems often perform sequences of coordination and heterogeneous data manipulation tasks to meet specified goals. Autonomous operation of DRE systems in dynamic environments can benefit from the integrated operation of (1) a Spreading Activation Partial Order Planner (SA-POP) that combines task planning and scheduling in uncertain environments with (2) a Resource Allocation and Control Engine (RACE) middleware framework that integrates multiple resource management algorithms for (re)deploying and (re)configuring task sequence components in DRE systems. This paper demonstrates the effectiveness of the SA-POP decisiontheoretic planner and the RACE framework in managing and executing mission goals for a multi-satellite system application. Our results show how a dynamic planner that handles both scheduling and resource constraints is a key element in implementing autonomy for DRE systems. I.

The problem we tackle is on-line rescheduling with temporal uncertainty, activity durations are uncertain and activity end times must be observed during execution.

We describe Probapop, a partial-order probabilistic planning system. Probapop is a blind (conformant) planner that finds plans for domains involving probabilistic actions but no observability. The Probapop implementation is based on Vhpop, a partial-order deterministic planner written in C++. The Probapop algorithm uses plan graph based heuristics for selecting a plan from the search queue, and probabilistic assessment heuristics for selecting a condition whose probability can be increased.

Planning consists of an action selection phase where actions are selected and ordered to reach the desired goals, and a resource allocation phase where enough resources are assigned to ensure the successful execution of the chosen actions. In most realworld problems, these two phases are loosely coupled. On the other hand, most existing planners do not exploit this loose-coupling and perform both action selection and resource assignment employing the same algorithm. The current work shows that the above strategy severely curtails the scale-up potential of existing planners, including such recent ones as Graphplan and Blackbox. In response, a novel planning framework was developed in which resource allocation is de-coupled from planning and is handled in a separate "scheduling" phase. Implementing this framework raises several interesting issues regarding the role of resources in planning, the interactions between the planning and scheduling phases and the choices in selecting the meth...

This article describes DI- RECTOR,astaff scheduling system for contact centers. DIRECTOR is a constraint-based system that uses AI search techniques to generate schedules that satisfy and optimize a wide range of constraints and service-quality metrics. DIRECTOR has successfully been deployed at more than 800 contact centers, with significant measurable benefits, some of which are documented in case studies included in this article