Wait, But Why? Assessing Behavior Explanation Strategies for Real-Time Strategy Games

Work in AI-based explanation systems has uncovered an interesting contradiction: people prefer and learn best from 'why' explanations but expert esports commentators primarily answer 'what' questions when explaining complex behavior in real-time strategy games. Three possible explanations for this contradiction are: 1.) broadcast audiences are well-informed and do not need 'why' explanations; 2.) consuming 'why' explanations in real-time is too cognitively demanding for audiences; or 3.) producing live 'why' explanations is too difficult for commentators. We answer this open question by investigating the effects of explanation types and presentation modalities on audience recall and cognitive load in the context of an esports broadcast. We recruit 131 Dota 2 players and split them into three groups: the first group views a Dota 2 broadcast, the second group has the addition of an interactive map that provides 'what' explanations, and the final group receives the interactive map with detailed 'why' explanations. We find that participants who receive short interactive text prompts that provide 'what' explanations outperform the other two groups on a multiple-choice recall task. We also find that participants who receive detailed 'why' explanations submit the highest reports of cognitive load. Our evidence supports the conclusion that informed audiences benefit from explanations but do not have the cognitive resources to process 'why' answers in real-time. It also supports the conclusion that stacked explanation interventions across different modalities, like audio, interactivity, and text, can aid real-time comprehension when attention resources are limited. Together, our results indicate that interactive multimedia interfaces can be leveraged to quickly guide attention and provide low-cost explanations to improve intelligibility when time is too scarce for cognitively demanding 'why' explanations

WARDS : Modelling the Worth of Vision in MOBA’s

Multiplayer strategy games are examples of imperfect information games, where information about the game state can be retrieved through in-game mechanics. One such mechanic is vision. Within esports titles of this genre, such as League of Legends (LoL) and Dota 2, players often gather map information through the use of friendly units called wards. In LoL, one of the most popular esports title worldwide, warding has hitherto been evaluated only using a heuristic called vision score, provided by Riot, the game’s developer. In this paper, we examine the accuracy at LoL’s vision score at predicting the overall game-winner within the context supported by the game. We have ported LoL’s vision score to Dota 2, a similarly popular esports title, and compared its performance against a novel warding model. We have compared both models not only at predicting the overall winner, but also the current state of the game and their ability to predict and reflect short term game advantage and events. We found our model significantly outperformed LoL’s vision score. Additionally, we trained and evaluated a model for predicting the value of wards in real-time through the use of a Neural Network

DAX: Data-Driven Audience Experiences in Esports

Esports(competitivevideogames)havegrownintoaglobalphenomenon with over 450m viewers and a 1.5bn USD market. Esports broadcasts follow a similar structure to traditional sports. However, due to their virtual nature, a large and detailed amount data is available about in-game actions not currently accessible in traditional sport. This provides an opportunity to incorporate novel insights about complex aspects of gameplay into the audience experience – enabling more in-depth coverage for experienced viewers, and increased accessibility for newcomers. Previous research has only explored a limited range of ways data could be incorporated into esports viewing (e.g. data visualizations post-match) and only a few studies have investigated how the presentation of statistics impacts spectators’ experiences and viewing behaviors. We present Weavr, a companion app that allows audiences to consume datadriven insights during and around esports broadcasts. We report on deployments at two major tournaments, that provide ecologically valid findings about how the app’s features were experienced by audiences and their impact on viewing behavior. We discuss implications for the design of second-screen apps for live esports events, and for traditional sports as similar data becomes available for them via improved tracking technologies

Monomeric and dimeric oxidomolybdenum(V and VI) complexes, cytotoxicity, and DNA interaction studies: molybdenum assisted C═N bond cleavage of salophen ligands

Four novel dimeric bis-μ-imido bridged metal–metal bonded oxidomolybdenum(V) complexes [MoV2O2L′21–4] (1–4) (where L′1–4 are rearranged ligands formed in situ from H2L1–4) and a new mononuclear dioxidomolybdenum(VI) complex [MoVIO2L5] (5) synthesized from salen type N2O2 ligands are reported. This rare series of imido- bridged complexes (1–4) have been furnished from rearranged H3L′1–4 ligands, containing an aromatic diimine (o-phenylenediamine) “linker”, where Mo assisted hydrolysis followed by −C═N bond cleavage of one of the arms of the ligand H2L1–4 took place. A monomeric molybdenum(V) intermediate species [MoVO(HL′1–4)(OEt)] (Id1–4) was generated in situ. The concomitant deprotonation and dimerization of two molybdenum(V) intermediate species (Id1–4) ultimately resulted in the formation of a bis-μ-imido bridge between the two molybdenum centers of [MoV2O2L′21–4] (1–4). The mechanism of formation of 1–4 has been discussed, and one of the rare intermediate monomeric molybdenum(V) species Id4 has been isolated in the solid state and characterized. The monomeric dioxidomolybdenum(VI) complex [MoVIO2L5] (5) was prepared from the ligand H2L5 where the aromatic “linker” was replaced by an aliphatic diimine (1,2-diaminopropane). All the ligands and complexes have been characterized by elemental analysis, IR, UV–vis spectroscopy, NMR, ESI- MS, and cyclic voltammetry, and the structural features of 1, 2, 4, and 5 have been solved by X-ray crystallography. The DNA binding and cleavage activity of 1–5 have been explored. The complexes interact with CT-DNA by the groove binding mode, and the binding constants range between 103 and 104 M–1. Fairly good photoinduced cleavage of pUC19 supercoiled plasmid DNA was exhibited by all the complexes, with 4 showing the most promising photoinduced DNA cleavage activity of ∼93%. Moreover, in vitro cytotoxic activity of all the complexes was evaluated by MTT assay, which reveals that the complexes induce cell death in MCF-7 (human breast adenocarcinoma) and HCT-15 (colon cancer) cell lines

