NASA and the international aviation community are investing in the development of a commercial transportation infrastructure that includes the increased use of rotorcraft, specifically helicopters and civil tilt rotors. However, there is significant concern over the impact of noise on the communities surrounding the transportation facilities. One way to address the rotorcraft noise problem is by exploiting powerful search techniques coming from artificial intelligence to design low-noise flight profiles that can be then validated though field tests. This article investigates the use of discrete heuristic search methods to design low-noise approach trajectories for rotorcraft. Our work builds on a long research tradition in trajectory optimization using either numerical methods or discrete search. Novel features of our approach include the use of a discrete search space with a resolution that can be varied, and the coupling of search with a robust simulator to evaluate candidates. The article includes a systematic comparison of different search techniques; in particular, in the experiments, we are able to do a trade study that compares complete search algorithms such as A * with faster but approximate methods such as local search.

In the past decade, the number of Earth observation satellites has burgeoned. Present and future Earth observing missions will continue to study different aspects and interacting pieces of Earth's hydrosphere, lithosphere, atmosphere and biosphere. Scientists are designing increasingly complex, interdisciplinary campaigns to exploit the diverse capabilities of multiple Earth sensing assets. Currently, the scheduling of scientific observations for satellites in low Earth orbit is conducted independently by every mission operations center. There is a lack of information infrastructure to enable the scheduling of coordinated observations involving multiple sensors. This paper proposes a software architecture and describes a prototype system called DESOPS (Distributed Earth Science Observation Planning and Scheduling) to address this deficiency.

A number of reasoning problems involving the manipulation of temporal information can naturally be viewed as implicitly inducing an ordering of potential local decisions involving time (specifically, associated with durations or orderings of events) on the basis of preferences. For example, a pair of events might be constrained to occur in a certain order and, in addition, it might be preferable that the delay between the start times of each of them be as large, or as small, as possible. Sometimes, however, it is more natural to view preferences as something initially ascribed to complete solutions to temporal reasoning problems, rather than to local decisions. For example, in classical scheduling problems, the preference for solutions which minimize makespan is a global, rather than a local, condition. In such cases, it might be useful to learn the local preferences that contribute to globally preferred solutions. This information could be used in heuristics to guide the solver to more promising solutions. To address the potential requirement for information about local preferences, we propose to apply learning techniques to infer local preferences from global ones. The preliminary work proposes an approach based on the notion of learning a set of soft temporal constraints, given a training set of solutions to a Temporal CSP, and an objective function for evaluating each solution in the set.

Effective manipulation of temporal information about periodic events is required for solving complex problems such as long‐range scheduling or querying temporal information. Furthermore, many problems involving repeating events involve the optimization of temporal aspects of these events (e.g., minimizing make‐span in job‐shop scheduling). In this paper, a constraint‐based formulation of reasoning problems with repeating events is presented, and its complexity is analyzed for a range of problems. Optimization constraints are interpreted formally using the Semiring CSPs (SCSP) representation of optimization in constraint reasoning. This allows for familiar algorithms such as branch‐and‐bound to be applied to solving them.

Numerous examples of temporal reasoning involve a process of abstraction from the number of times an event is to occur or the number of times events stand in a temporal relation. For example, scheduling a recurring event such as one's office hours may consider things like the relative temporal ordering of the office hours and a number of other events in a given work day. The number of times office hours will actually be held may be unknown, even irrelevant, at the time of scheduling them. The objective of this article is to formulate a domain‐independent framework for reasoning about recurring events and their relations. To achieve this end, we propose an ontology of recurrence based on the model‐theoretic structure underlying collective predication using plural noun phrases. We offer a calculus of binary temporal relations for temporal collections based on a well‐defined transformation of interval temporal relations into recurrence relations. Finally, we describe a reasoning framework based on manipulating knowledge stored in temporal relation networks, which is in turn a specialization of the CSP (constraint satisfaction problem) framework. The reasoner manipulates recurrence relations in the network to determine the network's consistency or to generate scenarios.

Automated control of a large electrical power distribution network through a single controller can provide advantages in efficiency and reliability as well as reduction in maintenance costs. For control to be most effective, it is necessary that a global view of the entire network be had by the controller, so that it can reason as to the cause of the readings of the various sensing devices located throughout the network, Traditional approaches to power system control have involved a set of local devices (i.e., protective relays) that base their decision on the instantaneous reading of a single sensor. These single-parameter decisions can sometimes be incorrect due to sensor failures. Furthermore, a special type of fault called a soft fault, where a fault impedance limits the current to a value below the relay operating point, are nearly impossible to detect with decisions based on a single local parameter. By reasoning over an entire suit of sensing devices spread throughout the entire network, protection decisions based on a global view can become more reliable as well as comprehensive. Some previous approaches have implemented global control with varying degrees of success through the use of rule-based knowledge-based systems. This paper describes an alternative knowledge-based approach that makes use of so-called models of structure-and-behavior, to which model-based diagnosis is applied. The objective of this approach is to develop a system that can reliably diagnose faults in power distribution networks (especially soft faults), identify sensor failures, and carry out appropriate corrective action automatically. An intelligent power controller (IPC) which has these capabilities is described. This IPC was rigorously tested in an DC electrical power distribution system testbed and found to successfully carry out the required functions. This paper also describes in detail the tests and the conclusions drawn from their results.

Traditional interval-based representations of time assume interval convexity, i.e., that intervals are uninterrupted. This assumption makes it difficult to represent the common sense notion of a single event with ‘gaps’. Having a representation of this species of event contributes favorably to the ability of the human or machine to solve certain tasks, such as planning or database retrieval. This paper defines two kinds of discourse and knowledge object which comprise collections of convex intervals. Although other researchers have suggested the need for a relaxation of the assumption of convexity in event representation, there has been no attempt to offer a concise representation of gapped events. The formulation employed here to introduce gapped events is an extension of James Allen's interval-based approach to time representation. Allen's calculus of thirteen binary relations defined between two convex intervals is generalized to a matrix of binary relations between each pair of subintervals of pairs of non-convex intervals. A primary goal of this paper is to show that this extension of the interval-based approach increases the expressive power of the calculus, while retaining its computational advantages.