"...an artificial intelligence system was placed on board to plan and execute spacecraft activities. In contrast to remote control, this sophisticated set of computer programs acts as an agent of the operations team on board the remote spacecraft. Rather than have humans do the detailed planning necessary to carry out desired tasks, remote agent will formulate its own plans, using high level goals provided by the operations team....Remote agent, like the other high-risk technologies that have now been tested on DS1, promises to make space exploration of the future more productive and more exciting while staying within NASA's limited budget." - from NASA's site about REMOTE AGENT

Artificial Intelligence Group at JPL (NASA's Jet Propulsion Laboratory). "The Artificial Intelligence Group performs basic research in the areas of Artificial Intelligence Planning and Scheduling, with applications to science analysis, spacecraft commanding, deep space network operations, and space transportation systems." Projects include CASPER: "An autonomous spacecraft must balance long-term and short-term considerations. It must perform purposeful activities that ensure long-term science and engineering goals are achieved and ensure that it maintains positive resource margins. This requires planning in advance to avoid a series of shortsighted decisions that can lead to failure. However, it must also respond in a timely fashion to a somewhat dynamic and unpredictable environment. Thus, spacecraft plans must often be modified due to fortuitous events such as early completion of observations and setbacks such as failure to acquire a guidestar for a science observation.CASPER (Continuous Activity Scheduling Planning Execution and Replanning) uses iterative repair to support continuous modification and updating of a current working plan in light of changing operating context. "

Planning. Entry by Austin Tate in the MIT Encyclopedia of Cognitive Science. "Planning is the process of generating (possibly partial) representations of future behavior prior to the use of such plans to constrain or control that behavior. The outcome is usually a set of actions, with temporal and other constraints on them, for execution by some agent or agents. As a core aspect of human intelligence, planning has been studied since the earliest days of AI and cognitive science. Planning research has led to many useful tools for real-world applications, and has yielded significant insights into the organization of behavior and the nature of reasoning about actions."

AI Planning: Systems and Techniques. By James Hendler, Austin Tate, Mark Drummond. AI Magazine 11(2): Summer, 1990, 61-77. "This article reviews research in the development of plan generation systems. Our goal is to familiarize the reader with some of the important problems that have arisen in the design of planning systems and to discuss some of the many solutions that have been developed in the over 30 years of research in this area. In this article, we broadly cover the major ideas in the field of AI planning and show the direction in which some current research is going. We define some of the terms commonly used in the planning literature, describe some of the basic issues coming from the design of planning systems, and survey results in the area. Because such tasks are virtually never ending, and thus, any finite document must be incomplete, we provide references to connect each idea to the appropriate literature and allow readers access to the work most relevant to their own research or applications." Some of the terms and planners covered are: application domanin, operator schemata, primitive action, STRIPS, HACKER, NOAH, NONLIN, NASL, OPM,ISIS-II, MOLGEN, SIPE, DEVISER and FORBIN.

Planning articles appearing in the Fall 2001 issue of AI Magazine, 22(3):

• The AIPS '00 Planning Competition
• FF: The Fast-Forward Planning System
• The GRT Planner
• MIPS: The Model-Checking Integrated Planning System
• A Planner Called R
• Heuristic Search Planner 2.0
• STAN4: A Hybrid Planning Strategy Based on Subproblem Abstraction;
• Tokenplan
• AltAlt: Combining Graphplan and Heuristic State Search
• The Shop Planning System
• TALPlanner: A Temporal Logic-Based Planner
• Planning in the Fluent Calculus Using Binary Decision Diagrams

The PLANET Roadmap on AI Planning and Scheduling. "Planning and Scheduling is the field of Artificial Intelligence that is concerned with all aspects of the system-supported or fully automated synthesis, execution, and monitoring of courses of actions, activities, and tasks. With that, it provides a technology for increasing the autonomy of systems by making them more flexible, robust, and adaptive. Consequently, it has a particularly large application potential in a variety of industrial and administrative areas including the growing e-business and e-work sectors. This road map document aims to take stock of current exploitation of the technology and points out future research and development steps for both improving the technology in current applications and widening the spectrum of future ones. The road map is joint work by members of PLANET, the European Network of Excellence in AI Planning - funded by the European Union under the Esprit programme from October 1998 to December 2000."

Recent Advances in AI Planning: A Unified View. Tutorials from Subbarao Kambhampati.

Planning. A summary by Patrick Doyle. "Planning is a problem solving technique. Planning is reasoning about future events in order to verify the existence of a reasonable series of actions to take in order to accomplish a goal. There are three major benefits of planning: reducing search, resolving goal conflicts, and providing a basis for error recovery."

