Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The orienteering problem: A survey

Unmanned aerial vehicles (UAVs) have found numerous applications and are expected to bring fertile business opportunities in the next decade. Among various enabling technologies for UAVs, wireless communication is essential and has drawn significantly growing attention in recent years. Compared to the conventional terrestrial communications, UAVs’ communications face new challenges due to their high altitude above the ground and great flexibility of movement in the 3-D space. Several critical issues arise, including the line-of-sight (LoS) dominant UAV-ground channels and induced strong aerial-terrestrial network interference, the distinct communication quality-of-service (QoS) requirements for UAV control messages versus payload data, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the exploitation of the new design degree of freedom (DoF) brought by the highly controllable 3-D UAV mobility. In this article, we give a tutorial overview of the recent advances in UAV communications to address the above issues, with an emphasis on how to integrate UAVs into the forthcoming fifth-generation (5G) and future cellular networks. In particular, we partition our discussion into two promising research and application frameworks of UAV communications, namely UAV-assisted wireless communications and cellular-connected UAVs, where UAVs are integrated into the network as new aerial communication platforms and users, respectively. Furthermore, we point out promising directions for future research.

Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond

Information and Communication Technologies in Tourism

Unmanned aerial vehicles (UAVs), or aerial drones, are an emerging technology with significant market potential. UAVs may lead to substantial cost savings in, for instance, monitoring of difficult-to-access infrastructure, spraying fields and performing surveillance in precision agriculture, as well as in deliveries of packages. In some applications, like disaster management, transport of medical supplies, or environmental monitoring, aerial drones may even help save lives. In this article, we provide a literature survey on optimization approaches to civil applications of UAVs. Our goal is to provide a fast point of entry into the topic for interested researchers and operations planning specialists. We describe the most promising aerial drone applications and outline characteristics of aerial drones relevant to operations planning. In this review of more than 200 articles, we provide insights into widespread and emerging modeling approaches. We conclude by suggesting promising directions for future research.

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

Iterated local search for the team orienteering problem with time windows

Research highlights? City Trip Planner integrates selection of attractions with routing between them. ? It provides personalised interest estimation. ? The real-time planning takes into account personal constraints and opening hours. ? Usage statistics and user feedback demonstrate the appreciation by tourists. Over the last few years, advanced digital applications have become available to tourists. Some of these offer the possibility of creating personalised routes. This paper introduces a tourist expert system, called the City Trip Planner, that allows planning routes for five cities in Belgium. It is implemented as a web application that takes into account the interests and trip constraints of the user and matches these to a database of locations in order to predict personal interests. A fast and effective planning algorithm provides an on-the-fly suggestion of a personal trip for a requested number of days, taking into account opening hours of attractions and time for a (lunch) break. The expert system is discussed in detail. Usage statistics and user feedback demonstrate that it is highly appreciated by tourists.

The City Trip Planner

A guided Local Search Metaheuristic for the Team Orienteering Problem

Mobile tourist guides evolve towards automated personalized tour planning devices. The contribution of this article is to put forward a combined artificial intelligence and metaheuristic approach to solve tourist trip design problems (TTDP). The approach enables fast decision support for tourists on small footprint mobile devices. The orienteering problem, which originates in the operational research literature, is used as a starting point for modelling the TTDP. The problem involves a set of possible locations having a score and the objective is to maximize the total score of the visited locations, while keeping the total time (or distance) below the available time budget. The score of a location represents the interest of a tourist in that location. Scores are calculated using the vector space model, which is a well-known technique from the field of information retrieval. The TTDP is solved using a guided local search metaheuristic. In order to compare the performance of this approach with an algorithm that appeared in the literature, both are applied to a real data set from the city of Ghent. A collection of tourist points of interest with descriptions was indexed and subsequently queried with popular interests, which resulted in a test set of TTDPs. The approach presented in this article turns out to be faster and produces solutions of better quality.

http://wwwis.win.tue.nl/bnaic2009/papers/bnaic2009_paper_6.pdf

Wouter Souffriau

Papers

The orienteering problem: A survey

Iterated local search for the team orienteering problem with time windows

The City Trip Planner

A guided Local Search Metaheuristic for the Team Orienteering Problem

A Personalized Tourist Trip Design Algorithm For Mobile Tourist Guides