Production of He-4 and (4) in Pb-Pb collisions at root(NN)-N-S=2.76 TeV at the LHC

Results on the production of He-4 and (4) nuclei in Pb-Pb collisions at root(NN)-N-S = 2.76 TeV in the rapidity range vertical bar y vertical bar <1, using the ALICE detector, are presented in this paper. The rapidity densities corresponding to 0-10% central events are found to be dN/dy4(He) = (0.8 +/- 0.4 (stat) +/- 0.3 (syst)) x 10(-6) and dN/dy4 = (1.1 +/- 0.4 (stat) +/- 0.2 (syst)) x 10(-6), respectively. This is in agreement with the statistical thermal model expectation assuming the same chemical freeze-out temperature (T-chem = 156 MeV) as for light hadrons. The measured ratio of (4)/He-4 is 1.4 +/- 0.8 (stat) +/- 0.5 (syst). (C) 2018 Published by Elsevier B.V.Peer reviewe

Time to Die 2: Improved in-game death prediction in Dota 2

Competitive video game playing, an activity called esports, is increasingly popular to the point that there are now many professional competitions held for a variety of games. These competitions are broadcast in a professional manner similar to traditional sports broadcasting. Esports games are generally fast paced, and due to the virtual nature of these games, camera positioning can be limited. Therefore, knowing ahead of time where to position cameras, and what to focus a broadcast and associated commentary on, is a key challenge in esports reporting. This gives rise to moment-to-moment prediction within esports matches which can empower broadcasters to better observe and process esports matches. In this work we focus on this moment-to-moment prediction and in particular present techniques for predicting if a player will die within a set number of seconds for the esports title Dota 2. A player death is one of the most consequential events in Dota 2. We train our model on ‘telemetry’ data gathered directly from the game itself, and position this work as a novel extension of our previous work on the challenge. We use an enhanced dataset covering 9,822 Dota 2 matches. Since the publication of our previous work, new dataset parsing techniques developed by the WEAVR project enable the model to track more features, namely player status effects, and more importantly, to operate in real time. Additionally, we explore two new enhancements to the original model: one data-based extension and one architectural. Firstly we employ learnt embeddings for categorical features, e.g. which in game character a player has selected, and secondly we explicitly model the temporal element of our telemetry data using recurrent neural networks. We find that these extensions and additional features all aid the predictive power of the model achieving an F1 score of 0.54 compared to 0.17 for our previous model (on the new data). We improve this further by experimenting with the length of the time-series in the input data and find using 15 time steps further improves the F1 score to 0.62. This compares to F1 of 0.1 for a standard RNN on the same task. Additionally a deeper analysis of the Time to Die model is carried out to assess its suitability as a broadcast aid

Multiplicity dependence of light (anti-)nuclei production in p–Pb collisions at sNN=5.02 TeV

The measurement of the deuteron and anti-deuteron production in the rapidity range −1 < y < 0 as a function of transverse momentum and event multiplicity in p–Pb collisions at √sNN = 5.02 TeV is presented. (Anti-)deuterons are identified via their specific energy loss dE/dx and via their time-of- flight. Their production in p–Pb collisions is compared to pp and Pb–Pb collisions and is discussed within the context of thermal and coalescence models. The ratio of integrated yields of deuterons to protons (d/p) shows a significant increase as a function of the charged-particle multiplicity of the event starting from values similar to those observed in pp collisions at low multiplicities and approaching those observed in Pb–Pb collisions at high multiplicities. The mean transverse particle momenta are extracted from the deuteron spectra and the values are similar to those obtained for p and particles. Thus, deuteron spectra do not follow mass ordering. This behaviour is in contrast to the trend observed for non-composite particles in p–Pb collisions. In addition, the production of the rare 3He and 3He nuclei has been studied. The spectrum corresponding to all non-single diffractive p-Pb collisions is obtained in the rapidity window −1 < y < 0 and the pT-integrated yield dN/dy is extracted. It is found that the yields of protons, deuterons, and 3He, normalised by the spin degeneracy factor, follow an exponential decrease with mass number

HΛ3 and H‾Λ‾3 lifetime measurement in Pb–Pb collisions at √sNN=5.02 TeV via two-body decay

An improved value for the lifetime of the (anti-)hypertriton has been obtained using the data sample of Pb–Pb collisions at √sNN = 5.02 TeV collected by the ALICE experiment at the LHC. The (anti-)hypertriton has been reconstructed via its charged two-body mesonic decay channel and the lifetime has been determined from an exponential fit to the dN/d(ct) spectrum. The measured value, τ = 242+34 −38 (stat.) ± 17 (syst.) ps, is compatible with representative theoretical predictions, thus contributing to the solution of the longstanding hypertriton lifetime puzzle