We propose and evaluate a number of solutions to the problem of calculating the cost to serve each location in a single-vehicle transport setting. Such cost to serve analysis has application both strategically and operationally in transportation. The problem is formally given by the traveling salesperson game (TSG), a cooperative total utility game in which agents correspond to locations in a travelling salesperson problem (TSP). The cost to serve a location is an allocated portion of the cost of an optimal tour. The Shapley value is one of the most important normative division schemes in cooperative games, giving a principled and fair allocation both for the TSG and more generally. We consider a number of direct and sampling-based procedures for calculating the Shapley value, and present the first proof that approximating the Shapley value of the TSG within a constant factor is NP-hard. Treating the Shapley value as an ideal baseline allocation, we then develop six proxies for that value which are relatively easy to compute. We perform an experimental evaluation using Synthetic Euclidean games as well as games derived from real-world tours calculated for fast-moving consumer goods scenarios. Our experiments show that several computationally tractable allocation techniques correspond to good proxies for the Shapley value.

Learning to provide appropriate assistance to people indifferent situations is an extremely important, but insufficientlyinvestigated machine learning task. Applications includehuman-robot and human-computer interactions settings to maximizing the benefits of assistive technologies. Three key challenges must be overcome to appropriately address this task: Complexity: the space of possible assistive policies can be very large, making many existing methods (e.g., fromreinforcement learning) too data inefficient to be practical. Noise and misspecification: observed human behavior is often noisy and parametric formulations that reduce complexity will typically suffer from model misspecification,leading to unboundedly sub-optimal assistance. Biasedness: data available for learning a model is biased by previously provided assistive actions, violating the typical assumptions of supervised learning. We develop a general framework for learning to assist in single intervention settings. The framework narrows the search for effective assistance by viewing previous behavior under assistance through a restricted set of statistics. Assistive policies for the worst-case context-assistance-outcome relationships satisfying these statistics are obtained. We embed the problem of learning how to assist users in cursor based target pointing tasks into this framework and outline its usage.

Recent increase in interest in using rotorcraft (helicopters and tilt-rotor craft) for public transportation has spurred research in making rotorcraft less noisy, particularly as they land. The ground noise associated with landing trajectories followed by rotorcraft depends in part on the changes in altitude and velocity of the rotorcraft during flight. Acoustic models of ground noise taking altitude and velocity effects into account can be used in an optimization process to determine a set of potentially quieter pilot operations. However, optimizing solely for acoustic properties produces patterns that abstract away from the environment in which the trajectory is flown. A quiet procedure flown over a residential area can create considerable annoyance. To overcome this limitation of acoustic-based optimization we propose a hybrid cost model for optimization that combines acoustic criteria with a land use model that views noise-sensitive areas around landing facilities as weighted obstacles. The result is a 3D route planning problem with obstacles. We introduce a system, called NORA (Noise Optimization for Rotorcraft Approach) that allows for the computation of trajectories that simultaneously solve for acoustically quiet patterns that also avoid land sensitive areas.

We introduce an application of self-driving vehicle technology to the problem of towing aircraft at busy airports from gate to runway and runway to gate. Autonomous towing can be supervised by human ramp- or ATC controllers, pilots, or ground crew. The controllers provide route information to the tugs, assisted by an automated route planning system. The planning system and tower and ground controllers work in conjunction with the tugs to make tactical decisions during operations to ensure safe and effective taxiing in a highly dynamic environment. We argue here for the potential for significantly reducing fuel emissions, fuel costs, and community noise, while addressing the added complexity of air terminal operations by increasing efficiency and reducing human workload. This paper describes work-in-progress for developing concepts and capabilities for autonomous engines-off taxiing using towing vehicles.

Energy consumption in commercial and educational buildings is impacted by group activities such as meetings, workshops, classes and exams, and can be reduced by scheduling these activities to take place at times and locations that are favorable from an energy standpoint. This paper improves on the effectiveness of energy-aware room-booking and occupancy scheduling approaches, by allowing the scheduling decisions to rely on an explicit model of the building's occupancy-based HVAC control. The core component of our approach is a mixed-integer linear programming (MILP) model which optimally solves the joint occupancy scheduling and occupancy-based HVAC control problem. To scale up to realistic problem sizes, we embed this MILP model into a large neighbourhood search (LNS). We obtain substantial energy reduction in comparison with occupancy-based HVAC control using arbitrary schedules or using schedules obtained by existing heuristic energy-aware scheduling approaches.

