The development of a rich multimedia training environment for crisis management: using emotional affect to enhance learning

PANDORA is an EU FP7-funded project developing a novel training and learning environment for Gold Commanders, individuals who carry executive responsibility for the services and facilities identified as strategically critical e.g. Police, Fire, in crisis management strategic planning situations. A key part of the work for this project is considering the emotional and behavioural state of the trainees, and the creation of more realistic, and thereby stressful, representations of multimedia information to impact on the decision-making of those trainees. Existing training models are predominantly paper-based, table-top exercises, which require an exercise of imagination on the part of the trainees to consider not only the various aspects of a crisis situation but also the impacts of interventions, and remediating actions in the event of the failure of an intervention. However, existing computing models and tools are focused on supporting tactical and operational activities in crisis management, not strategic. Therefore, the PANDORA system will provide a rich multimedia information environment, to provide trainees with the detailed information they require to develop strategic plans to deal with a crisis scenario, and will then provide information on the impacts of the implementation of those plans and provide the opportunity for the trainees to revise and remediate those plans. Since this activity is invariably multi-agency, the training environment must support group-based strategic planning activities and trainees will occupy specific roles within the crisis scenario. The system will also provide a range of non-playing characters (NPC) representing domain experts, high-level controllers (e.g. politicians, ministers), low-level controllers (tactical and operational commanders), and missing trainee roles, to ensure a fully populated scenario can be realised in each instantiation. Within the environment, the emotional and behavioural state of the trainees will be monitored, and interventions, in the form of environmental information controls and mechanisms impacting on the stress levels and decisionmaking capabilities of the trainees, will be used to personalise the training environment. This approach enables a richer and more realistic representation of the crisis scenario to be enacted, leading to better strategic plans and providing trainees with structured feedback on their performance under stress

Central Asia Forecasting 2021: Results from an Expert Survey

The 'Central Asia Forecasting' study, jointly implemented by the Friedrich Ebert Foundation (FES), the OSCE Academy in Bishkek, and the SPCE Hub, aims to help strengthen EU-Central Asia relations. The study results are intended to stimulate the debate on the region, foster understanding of the common challenges and opportunities, and encourage data-driven policymaking. It is a pilot project that will be followed by an annual or biennial study to analyse regional trends over time. The audience that we aim to address with this report comprises the broader public in Europe and Central Asia, civil society representatives, regional experts, researchers and especially EU foreign-policy makers. For this study, a human-judgement forecasting method was employed in the form of an opinion survey among experts and the informed public on developments in the region in the next three years. In total, 144 respondents took our 20-minute survey. About half of the respondents are Central Asian citizens and half are from outside the region. The majority are affiliated with academic institutions and think tanks. This report launch will present the analysis of the survey responses regarding domestic politics and regional affairs, global challenges affecting the region, and EU-Central Asian relations

Novelty Search in Competitive Coevolution

One of the main motivations for the use of competitive coevolution systems is their ability to capitalise on arms races between competing species to evolve increasingly sophisticated solutions. Such arms races can, however, be hard to sustain, and it has been shown that the competing species often converge prematurely to certain classes of behaviours. In this paper, we investigate if and how novelty search, an evolutionary technique driven by behavioural novelty, can overcome convergence in coevolution. We propose three methods for applying novelty search to coevolutionary systems with two species: (i) score both populations according to behavioural novelty; (ii) score one population according to novelty, and the other according to fitness; and (iii) score both populations with a combination of novelty and fitness. We evaluate the methods in a predator-prey pursuit task. Our results show that novelty-based approaches can evolve a significantly more diverse set of solutions, when compared to traditional fitness-based coevolution.Comment: To appear in 13th International Conference on Parallel Problem Solving from Nature (PPSN 2014

A game-based corpus for analysing the interplay between game context and player experience

Recognizing players’ affective state while playing video games has been the focus of many recent research studies. In this paper we describe the process that has been followed to build a corpus based on game events and recorded video sessions from human players while playing Super Mario Bros. We present different types of information that have been extracted from game context, player preferences and perception of the game, as well as user features, automatically extracted from video recordings. We run a number of initial experiments to analyse players’ behavior while playing video games as a case study of the possible use of the corpus.peer-reviewe

Design of a fuzzy affective agent based on typicality degrees of physiological signals

Conference paper presented at International Conference on Information Processing and Management in July 2014Physiology-based emotionally intelligent paradigms provide an opportunity to enhance human computer interactions by continuously evoking and adapting to the user experiences in real-time. However , there are unresolved questions on how to model real- time emotionally intelligent applications through mapping of physiological patterns to users ' affective states. In ·this study, we consider an approach for design of fuzzy affective agent based on the concept of typicality. We propose the use of typicality degrees of physiological patterns to construct the fuzzy rules representing the continuous transitions of user 's affective states. The approach was tested· on experimental data in which physiological measures were recorded on players involved in an action game to characterize various gaming experiences . We show that , in addition to exploitation of the results to characterize users ' affective states through .typicality degrees, this approach is a systematic way to automatically define fuzzy rules from experimental data for an affective agent to be used in real -time continuous assessment of user's affective states.Physiology-based emotionally intelligent paradigms provide an opportunity to enhance human computer interactions by continuously evoking and adapting to the user experiences in real-time. However , there are unresolved questions on how to model real- time emotionally intelligent applications through mapping of physiological patterns to users ' affective states. In ·this study, we consider an approach for design of fuzzy affective agent based on the concept of typicality. We propose the use of typicality degrees of physiological patterns to construct the fuzzy rules representing the continuous transitions of user 's affective states. The approach was tested· on experimental data in which physiological measures were recorded on players involved in an action game to characterize various gaming experiences . We show that , in addition to exploitation of the results to characterize users ' affective states through .typicality degrees, this approach is a systematic way to automatically define fuzzy rules from experimental data for an affective agent to be used in real -time continuous assessment of user's affective states

Affective Man-Machine Interface: Unveiling human emotions through biosignals

As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals

Tune in to your emotions: a robust personalized affective music player

The emotional power of music is exploited in a personalized affective music player (AMP) that selects music for mood enhancement. A biosignal approach is used to measure listeners’ personal emotional reactions to their own music as input for affective user models. Regression and kernel density estimation are applied to model the physiological changes the music elicits. Using these models, personalized music selections based on an affective goal state can be made. The AMP was validated in real-world trials over the course of several weeks. Results show that our models can cope with noisy situations and handle large inter-individual differences in the music domain. The AMP augments music listening where its techniques enable automated affect guidance. Our approach provides valuable insights for affective computing and user modeling, for which the AMP is a suitable carrier application