15,471 research outputs found
Controlled Information Fusion with Risk-Averse CVaR Social Sensors
Consider a multi-agent network comprised of risk averse social sensors and a
controller that jointly seek to estimate an unknown state of nature, given
noisy measurements. The network of social sensors perform Bayesian social
learning - each sensor fuses the information revealed by previous social
sensors along with its private valuation using Bayes' rule - to optimize a
local cost function. The controller sequentially modifies the cost function of
the sensors by discriminatory pricing (control inputs) to realize long term
global objectives. We formulate the stochastic control problem faced by the
controller as a Partially Observed Markov Decision Process (POMDP) and derive
structural results for the optimal control policy as a function of the
risk-aversion factor in the Conditional Value-at-Risk (CVaR) cost function of
the sensors. We show that the optimal price sequence when the sensors are risk-
averse is a super-martingale; i.e, it decreases on average over time.Comment: IEEE CDC 201
Affective games:a multimodal classification system
Affective gaming is a relatively new field of research that exploits human emotions to influence gameplay for an enhanced player experience. Changes in player’s psychology reflect on their behaviour and physiology, hence recognition of such variation is a core element in affective games. Complementary sources of affect offer more reliable recognition, especially in contexts where one modality is partial or unavailable. As a multimodal recognition system, affect-aware games are subject to the practical difficulties met by traditional trained classifiers. In addition, inherited game-related challenges in terms of data collection and performance arise while attempting to sustain an acceptable level of immersion. Most existing scenarios employ sensors that offer limited freedom of movement resulting in less realistic experiences. Recent advances now offer technology that allows players to communicate more freely and naturally with the game, and furthermore, control it without the use of input devices. However, the affective game industry is still in its infancy and definitely needs to catch up with the current life-like level of adaptation provided by graphics and animation
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
- …