Opin vísindi
Opin vísindi er varðveislusafn vísindaefnis og doktorsritgerða í opnum aðgangi á vegum íslenskra háskóla og Landsbókasafns Íslands - Háskólabókasafns.
Opinn aðgangur að rannsóknaniðurstöðum er í samræmi við 10. gr. laga nr. 3/2003 um opinberan stuðning við vísindarannsóknir sem og kröfur innlendra og erlendra rannsóknasjóða. Markmiðið með opnum aðgangi er að niðurstöður rannsókna séu aðgengilegar sem flestum óhindrað og án endurgjalds á rafrænu formi. Vistun í varðveislusafninu er varanleg og ætlað að tryggja aðgang að vísindaefni íslenskra háskóla í opnum aðgangi um ókomna tíð. Varðveislusafnið Opin vísindi er tengt við rannsóknagáttina IRIS og rannsóknaniðurstöður í opnum aðgangi sem eru skráðar í IRIS eru um leið vistaðar og gerðar aðgengilegar til framtíðar í varðveislusafninu. Með því að safna þessu efni saman í eitt safn verður aðgangur að því einfaldur og þægilegur fyrir alla sem vilja kynna sér það og geta þannig notið þess öfluga vísindastarfs sem fram fer í háskólum landsins.
Varðveislusafnið er OpenAIRE / OpenAIREplus samhæft og samrýmist kröfum sem gerðar eru um birtingu rannsóknaniðurstaðna úr verkefnum sem styrkt eru úr evrópsku rannsóknaáætlununum FP7 og H2020.
Varðveislusafnið notar opna hugbúnaðinn DSpace.
Opinn aðgangur að rannsóknaniðurstöðum er í samræmi við 10. gr. laga nr. 3/2003 um opinberan stuðning við vísindarannsóknir sem og kröfur innlendra og erlendra rannsóknasjóða. Markmiðið með opnum aðgangi er að niðurstöður rannsókna séu aðgengilegar sem flestum óhindrað og án endurgjalds á rafrænu formi. Vistun í varðveislusafninu er varanleg og ætlað að tryggja aðgang að vísindaefni íslenskra háskóla í opnum aðgangi um ókomna tíð. Varðveislusafnið Opin vísindi er tengt við rannsóknagáttina IRIS og rannsóknaniðurstöður í opnum aðgangi sem eru skráðar í IRIS eru um leið vistaðar og gerðar aðgengilegar til framtíðar í varðveislusafninu. Með því að safna þessu efni saman í eitt safn verður aðgangur að því einfaldur og þægilegur fyrir alla sem vilja kynna sér það og geta þannig notið þess öfluga vísindastarfs sem fram fer í háskólum landsins.
Varðveislusafnið er OpenAIRE / OpenAIREplus samhæft og samrýmist kröfum sem gerðar eru um birtingu rannsóknaniðurstaðna úr verkefnum sem styrkt eru úr evrópsku rannsóknaáætlununum FP7 og H2020.
Varðveislusafnið notar opna hugbúnaðinn DSpace.
Nýlega bætt við
Erratum : Estimates of global, regional, and national incidence, prevalence, and mortality of HIV, 1980-2015: The Global Burden of Disease Study 2015 (The Lancet HIV (2016) 3 (e361-e387) PII: S235230181630087X DOI: 10.1016/S2352-3018(16)30087-X)
(2016-09-01) GBD 2015 HIV Collaborators; Department of Psychology
GBD 2015 HIV Collaborators. Estimates of global, regional, and national incidence, prevalence, and mortality of HIV, 1980–2015: the Global Burden of Disease Study 2015. Lancet HIV 2016; 3: e361–87—In this Article, Kerrie E Doyle and David M Pereira have been added to the list of collaborators and Claudia C Pereira has been removed. These corrections have been made as of Aug 22, 2016.
