Predicting performance in team games: The automatic coach

This is an electronic version of the paper presented at the 3rd International Conference on Agents and Artificial Intelligence, held in Rome on 2011A wide range of modern videogames involves a number of players collaborating to obtain a common goal. The way the players are teamed up is usually based on a measure of performance that makes players with a similar level of performance play together. We propose a novel technique based on clustering over observed behaviour in the game that seeks to exploit the particular way of playing of every player to find other players with a gameplay such that in combination will constitute a good team, in a similar way to a human coach. This paper describes the preliminary results using these techniques for the characterization of player and team behaviours. Experiments are performed in the domain of Soccerbots.This work has been partly supported by: Spanish Ministry of Science and Education under grant TIN2009-13692-C03-03, TIN2010-19872 and Spanish Ministry of Industry under grant TSI, 020110- 2009-205

Aprendizaje basado en juegos

Gracias al incremento de potencia de los ordenadores, gran cantidad de personas dedican horas y horas a aprovechar su aspecto más lúdico, los videojuegos. Por otro lado, existen programas educativos que aprovechan la infinita paciencia de los ordenadores que les hacen capaces de explicar conceptos una y otra vez hasta que los alumnos lo entiendan. En este artículo mostramos qué cosas pueden aportar las aplicaciones de enseñanza a los videojuegos y viceversa. Terminamos describiendo JV2M, como un ejemplo de sistema de aprendizaje basado en juegos

Measuring Control to Dynamically Induce Flow in Tetris.

Dynamic Difficulty Adjustment (DDA) is a set of techniques that aim to automatically adapt the difficulty of a video game based on the player’s performance. This paper presents a methodology for DDA using ideas from the theory of flow and case-based reasoning (CBR). In essence we are looking to generate game sessions with a similar difficulty evolution to previous game sessions that have produced flow in players with a similar skill level. We propose a CBR approach to dynamically assess the player’s skill level and adapt the difficulty of the game based on the relative complexity of the last game states. We develop a DDA system for Tetris using this methodology and show, in a experiment with 40 participants, that the DDA version has a measurable impact on the perceived flow using validated questionnaires.pre-print456 K

Machine Learning-Based Analysis in the Management of Iatrogenic Bile Duct Injury During Cholecystectomy: a Nationwide Multicenter Study

Background Iatrogenic bile duct injury (IBDI) is a challenging surgical complication. IBDI management can be guided by artificial intelligence models. Our study identified the factors associated with successful initial repair of IBDI and predicted the success of definitive repair based on patient risk levels. Methods This is a retrospective multi-institution cohort of patients with IBDI after cholecystectomy conducted between 1990 and 2020. We implemented a decision tree analysis to determine the factors that contribute to successful initial repair and developed a risk-scoring model based on the Comprehensive Complication Index. Results We analyzed 748 patients across 22 hospitals. Our decision tree model was 82.8% accurate in predicting the success of the initial repair. Non-type E (p < 0.01), treatment in specialized centers (p < 0.01), and surgical repair (p < 0.001) were associated with better prognosis. The risk-scoring model was 82.3% (79.0-85.3%, 95% confidence interval [CI]) and 71.7% (63.8-78.7%, 95% CI) accurate in predicting success in the development and validation cohorts, respectively. Surgical repair, successful initial repair, and repair between 2 and 6 weeks were associated with better outcomes. Discussion Machine learning algorithms for IBDI are a novel tool may help to improve the decision-making process and guide management of these patients

A horizon scan exercise for aquatic invasive alien species in Iberian inland waters

As the number of introduced species keeps increasing unabatedly, identifying and prioritising current and potential Invasive Alien Species (IAS) has become essential to manage them. Horizon Scanning (HS), defined as an exploration of potential threats, is considered a fundamental component of IAS management. By combining scientific knowledge on taxa with expert opinion, we identified the most relevant aquatic IAS in the Iberian Peninsula, i.e., those with the greatest geographic extent (or probability of introduction), severe ecological, economic and human health impacts, greatest difficulty and acceptability of management. We highlighted the 126 most relevant IAS already present in Iberian inland waters (i.e., Concern list) and 89 with a high probability of being introduced in the near future (i.e., Alert list), of which 24 and 10 IAS, respectively, were considered as a management priority after receiving the highest scores in the expert assessment (i.e., top-ranked IAS). In both lists, aquatic IAS belonging to the four thematic groups (plants, freshwater invertebrates, estuarine invertebrates, and vertebrates) were identified as having been introduced through various pathways from different regions of the world and classified according to their main functional feeding groups. Also, the latest update of the list of IAS of Union concern pursuant to Regulation (EU) No 1143/2014 includes only 12 top-ranked IAS identified for the Iberian Peninsula, while the national lists incorporate the vast majority of them. This fact underlines the great importance of taxa prioritisation exercises at biogeographical scales as a step prior to risk analyses and their inclusion in national lists. This HS provides a robust assessment and a cost-effective strategy for decision-makers and stakeholders to prioritise the use of limited resources for IAS prevention and management. Although applied at a transnational level in a European biodiversity hotspot, this approach is designed for potential application at any geographical or administrative scale, including the continental one

Constraints on the χ_(c1) versus χ_(c2) polarizations in proton-proton collisions at √s = 8 TeV

The polarizations of promptly produced χ_(c1) and χ_(c2) mesons are studied using data collected by the CMS experiment at the LHC, in proton-proton collisions at √s=8  TeV. The χ_c states are reconstructed via their radiative decays χ_c → J/ψγ, with the photons being measured through conversions to e⁺e⁻, which allows the two states to be well resolved. The polarizations are measured in the helicity frame, through the analysis of the χ_(c2) to χ_(c1) yield ratio as a function of the polar or azimuthal angle of the positive muon emitted in the J/ψ → μ⁺μ⁻ decay, in three bins of J/ψ transverse momentum. While no differences are seen between the two states in terms of azimuthal decay angle distributions, they are observed to have significantly different polar anisotropies. The measurement favors a scenario where at least one of the two states is strongly polarized along the helicity quantization axis, in agreement with nonrelativistic quantum chromodynamics predictions. This is the first measurement of significantly polarized quarkonia produced at high transverse momentum