The stand-allocation system (SAS) is an AI application developed for the Hong Kong International Airport (HKIA) at Chek Lap Kok. The system ensures a high standard of quality in customer service, airport safety, and use of stand resources. This article describes our experience in developing an AI system using standard off-the-shelf software components. SAS is an example of how development methodologies used to construct modern AI applications have become fully inline with mainstream practices.

In spreadsheets, numeric data are represented as exact numbers and their mutual relations as functions, whose values (output) are computed from given argument values (input). However, in the real world, data are often inexact and uncertain in many ways, and the relationships, that is, constraints, between input and output are far more complicated. This article shows that interval constraint solving, an emerging AI-based technology, provides a more versatile and useful foundation for spreadsheets. The idea has been successfully integrated with Microsoft excel as the add-in interval solver that seamlessly upgrades the arithmetic core of excel into interval constraint solving.

In addition, to cope with conflicts caused by changes in actual operations, the airport authority also needs to make real-time problem-solving decisions on stand reassignments. the Hong Kong International Airport The stand-allocation system ( Figure world's busiest international airports in terms 1 is a snapshot of the The Although there were some initial hitches when system is installed and used in the Airport the new airport opened on 6 July 1998, operations Control Center (ACC), which is located in the quickly returned to normal within a control tower. Within a month, operational statistics management, and reactive scheduling capabilities surpassed those of the old airport--80 for stand management. The system supports percent of all flights were on time or within 15 concurrent use by multiple operators in minutes of schedule, all passengers cleared nonstop 24-hour-a-day operations because immigration within 15 minutes, and average HKIA is a 24-hour airport. Typically, a human operator must have several years of experience to acquire enough knowledge about airport operations before he/she can produce a "good" quality stand-assignment plan. Generating an allocation plan manually not only requires a highly experienced individual but is also very time consuming because it requires balancing many objectives against many possible alternatives.

In this project, we have developed the ramp activity coordination expert system (races) to solve aircraft-parking problems. races includes a knowledge-based scheduling system that assigns all daily arriving and departing flights to the gates and remote spots with domain-specific knowledge and heuristics acquired from human experts. races processes complex scheduling problems such as dynamic interrelations among the characteristics of remote spots-gates and aircraft with various other constraints, for example, customs and ground-handling factors, at an airport. 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. races is also designed to deal with the partial adjustment of the schedule when unexpected events occur. 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. Because the modification of the aircraft dispositions, such as aircraft changes and cancellations of flights, is reflected in the current schedule, the modification should be sent to races from the mainframe for the reactive scheduling. The adjustments of the schedule are made semiautomatically by races because there are many irregularities in dealing with the partial rescheduling.

There is a fundamental mismatch between the computational basis of spreadsheets and our knowledge of the real world. In spreadsheets, numeric data are represented as exact numbers and their mutual relations as functions, whose values (output) are computed from given argument values (input). However, in the real world, data are often inexact and uncertain in many ways, and the relationships, that is, constraints, between input and output are far more complicated. This article shows that interval constraint solving, an emerging AI-based technology, provides a more versatile and useful foundation for spreadsheets. The new computational basis is 100-percent downward compatible with the traditional spreadsheet paradigm. The idea has been successfully integrated with Microsoft excel as the add-in interval solver that seamlessly upgrades the arithmetic core of excel into interval constraint solving. The product has been downloaded by thousands of end users all over the world and has been used in various applications in business computing, engineering, education, and science. There is an intriguing chance for a major breakthrough of the AI technology on the spreadsheet platform: Tens of millions of excel users are making important decisions based on spreadsheet calculations.

In this paper we propose a new type of random CSP model, called Model RB, which is a revision to the standard Model B. It is proved that phase transitions from a region where almost all problems are satisfiable to a region where almost all problems are unsatisfiable do exist for Model RB as the number of variables approaches infinity. Moreover, the critical values at which the phase transitions occur are also known exactly. By relating the hardness of Model RB to Model B, it is shown that there exist a lot of hard instances in Model RB.

We introduce a temporal model for reasoning on disjunctive metric constraints on intervals and time points in temporal contexts. This temporal model is composed of a labeled temporal algebra and its reasoning algorithms. The labeled temporal algebra defines labeled disjunctive metric point-based constraints, where each disjunct in each input disjunctive constraint is univocally associated to a label. Reasoning algorithms manage labeled constraints, associated label lists, and sets of mutually inconsistent disjuncts. These algorithms guarantee consistency and obtain a minimal network. Additionally, constraints can be organized in a hierarchy of alternative temporal contexts. Therefore, we can reason on context-dependent disjunctive metric constraints on intervals and points. Moreover, the model is able to represent non-binary constraints, such that logical dependencies on disjuncts in constraints can be handled. The computational cost of reasoning algorithms is exponential in accordance with the underlying problem complexity, although some improvements are proposed.

In 1986, Tanner and Mead [1] implemented an interesting constraint satisfaction circuit for global motion sensing in a VLSI. We report here a new and improved a VLSI implementation that provides smooth optical flow as well as global motion in a two dimensional visual field. The computation of optical flow is an ill-posed problem, which expresses itself as the aperture problem. However, the optical flow can be estimated by the use of regularization methods, in which additional constraints are introduced in terms of a global energy functional that must be minimized. We show how the algorithmic constraints of Hom and Schunck [2] on computing smooth optical flow can be mapped onto the physical constraints of an equivalent electronic network.

In 1986, Tanner and Mead [1] implemented an interesting constraint satisfaction circuit for global motion sensing in a VLSI. We report here a new and improved a VLSI implementation that provides smooth optical flow as well as global motion in a two dimensional visual field. The computation of optical flow is an ill-posed problem, which expresses itself as the aperture problem. However, the optical flow can be estimated by the use of regularization methods, in which additional constraints are introduced in terms of a global energy functional that must be minimized. We show how the algorithmic constraints of Hom and Schunck [2] on computing smooth optical flow can be mapped onto the physical constraints of an equivalent electronic network.

In 1986, Tanner and Mead [1] implemented an interesting constraint satisfaction circuitfor global motion sensing in aVLSI. We report here a new and improved aVLSI implementation that provides smooth optical flow as well as global motion in a two dimensional visual field. The computation ofoptical flow is an ill-posed problem, which expresses itself as the aperture problem. However, the optical flow can be estimated by the use of regularization methods, in which additional constraints are introduced interms of a global energy functional that must be minimized. We show how the algorithmic constraints of Hom and Schunck [2] on computing smoothoptical flow can be mapped onto the physical constraints of an equivalent electronic network.