If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
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.
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.
Mixed-initiative planning systems attempt to integrate human and AI planners so that the synthesis results in high-quality plans. In the AI community, the dominant model of planning is search. In state-space planning, search consists of backward and forward chaining through the effects and preconditions of operator representations. Although search is an acceptable mechanism to use in performing automated planning, we present an alternative model to present to the user at the interface of a mixed-initiative planning assistant. That is, we propose to model planning as a goal-manipulation task.
Real-time traffic signal control presents a challenging multiagent planning pro blem, particularly in urban road networks where, unlike simpler arterial settings, there are competing dominant traffic flows that shift through the day. Further complicating matters, urban environments require attention to multimodal traffic flows (vehicles, pedestrians, bicyclists, buses) that move at different speeds and may be given different priorities. For the past several years, my research group has been developing and refining a real-time, adaptive traffic signal control system to address these challenges, referred to as scalable urban traffic control (Surtrac). Combining principles from automated planning and scheduling, multiagent systems, and traffic theory, Surtrac treats traffic signal control as a decentralized online planning process. In operation, each intersection repeatedly generates and executes (in rolling horizon fashion) signal-timing plans that optimize the movement of currently sensed approaching traffic through the intersection.
SEOUL – When a man in Seoul tested positive for coronavirus in May, South Korean authorities were able to confirm his wide-ranging movements in and outside the city in minutes, including five bars and clubs he visited on a recent night out. The fast response -- well ahead of many other countries facing outbreaks -- was the result of merging South Korea's already advanced methods of collecting information and tracking the virus into a new data sharing system that patches together cellphone location data and credit card records. The Epidemic Investigation Support System (EISS), introduced in late March, effectively removed technological barriers to sharing that information between authorities, by building on the country's "Smart City" data system. That platform was originally designed to let local authorities share urban planning information, from population to traffic and pollution, by uploading data in Excel spreadsheets and other formats. Now it forms the foundation for a data clearinghouse that has turbocharged South Korea's response to the virus.