Planning and Scheduling Materials from The Computational Intelligence Research Laboratory (CIRL) of the University of Oregon:

• "In industry in general, the term planning has been used to describe a wide variety of problems, including problems referred to by the artificial intelligence community as scheduling or bin packing. In AI, the term planning is used to describe the construction of a sequence of formally-described world-states, as follows: A planning domain consists of a set of operators or action types. Each operator may be executed only in some particular set of world states (its preconditions), and has some particular set of effects on its world state (its effects). A planning problem consists of a planning domain together with an initial state of the world, and a desired goal state (or set of goal states) of the world. A planner solves a planning problem by producing a sequence of actions (operator instances), each of which is legal in its starting world state, which takes the initial state to a goal state. ... An often cited example of a planning domain is the infamous blocks world, a model of stacks of blocks on an infinite table."
• "Scheduling is the problem of assigning a set of tasks to a set of resources subject to a set of constraints. Examples of scheduling constraints include deadlines (e.g., job i must be completed by time t), resource capacities (e.g., there are only four drills), precedence constraints on the order of tasks (e.g., a piece must be sanded before it is painted), and priorities on tasks (e.g., finish job j as soon as possible while meeting the other deadlines). Examples of scheduling domains include classical job-shop scheduling, manufacturing scheduling, and transportation scheduling." In addition to papers and articles, their page offers links to application programs.
• Also see: Eugene firm wins Navy contract. By Sherri Buri McDonald. The Register-Guard (September 8, 2006). "In the next nine months, On Time Systems will evaluate the potential cost savings if the Navy used the firm's shipyard scheduling software at its major repair yards. ... This is the second major contract On Time Systems has secured with the Navy. Two years ago, the Navy awarded a $1.38 million contract to On Time Systems to create a simulated shipyard to test the potential cost savings if the firm's shipyard scheduling software, called ARGOS, was used to build new vessels. On Time Systems projected that the software could save taxpayers $500 million annually. ... On Time Systems is a 1998 spin-off of CIRL, the UO's Computational Intelligence Research Laboratory. On Time Systems also has developed software to route all U.S. Air Force noncombat flights - typically 700 to 900 flights a day. The technology prevents 20 million gallons of jet fuel a year from being burned in the upper atmosphere, [Matt] Ginsberg said."
  • Visit On Time Systems, Inc. (OTS).

Honoring Asia's best. By Isabelle Chan. ZDNet Asia (July 6, 2006). "ZDNet Asia also handed out special awards in six categories. We salute the winners of the Project of the Year Awards--their IT departments, initiative and commitment to making their IT project a success. Winning these awards is no mean feat, as the quality of entries was extremely high and the judges had a difficult time picking the winners. The winners are: ... Project of the Year: ... There were several hot favorites, but the winner was MTR's Engineering Works & Traffic Information Management System (ETMS). The Hong Kong rail transportation company developed the system to ensure better utilization of MTR's limited resources--people, tools, workspace and time--during four non-traffic hours of the day. Developed using artificial intelligence technology, the system automates the planning, monitoring, controlling and reviewing of all maintenance and engineering works."

