The Next-Generation Transportation program coordinates the evolution and transformation of the current air-traffic management (ATM) system for the National Airspace System (NAS). Currently the NAS has a limited capacity and cannot handle the increasing future air traffic demands. However, before newly proposed ATM concepts are deployed they must be rigorously evaluated under realistic conditions. This paper presents AGENTFLY, an emerging NAS-wide highfidelity multi-agent ATM simulator with precise emulation of the human controller operation workload model and human-system interaction. The simulator is validated using a flight scenario developed by the U.S. Federal Aviation Administration that is based on real data. We present preliminary results focusing on the accuracy of the simulated controllers within AGENTFLY.

Adaptive Cruise Control (ACC) is a technology that allows a vehicle to automatically adjust its speed to maintain a preset distance from the vehicle in front of it based on the driver's preferences. Individual drivers have different driving styles and preferences. Current systems do not distinguish among the users. We introduce a method to combine machine learning algorithms with demographic information and expert advice into existing automated assistive systems. This method can save on the interactions between drivers and automated systems by adjusting parameters relevant to the operation of these systems based on their specific drivers and context of drive. We also learn when users tend to engage and disengage the automated system. This method sheds light on the kinds of dynamics that users develop while interacting with automation and can teach us how to improve these systems for the benefit of their users. While accepted packages such as Weka were successful in learning drivers' behavior, we found that improved learning models could be developed by adding information on drivers' demographics and a previously developed model about different driver types. We present the general methodology of our learning procedure and suggest applications of our approach to other domains as well.

NASA and the international community are investing in the development of a commercial transportation infrastructure that includes the increased use of rotorcraft, specifically heli- copters and civil tilt rotors. However, there is significant con- cern 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 coupled with simulation and field tests to design low-noise flight profiles which can be tested in simulation or through field tests. This paper in- vestigates the use of simulation based on predictive physical models to facilitate the search for low-noise trajectories using local search combined with a robust noise simulator.

This paper shows how to improve the recommendations of an interaction-based collaborative filtering (IBCF) recommender used in online dating. Previous work has shown that IBCF works well in this domain, although it tends to rank popular candidates highly, which leads to these users receiving a large number of contacts. We address this problem by using a Decision Tree model as a "critic" to re-rank the candidates generated by IBCF, effectively promoting less popular candidates. This method was first evaluated on historical data from a large online dating site and then trialled live on the same site by providing recommendations to a large number of users throughout a 9 week period. The live trial confirmed the consistency of the analysis on historical data and the ability of the method to generate suitable candidates over an extended period. Our recommendations gave higher success rates than those for a control group made with a baseline recommender.

This paper introduces a new technique for intelligently exploring the reachability set of a spacecraft: the set of trajectories from a given initial condition that are possible under a specified range of control actions. The high dimension of this problem and the nonlinear nature of gravitational interactions make the geometry of these sets complicated, hard to compute, and all but impossible to visualize. Currently, exploration of a problem’s state space is done heuristically, based on previously identified solutions. This potentially misses out on improved mission design solutions that are not close to previous approaches. The goal of the work described here is to map out reachability sets automatically. This would not only aid human mission planners, but also allow a spacecraft to determine its own course without input from Earth-based controllers. Brute-force approaches to this are computationally prohibitive, so one must focus the effort on regions that are of interest: where neighboring trajectories diverge quickly, for instance, or come close to a body that the spacecraft is orbiting. In this paper, we focus on the first of those two criteria; the goal is to identify regions in the system’s state space where small changes have large effects— or vice versa—and concentrate the computational mesh accordingly.

With rapid profusion of video data, automated surveillanceand intrusion detection is becoming closer to reality. In orderto provide timely responses while limiting false alarms, an intrusiondetection system must balance resources (e.g., time)and accuracy. In this paper, we show how such a system canbe modeled with a partially observable Markov decision process(POMDP), representing possible computer vision filtersand their costs in a way that is similar to human vision systems.The POMDP representation can be optimized to producea dynamic sequence of operations and achieve a tradeoffbetween time and detection quality, taking into accountuncertainty in the filter predictions. In a set of experiments onactual video data, we show that our method can both outperformstatic “expert” models and scale to large dynamic domains.These results suggest that our method could be usedin real-world intrusion detection systems.

This paper describes the development and empirical testing of an intelligent tutoring system (ITS) with two emerging methodologies: (1) a partially observable Markov decision process (POMDP) for representing the learner model and (2) inquiry modeling, which informs the learner model with questions learners ask during instruction. POMDPs have been successfully applied to non-ITS domains but, until recently, have seemed intractable for large-scale intelligent tutoring challenges. New, ITS-specific representations leverage common regularities in intelligent tutoring to make a POMDP practical as a learner model. Inquiry modeling is a novel paradigm for informing learner models by observing rich features of learners’ help requests such as categorical content, context, and timing. The experiment described in this paper demonstrates that inquiry modeling and planning with POMDPs can yield significant and substantive learning improvements in a realistic, scenario-based training task.

Spoken dialogue is increasingly central to systems that assist people. As the tasks that people and machines speak about together become more complex, however, users’ dissatisfaction with those systems is an important concern. This paper presents a novel approach to learning for spoken dialogue systems. It describes embedded wizardry, a methodology for learning from skilled people, and applies it to a library whose patrons order books by telephone. To address the challenges inherent in this application, we introduce RFW+, a domain-independent, feature-selection method that considers feature categories. Models learned with RFW+ on embedded-wizard data improve the performance of a traditional spoken dialogue system.

We present a methodology for the deployment of the max-sum algorithm, a well known decentralised algorithm for coordinating autonomous agents, for problems related to situational awareness. In these settings, unmanned autonomous vehicles are deployed to collect information about an unknown environment. Our methodology then helps identify the choices that need to be made to apply the algorithm to these problems. Next, we present a case study where the methodology is used to develop a system for disaster management in which a team of unmanned aerial vehicles coordinate to provide the first responders of the area of a disaster with live aerial imagery. To evaluate this system, we deploy it on two unmanned hexacopters in a variety of scenarios. Our tests show that the system performs well when confronted with the dynamism and the heterogeneity of the real world.

Qualitative representations are suitable for sketch understanding systems because they highlight important relationships while leaving out details that are not essential for conceptual understanding. These representations can be used to perform spatial analogies between sketches, which determine qualitative similarities and differences. However, there are cases where including quantitative information is necessary for accurately representing a sketch. We describe a method for using quantitative information to constrain qualitative spatial analogies. The utility of this method is demonstrated in the context of a sketch-based educational software system. Importantly, using quantitative information to improve analogical matches is not domain-specific. It can be used in any situation where qualitative and quantitative spatial information must be combined to accurately interpret a sketch. This approach has the potential to improve sketch understanding in educational software applications for highly spatial domains.