International Commissions of Inquiry in a Networked World: Unveiling the Roles of Diasporas through an Eritrean Case Study

Computer Systems, Imagery and Medi

Rational bidding using reinforcement learning: an application in automated resource allocation

The application of autonomous agents by the provisioning and usage of computational resources is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic resource provisioning and usage of computational resources, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a framework for supporting consumers and providers in technical and economic preference elicitation and the generation of bids. Secondly, we introduce a consumer-side reinforcement learning bidding strategy which enables rational behavior by the generation and selection of bids. Thirdly, we evaluate and compare this bidding strategy against a truth-telling bidding strategy for two kinds of market mechanisms – one centralized and one decentralized

A Students’ Preferences-Based Approach to Select Methods for Detecting and Handling Free-Riding

Free-riding is a serious challenge in group projects. While there are various methods to reduce free-riding, marketing educators still face a difficult task when selecting an appropriate method for their course. In this study, we propose a students’ preferences-based approach that supports marketing educators with the selection of methods to detect and handle free-riding. To measure these preferences, students completed an online survey based on a choice task about two methods to detect free-riding and a ranking task about four methods to handle free-riding (n = 254). Their answers were analyzed using chi-squared tests, Borda scores, and rank-ordered logit models. The results show that (a) neither Dutch nor international students have a clear preference for one of the two detection methods (the reporting system vs. the process evaluation system), (b) grade discussion (a possible reduction of the free-rider’s grade based on a conversation with the course coordinator about each student’s contribution) is the most preferred method to handle free-riding, and (c) international students have a stronger preference for stricter handling methods. Marketing educators can apply the proposed approach, or use our specific findings, for designing methods to reduce free-riding in their courses

Automated machine learning for remaining useful life estimation of aircraft engines

Algorithms and the Foundations of Software technolog

11 x 11 Domineering is Solved: The first player wins

We have developed a program called MUDoS (Maastricht University Domineering Solver) that solves Domineering positions in a very efficient way. This enables the solution of known positions so far (up to the 10 x 10 board) much quicker (measured in number of investigated nodes). More importantly, it enables the solution of the 11 x 11 Domineering board, a board up till now far out of reach of previous Domineering solvers. The solution needed the investigation of 259,689,994,008 nodes, using almost half a year of computation time on a single simple desktop computer. The results show that under optimal play the first player wins the 11 x 11 Domineering game, irrespective if Vertical or Horizontal starts the game. In addition, several other boards hitherto unsolved were solved. Using the convention that Vertical starts, the 8 x 15, 11 x 9, 12 x 8, 12 x 15, 14 x 8, and 17 x 6 boards are all won by Vertical, whereas the 6 x 17, 8 x 12, 9 x 11, and 11 x 10 boards are all won by Horizontal

Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services

The application of autonomous agents by the provisioning and usage of computational services is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic service provisioning and usage of Grid services, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a bidding agent framework for implementing artificial bidding agents, supporting consumers and providers in technical and economic preference elicitation as well as automated bid generation by the requesting and provisioning of Grid services. Secondly, we introduce a novel consumer-side bidding strategy, which enables a goal-oriented and strategic behavior by the generation and submission of consumer service requests and selection of provider offers. Thirdly, we evaluate and compare the Q-strategy, implemented within the presented framework, against the Truth-Telling bidding strategy in three mechanisms – a centralized CDA, a decentralized on-line machine scheduling and a FIFO-scheduling mechanisms

Expansion tube capabilities for studying boost-glide re-entry conditions

The expansion tube is a unique hypersonic impulse facility capable of producing both high-enthalpy and high total pressure conditions simultaneously through the unsteady expansion of a non-stagnated test flow. When coupled with high-performance free-piston or detonation drivers, expansion tubes allow for the simulation of such conditions as scaled Earth re-entry, scaled entry into the atmospheres of other planets in the solar system, and high-speed flight through the Earth’s atmosphere. This paper focuses on the latter case and considers the capabilities of expansion tubes for re-creating the conditions experienced at various parts of the re-entry trajectory of a boost-glide vehicle. Boost-glide vehicles are a type of hypersonic vehicle which is generally boosted just outside the atmosphere by a rocket before ‘gliding’ down through the Earth’s atmosphere to a target, often re-entering at very high-speeds for atmospheric flight of up to Mach 22 (greater than 6 km/s). In a military sense, they are strategically important and are currently being developed by several nations around the world. The expansion tube’s unique ability to simulate high-enthalpy and high total pressure flight makes it particularly well suited to the study of these conditions. This paper will present expansion tube performance envelopes compared to planned boost-glide trajectories, as well as considering specific facility considerations required to generate these conditions