• Project of the Year. By Isabelle Chan. ZDNet Asia (July 6, 2006). "The business challenge was, therefore, to optimize the utilization of MTR's limited resources--people, tools, workspace and time (four non-traffic hours every day)--and yet be able to comply with the statutory and safety regulations. In 2005, MTR embarked on a project called the Engineering Works & Traffic Information Management System (ETMS) which uses artificial intelligence (AI) for planning, scheduling and managing engineering works. ... The project team comprised MTR ITSD in-house staff and a City University of Hong Kong associate professor who specializes in artificial intelligence. MTR ITSD was the project manager of the project, and a pool of users with domain experts provided input to the functions of the system. ... This same AI rule engine is now used by the Immigration Department of Hong Kong for application assessment."
  • Also see this article from the Hong Kong Trade Development Council: MTR Corporation: Safety and efficiency brought about by Artificial Intelligence (June 1, 2006).
• In 2005, this application received the AAAI Award for Innovative Applications of AI: "MTR Corporation for the Hong Kong subway system - Automatic planning and scheduling of maintenance and repair workThe Hong Kong MTR metro system carries 2.4 million passengers each weekday, compared with New York’s subway system which carries roughly one-tenth that number daily. Despite the volume of traffic, the Hong Kong subway was punctual more than 99% of the time in 2004. Each night the system is shut down at midnight for only 4 to 5 hours, during which time all necessary maintenance and repair work is performed. The AI based system streamlines scheduling this work -- maximizing the number of jobs done while ensuring operational safety and resource availability."
• And here's the paper that was presented at the conference: Scheduling Engineering Works for the MTR Corporation in Hong Kong. By Andy Hon Wai Chun, Dennis Wai Ming Yeung, Garbbie Pui Shan Lam, Daniel Lai, Richard Keefe, Jerome Lam, and Helena Chan. 2005. In Proceedings of the Seventeenth Innovative Applications of Artificial Intelligence Conference, 1467 - 1474. Menlo Park, Calif.: AAAI Press. Abstract: "This paper describes a Hong Kong MTR Corporation subway project to enhance and extend the current Web-based Engineering Works and Traffic Information Management System (ETMS) with an intelligent 'AI Engine.' The challenge is to be able to fully and accurately encapsulate all the necessary domain and operation knowledge on subway engineering works and to be able to apply this knowledge in an efficient manner for both validation as well as scheduling. Since engineering works can only be performed a few hours each night, it is crucially important that the 'AI Engine' maximizes the number of jobs done while ensuring operational safety and resource availability. Previously, all constraint/resource checking and scheduling decisions were made manually. The new AI approach streamlines the entire planning, scheduling and rescheduling process and extends the ETMS with intelligent abilities to (1) automatically detect potential conflicts as work requests are entered, (2) check all approved work schedules for any conflicts before execution, (3) generate weekly operational schedules, (4) repair schedules after changes and (5) generate quarterly schedules for planning. The AI Engine uses a rule representation combined with heuristic search and a genetic algorithm for scheduling. An iterative repair algorithm was used for dynamic rescheduling."

Proceedings of the International Conference on Automated Planning and Scheduling. Menlo Park, Calif.: AAAI Press. "The International Conference on Automated Planning and Scheduling Systems Conference came about from the merging of the International AI Planning and Scheduling Conference (AIPS) and the European Conference on Planning (ECP). Although the first conference under the new name was held in 2003, the organizers have honored the numbering of the two previous conferences. ICAPS 2003 is thus the thirteenth conference.

The Fifth International Conference on Artificial Intelligence Planning and Scheduling. By Anthony Barrett and Steve Chien. AI Magazine 21(4): Winter, 2000, 111-115.

POMDPs for Dummies. By Tony Cassandra, Brown University's Computer Science Department. "This is a tutorial aimed at trying to build up the intuition behind solution procedures for partially observable Markov decision processes (POMDPs). It sacrifices completeness for clarity. It tries to present the main problems geometrically, rather than with a series of formulas. In fact, we avoid the actual formulas altogether, try to keep notation to a minimum and rely on pictures to build up the intuition. We try to keep the required background to a minimum and provide some brief mini-tutorials on the required background material."

Planning Under Uncertainty. A collection of related publications co-authored by Thomas Dean,Professor of Computer Science,Brown University.

Why you should buy an emotional planner. By Jonathan Gratch. In Proceedings of the Agents'99 Workshop on Emotion-based Agent Architectures.

Intelligent Retail Logistics Scheduling. John Rowe, Keith Jewers, Joe Sivayogan, Andrew Codd, and Andrew Alcock. AI Magazine 17(4): Winter 1996, 31-40. "The supply-chain integrated ordering network (SCION) depot-bookings system automates the planning and scheduling of perishable and nonperishable commodities and the vehicles that carry them into J. Sainsbury depots. This initiative is strategic, enabling the business to make the key move from weekly to daily ordering. The system is mission critical, managing the inward flow of commodities from suppliers into J. Sainsbury’s depots. The system leverages AI techniques to provide a business solution that meets challenging functional and performance needs. The SCION depot-bookings system is operational, providing schedules for 22 depots across the United Kingdom."

Multiagent Systems: A Survey from a Machine Learning Perspective. By Peter Stone and Manuela Veloso, Computer Science Department, Carnegie Mellon University. "Another example of a domain that requires MAS is hospital scheduling as presented in [20]. This domain from an actual case study requires different agents to represent the interests of different people within the hospital. Hospital employees have different interests, from nurses who want to minimize the patient's time in the hospital, to x-ray operators who want to maximize the throughput on their machines. Since different people evaluate candidate schedules with different criteria, they must be represented by separate agents if their interests are to be justly considered." - Multiagent Systems

AI on the Web: Planning. A resource companion to Stuart Russell and Peter Norvig's "Artificial Intelligence: A Modern Approach." that provides links to reference material, people, research groups, software, companies and much more.

