Computer-aided practice can help improve personnel training for demanding scenarios in terms of time and quality. In this paper, we concentrate on asymmetrical conflicts, such as a unit that deals with hostile crowds robbing a store, with the aim of preventing further criminal activity and at the same time minimizing physical and emotional damage. We propose a surrogate-agent modeling approach based on execution of the following loop: (i) observe a human (the unit leader) playing a set of scenarios in a simulated environment and induce strategic patterns of human play; (ii) use patterns to construct a surrogate agent (digital clone); (iii) test the surrogate under all possible circumstances through data farming; and (iv) evaluate the performance and highlight deficiencies in the agent's responses, thereby enabling human improvements in new attempts. Experiments on two domains indicate that the proposed approach could significantly improve the training procedure and help trainees to properly perceive the cognitive properties of the crowds.

Monitoring human energy expenditure is important in many health and sport applications, since the energy expenditure directly reflects the level of physical activity. The actual energy expenditure is unpractical to measure; hence, the field aims at estimating it by measuring the physical activity with accelerometers and other sensors. Current advanced estimators use a context-dependent approach in which a different regression model is invoked for different activities of the user. In this paper, we go a step further and use multiple contexts corresponding to multiple sensors, resulting in an ensemble of models for energy expenditure estimation. This provides a multi-view perspective, which leads to a better estimation of the energy. The proposed method was experimentally evaluated on a comprehensive set of activities where it outperformed the current state-of-the-art.