"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."
– Planning entry by Austin Tate in the MIT Encyclopedia of Cognitive Science.
The modern space race is heating up, and the European Union is acutely aware that it needs to keep pace. Space chief Thierry Breton told Reuters in an interview that the EU is accelerating its plans in light of rapid progress by private companies like SpaceX as well as China's successes. It's moving the deployment of its Galileo navigation satellites ahead by three years, to 2024, and will use its budget for the first time to support reusable rockets and other new launch tech. The EU is also forging a €1 billion deal with Arianespace to spur innovation, and will propose a €1 billion European Space Fund and competitions to foster startups. Breton also hoped to launch a pan-European satellite broadband network as well as a system to avoid collisions with satellites and other items in orbit.
When it comes to artificial intelligence (AI), countless conference sessions and seminars have dedicated inconceivable amount of hours asking what-if questions, with terrifying examples from across science fiction acting as the bleak backgrounds. Terminator's Skynet, Agents in The Matrix, and Ava in Ex Machina are just some of the fictional antagonists which have stemmed from humanity's own creations. But one franchise has spent over 50 years diving deeper than its contemporaries to depict scenarios of AI enhancing life, and in some cases not so – and that is Star Trek. Gene Roddenberry's utopic vision of the future has led to some of the most thought-provoking media to come to life. Topics of race and discrimination, death, and morality are some of the cornerstone topics that kept it relevant across multiple iterations for so long.
Housing Secretary Robert Jenrick still has questions to answer over his role in a planning case involving a Tory donor, Sir Keir Starmer has said. The Labour leader told the BBC the matter was "far from closed" but stopped short of calling for the minister's resignation. Mr Jenrick is under fire after granting permission for a luxury housing development to donor Richard Desmond. Downing Street said the PM had full confidence in the minister. Mr Jenrick says he was motivated by a desire to see more homes built when he overruled government inspectors to give the green light to Mr Desmond's plans for a 1,500 home development at the former Westferry printing works, in London's Isle of Dogs.
Satellite domains are becoming a fashionable area of research within the AI community due to the complexity of the problems that satellite domains need to solve. With the current U.S. and European focus on launching satellites for communication, broadcasting, or localization tasks, among others, the automatic control of these machines becomes an important problem. Many new techniques in both the planning and scheduling fields have been applied successfully, but still much work is left to be done for reliable autonomous architectures. The purpose of this article is to present CONSAT, a real application that plans and schedules the performance of nominal operations in four satellites during the course of a year for a commercial Spanish satellite company, HISPASAT. For this task, we have used an AI domain-independent planner that solves the planning and scheduling problems in the HISPASAT domain thanks to its capability of representing and handling continuous variables, coding functions to obtain the operators' variable values, and the use of control rules to prune the search.
Daewoo Shipbuilding Company, one of the largest shipbuilders in the world, has experienced great deal of trouble with the planning and scheduling of its production process. To solve the problems, from 1991 to 1993, Korea Advanced Institute of Science and Technology (KAIST) and Daewoo jointly conducted the Daewoo Shipbuilding Scheduling (das) Project. To integrate the scheduling expert systems for shipbuilding, we used a hierarchical scheduling architecture. To automate the dynamic spatial layout of objects in various areas of the shipyard, we developed spatial scheduling expert systems. For reliable estimation of person-hour requirements, we implemented the neural network-based person-hour estimator.
For many years, research in AI plan generation was governed by a number of strong, simplifying assumptions: The planning agent is omniscient, its actions are deterministic and instantaneous, its goals are fixed and categorical, and its environment is static. More recently, researchers have developed expanded planning algorithms that are not predicated on such assumptions, but changing the way in which plans are formed is only part of what is required when the classical assumptions are abandoned. The demands of dynamic, uncertain environments mean that in addition to being able to form plans -- even probabilistic, uncertain plans -- agents must be able to effectively manage their plans. In this article, which is based on a talk given at the 1998 AAAI Fall Symposium on Distributed, Continual Planning, we first identify reasoning tasks that are involved in plan management, including commitment management, environment monitoring, alternative assessment, plan elaboration, metalevel control, and coordination with other agents. We next survey approaches we have developed to many of these tasks and discuss a plan-management system we are building to ground our theoretical work, by providing us with a platform for integrating our techniques and exploring their value in a realistic problem.
The 1998 Planning Competition at the AI Planning Systems Conference was the first of its kind. Its goal was to create planning domains that a wide variety of planning researchers could agree on to make comparison among planners more meaningful, measure overall progress in the field, and set up a framework for long-term creation of a repository of problems in a standard notation. A rules committee for the competition was created in 1997 and had long discussions on how the contest should go. One result of these discussions was the pddl notation for planning domains. This notation was used to set up a set of planning problems and get a modest problem repository started.
In this project, we have developed the ramp activity coordination expert system (races) to solve aircraft-parking problems. By user-driven modeling for end users and near-optimal knowledge-driven scheduling acquired from human experts, races can produce parking schedules for about 400 daily flights in approximately 20 seconds; human experts normally take 4 to 5 hours to do the same. Scheduling results in the form of Gantt charts produced by races are also accepted by the domain experts. After daily scheduling is completed, the messages for aircraft change, and delay messages are reflected and updated into the schedule according to the knowledge of the domain experts. By analyzing the knowledge model of the domain expert, the reactive scheduling steps are effectively represented as the rules, and the scenarios of the graphic user interfaces are designed.
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 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 planning to minimal-knowledge methods such as STRIPS-based planners and disjunctive planners. We argue that the former methods have advantages such as scalability, expressiveness, continuous plan modification during execution, and the ability to interact with humans.
Fast-forward (FF) was the most successful automatic planner in the Fifth International Conference on Artificial Intelligence Planning and Scheduling (AIPS '00) planning systems competition. Like the well-known hsp system, FF relies on forward search in the state space, guided by a heuristic that estimates goal distances by ignoring delete lists. It differs from HSP in a number of important details. This article describes the algorithmic techniques used in FF in comparison to hsp and evaluates their benefits in terms of run-time and solution-length behavior. Humans have a remarkable capability to perform a wide variety of physical and mental tasks without any measurements and any computations.