AI Planning Resources. Maintained by Rob St. Amant. "This is a list of AI planners and where they were developed, or where implementations are currently accessible."

AIG, the Artificial Intelligence Group at NASA's Jet Propulsion Laboratory, California Institute of Technology, "performs basic research in the areas of Artificial Intelligence Planning and Scheduling, with applications to science analysis, spacecraft commanding, deep space network operations, and space transportation systems."

Intelligent Coordination and Logistics Laboratory (ICLL) at the Carnegie Mellon UniversityRobotics Institute. One of the projects you'll find there is Integrated Planning and Scheduling: "In collaboration with the researchers at SRI, we are investigating the development of techniques for tighter integration of planning and scheduling processes. Starting from pre-existing planning and scheduling technologies (SRI's CHIP HTN Planner and CMU's ACS scheduler), we have developed a joint planner/scheduler for air operations planning."

"The International Conference on Automated Planning and Scheduling (ICAPS) is the premier forum for researchers and practitioners in planning and scheduling - two technologies critical to manufacturing, space systems, software engineering, robotics, education, and entertainment. The ICAPS conference resulted from merging two bi-annual conferences, namely the International Conference on Artificial Intelligence Planning and Scheduling (AIPS) and the European Conference on Planning (ECP)."

MAPGEN. From NASA Ames Research Center. "MAPGEN (Mixed Initiative Activity Planning Generator) is a ground-based decision support system for MER mission operations and science teams that begins to give content to the notion of autonomous planetary exploration. MAPGEN is called a mixed initiative planner. The essence is in human interaction with the advanced planning software. While the routine plan generation process is handled by the machine, the user brings unique knowledge and experience to bear to produce qualitatively good plans, said MAPGEN project lead Kanna Rajan. The paradigm is to enable the person using the software to critique a plan that the system automatically produces and ensure that resulting plans are viable within mission and flight rules."

• Read this interview with "principal investigator and project lead for the Mars Mission’s on-ground software effort, Kanna Rajan."
• Also see: Mixed-Initiative Planning in Space Mission Operations. By John L. Bresina and Paul H. Morris. AI Magazine 28(2): Summer 2007, 75. Abstract: "The MAPGEN system represents a successful mission infusion of mixed-initiative planning technology. MAPGEN was deployed as a mission-critical component of the ground operations system for the Mars Exploration Rover mission. Each day, the ground-planning personnel employ MAPGEN to collaboratively plan the activities of the Spirit and Opportunity rovers, with the objective of achieving as much science as possible while ensuring rover safety and keeping within the limitations of the rovers’ resources. The Mars Exploration Rover mission has now been operating for more than two years, and MAPGEN continues to be employed for activity plan generation for the Spirit and Opportunity rovers. During the multiyear deployment effort and subsequent mission operations experience, we have learned valuable lessons regarding application of mixed-initiative planning technology to mission operations. These lessons have spawned new research in mixed-initiative planning and have influenced the design of a new ground operations system, called M-SLICE, that is baselined for the Mars Science Laboratory mission. In this article, we discuss the mixed-initiative aspects of the MAPGEN system, focusing on the task, control, and awareness issues."

PLANET: European Network of Excellence in AI Planning. "PLANET is a coordinating organisation for European research and development in the field of Artificial Intelligence Planning and Scheduling and in particular aims to promote the transfer of this leading-edge technology into European industry." Be sure to check out their collection of resources.

POMDP information page. From Michael Littman. "A POMDP is a partially observable Markov decision process. It is a model, originating in the operations research (OR) literature, for describing planning tasks in which the decision maker does not have complete information as to its current state. The POMDP model provides a convenient way of reasoning about tradeoffs between actions to gain reward and actions to gain information."

• also see: POMDP Symposium Home Page - 1998 American Association for Artificial Intelligence Fall Symposium Planning with Partially Observable Markov Decision Processes.

Research in Planning Systems. Computer Science and Engineering, University of Washington. Scroll down their page to find descriptions of several projects, with links to papers and related web sites.

Other References Offline

Reinforcement Learning Repository at University of Massachusetts, Amherst.

• Topics > Planning: "The issue of planning is one that is common to Artificial Intelligence methods: an agent must devise a sequence of actions that leads from an initial state to a goal state. However, in the context of RL, planning is often required to be done under uncertainty."
• Technical Publications on Reinforcement Learning > Industrial Applications & Planning.