Western societies are facing demographic challenges due the rapid aging of their population. In this context, economic and social issues are emerging, such as an increasing number of elderly in need of home cares and a shortage of caregivers. Smart home technology has imposed itself as a potential avenue of solution to these important issues. Its goal is to provide adapted assistance to a semi-autonomous resident in the form of hints, suggestions, reminders, and to take preventive actions, for instance turning off the oven, in the case of an emergency. The main scientific challenge related to this kind of assistance concerns the problem of recognizing, in real time, of the on-going activities of the resident in order to provide punctual guidance for the completion of everyday tasks. In the literature, the majority of the proposed solutions for activity recognition exploit a complex and expensive network of intrusive sensors (i.e. infrared, radio-identification, electromagnetic, pressure, cameras, etc.). A recent and innovative way of performing activity recognition is based on the monitoring of electrical household appliances by analyzing the electrical signals solely at the main panel. This approach is less intrusive and required only one sensor. In this paper, we present new advancements in that field, which take the form of an efficient method for recognizing electrical appliances within smart home based on the analysis of the features of the load signatures (active and reactive power, FFT) and on the use of the C4.5 algorithm to extract decision trees. This method has been implemented and tested in real smart home infrastructure showing that it is economical, simple and efficient.

In the presented research project, a software and hardware infrastructure for parking space focussed inter-modal route planning in a public pilot region in Berlin is developed. One central topic is the development of a prediction system which gives an estimated occupancy for the parking spaces in the pilot region for a given date and time in the future. Occupancy data will be collected online by roadside parking sensors developed within the project. The occupancy prediction will be implemented using “Neural Gas” machine learning in combination with a proposed method which uses data threads to improve the prediction quality. In this paper, a short overview of the whole research project is given. Furthermore, the concept of the software framework and the learning methods are presented and first collected data is shown. The prediction method using data threads is explained in more detail.

Many experts believe that in the coming decades, artificial intelligence will change, and perhaps significantly reduce, the demand for human labor in the economy, but there remains much uncertainty about the accuracy of this claim and what to do about it. This paper identifies several ways in which the artificial intelligence community can help society to anticipate and shape such outcomes in a socially beneficial direction. First, different technical aspirations for the field of AI may be associated with different social outcomes, increasing the stakes of decisions made in the AI community. Second, the extent of researchers' efforts to apply AI to different social and economic domains will influence the distribution of cognition between humans and machines in those domains. Third, the AI community can play a key role in initiating a more nuanced and inclusive public discussion of the social and economic possibilities afforded by AI technologies. To pave the way for such dialogue, we suggest a line of research aimed at better understanding the nature, pace, and drivers of progress in AI in order to more effectively anticipate and shape AI's role in society.

The cultural and political implications of modern AI research are not some far off concern, they are things that affect the world in the here and now. From advanced control systems with advanced visualizations and image processing techniques that drive the machines of the modern military to the slow creep of a mechanized workforce, ethical questions surround us. Part of dealing with these ethical questions is not just speculating on what could be but teaching our students how to engage with these ethical questions. We explore the use of science fiction as an appropriate tool to enable AI researchers to help engage students and the public on the current state and potential impacts of AI.

Algorithm selection (AS) techniques — which involve choosing from a set of algorithms the one expected to solve a given problem instance most efficiently — have substantially improved the state-of-the-art in solving many prominent AI problems, such as SAT, CSP, ASP, MAXSAT, and QBF.Although several AS procedures have been introduced,not too surprisingly, none of them dominates all others across all AS scenarios.Furthermore, these procedures have parameters whose optimal values vary across AS scenarios.This holds specifically for the machine learning techniques that form the core of current AS proceduresand for their hyperparameters. Therefore, to successfully apply AS to new problems, algorithms and benchmark sets, two questions need to be answered:(i) how to select an AS approach and (ii) how to set its parameters effectively.We address both of these problems simultaneously by using automated algorithm configuration.Specifically, we demonstrate that we can use algorithm configurators to automatically configure clasp folio 2,which implements a large variety of different AS approaches and their respective parameters in a single highly parameterized algorithm framework.We demonstrate that this approach, dubbed auto folio, can significantly improve the performance of clasp folio 2 on 11 out of the 12 scenarios from the Algorithm Selection Library and leads to new state-of-the-art algorithm selectors for 8 of these scenarios.