Predictive models of the 2015 Rugby World Cup : Accuracy and application
(2016-07) O'Donoghue, P.; Ball, D.; Eustace, J.; McFarlan, B.; Nisotaki, M.; Department of Sport Science
The current investigation compared 12 models of outcomes of international rugby union matches and then used the most accurate model to investigate performances within the 2015 Rugby World Cup. The underlying linear regression models were used within a simulation package that introduced random variability about performance evidenced by the residual distribution of the regression analyses. Each model was used within 10,000 simulations of the 2015 Rugby World Cup from which match outcome and team progression statistics were recorded. The most accurate model with respect to the actual 2015 tournament was developed using data from all seven previous tournaments rather than restricting cases to the most recent three tournaments. The model was more accurate when the data used violated the assumptions of linear regression rather than transforming variables to satisfy the assumptions. The model included World ranking points as a predictor variable and was more accurate than corresponding models that represented relative home advantage as well. The most accurate model used separate models for the pool and knockout stage matches although the 9 models that separating these match types were less accurate on average than when the two match types were considered together. This model was used to investigate properties of the 2015 Rugby World Cup. The tournament disadvantaged three teams in the World's top 5 who were drawn in the same pool. Teams ranked in the World's top 7 did not perform as well as predicted while teams ranked 16th and below performed better than predicted suggesting that the strength in depth in international rugby union is increasing. There was a small effect of having additional recovery days from the previous match compared to the opponents which was worth 4.1 points. The information produced by this research should be considered by those design tournaments such as the Rugby World Cup.
Learning rules of simplified boardgames by observing
(IOS Press BV, 2012) Björnsson, Yngvi; Department of Computer Science
General Game Playing (GGP) agents learn strategies to skillfully play a wide variety of games when given only the rules of the game. The rules are provided in a language called Game Description Language (GDL) and specify the initial game setup, what constitutes legal moves and how they update the game state when played, how the game terminates, and what the outcome is. In here we extend this line of research further, that is, we assume that the game-playing agent must learn the rules of a game by observing others play instead of them being provided. Our focus here will mainly be on modeling piece movements with less attention placed on the remaining game-rule properties. We define a subset of games, we name simplified boardgames, that despite constituting only a small subset of games expressible in GDL nonetheless encapsulate a large variety of interesting piece movement patterns found in popular boardgames. We provide a well-defined formalism and a practicable algorithm for learning game rules of simplified boardgames. We empirically evaluate the learning algorithm on different boardgames and under different assumptions of availability of observations. Furthermore, we show that our formalism offers at least an order of magnitude speedup over state-of-the-art logic-based GDL reasoners for fitting boardgames. The method is thus directly relevant for GGP systems.
Case-based subgoaling in real-time heuristic search for video game pathfinding
(2010) Bulitko, Vadim; Björnsson, Yngvi; Lawrence, Ramon; Department of Computer Science
Real-time heuristic search algorithms satisfy a constant bound on the amount of planning per action, independent of problem size. As a result, they scale up well as problems become larger. This property would make them well suited for video games where Artificial Intelligence controlled agents must react quickly to user commands and to other agents' actions. On the downside, real-time search algorithms employ learning methods that frequently lead to poor solution quality and cause the agent to appear irrational by re-visiting the same problem states repeatedly. The situation changed recently with a new algorithm, D LRTA*, which attempted to eliminate learning by automatically selecting subgoals. D LRTA* is well poised for video games, except it has a complex and memory-demanding pre-computation phase during which it builds a database of subgoals. In this paper, we propose a simpler and more memory-efficient way of pre-computing subgoals thereby eliminating the main obstacle to applying state-of-the-art real-time search methods in video games. The new algorithm solves a number of randomly chosen problems off-line, compresses the solutions into a series of subgoals and stores them in a database. When presented with a novel problem on-line, it queries the database for the most similar previously solved case and uses its subgoals to solve the problem. In the domain of pathfinding on four large video game maps, the new algorithm delivers solutions eight times better while using 57 times less memory and requiring 14% less pre-computation time.
Automated discovery of search-extension features
(2010) Skowronski, Pálmi; Björnsson, Yngvi; Winands, Mark H.M.; Department of Computer Science
One of the main challenges with selective search extensions is designing effective move categories (features). Usually, it is a manual trial-and-error task, which requires both intuition and expert human knowledge. Automating this task potentially enables the discovery of both more complex and more effective move categories. The current work introduces Gradual Focus, an algorithm for automatically discovering interesting move categories for selective search extensions. The algorithm iteratively creates new more refined move categories by combining features from an atomic feature set. Empirical data is presented for the game Breakthrough showing that Gradual Focus looks at a number of combinations that is two orders of magnitude fewer than a brute-force method does, while preserving adequate precision and recall.
Flokkar í Opnum vísindum
Veldu flokk til að skoða.
- University of Iceland
- University of Akureyri
- Bifröst University
- Hólar University College
- IRIS
- Agricultural University of Iceland
- National and University Library of Iceland
- Iceland University of the Arts