A Situation-Aware Fear Learning (SAFEL) Model for Robots

This work proposes a novel Situation-Aware FEar Learning (SAFEL) model for robots. SAFEL combines concepts of situation-aware expert systems with well-known neuroscientific findings on the brain fear-learning mechanism to allow companion robots to predict undesirable or threatening situations based on past experiences. One of the main objectives is to allow robots to learn complex temporal patterns of sensed environmental stimuli and create a representation of these patterns. This memory can be later associated with a negative or positive “emotion”, analogous to fear and confidence. Experiments with a real robot demonstrated SAFEL’s success in generating contextual fear conditioning behaviour with predictive capabilities based on situational information

Nurses' problem detection of infection risk: The effects of risk factors, expertise, and time pressure

Problem detection is a critical component in nursing, such that superior detection could lead to quicker intervention, even if the nature of the problem is not yet clear. A critical problem intensive care nurses typically engage in is detecting the threat of an impending hospital-acquired infection. The purpose of this study was to investigate the effects of the presence of risk factors, expertise, and time pressure on problem detection. The results suggested that time pressure seemed to have a detrimental effect on problem detection, and nurses benefitted from the presence of more risk factors. When not under time pressure, nurses were more sensitive in their problem detection judgments, and only needed one risk factor to trigger problem detection. Experienced nurses were more sensitive to the type of infection at detection, and were more likely to identify the problem correctly after information had been accumulated. These results suggest that problem detection was differentially affected by risk factors based on the presence or absence of time pressure. In addition, experienced nurses took a different approach to problem detection when compared to novices. Finally, problem detection and problem identification can in some situations occur simultaneously, but are distinct processes.M.S

Ghost-in-the-Machine reveals human social signals for human-robot interaction

© 2015 Loth, Jettka, Giuliani and de Ruiter. We used a new method called "Ghost-in-the-Machine" (GiM) to investigate social interactions with a robotic bartender taking orders for drinks and serving them. Using the GiM paradigm allowed us to identify how human participants recognize the intentions of customers on the basis of the output of the robotic recognizers. Specifically, we measured which recognizer modalities (e.g., speech, the distance to the bar) were relevant at different stages of the interaction. This provided insights into human social behavior necessary for the development of socially competent robots. When initiating the drink-order interaction, the most important recognizers were those based on computer vision. When drink orders were being placed, however, the most important information source was the speech recognition. Interestingly, the participants used only a subset of the available information, focussing only on a few relevant recognizers while ignoring others. This reduced the risk of acting on erroneous sensor data and enabled them to complete service interactions more swiftly than a robot using all available sensor data. We also investigated socially appropriate response strategies. In their responses, the participants preferred to use the same modality as the customer's requests, e.g., they tended to respond verbally to verbal requests. Also, they added redundancy to their responses, for instance by using echo questions. We argue that incorporating the social strategies discovered with the GiM paradigm in multimodal grammars of human-robot interactions improves the robustness and the ease-of-use of these interactions, and therefore provides a smoother user experience

Automatic methods for long-term tracking and the detection and decoding of communication dances in honeybees

The honeybee waggle dance communication system is an intriguing example of abstract animal communication and has been investigated thoroughly throughout the last seven decades. Typically, observables such as waggle durations or body angles are extracted manually either directly from the observation hive or from video recordings to quantify properties of the dance and related behaviors. In recent years, biology has profited from automation, improving measurement precision, removing human bias, and accelerating data collection. We have developed technologies to track all individuals of a honeybee colony and to detect and decode communication dances automatically. In strong contrast to conventional approaches that focus on a small subset of the hive life, whether this regards time, space, or animal identity, our more inclusive system will help the understanding of the dance comprehensively in its spatial, temporal, and social context. In this contribution, we present full specifications of the recording setup and the software for automatic recognition of individually tagged bees and the decoding of dances. We discuss potential research directions that may benefit from the proposed automation. Lastly, to exemplify the power of the methodology, we show experimental data and respective analyses from a continuous, experimental recording of 9 weeks duration