Trading off impact and mutation of knowledge by cooperatively learning robots

We present a socially inspired approach that allows agents in Multi-Agent Systems to speed up their own learning process through communication. Thereby, they are able to trade off impact of knowledge by mutation dependent on the recent performance of the interacting agents. This is inspired by social interaction of humans, where the opinions of experts have greater impact on the overall opinion and are incorporated more exactly than those of newbies. The approach is successfully evaluated in a simulation in which mobile robots have to accomplish a task while taking care of timely recharging their resources1st IFIP International Conference on Biologically Inspired Cooperative Computing - Robotics and Sensor NetworksRed de Universidades con Carreras en Informática (RedUNCI

Trading off impact and mutation of knowledge by cooperatively learning robots

We present a socially inspired approach that allows agents in Multi-Agent Systems to speed up their own learning process through communication. Thereby, they are able to trade off impact of knowledge by mutation dependent on the recent performance of the interacting agents. This is inspired by social interaction of humans, where the opinions of experts have greater impact on the overall opinion and are incorporated more exactly than those of newbies. The approach is successfully evaluated in a simulation in which mobile robots have to accomplish a task while taking care of timely recharging their resources1st IFIP International Conference on Biologically Inspired Cooperative Computing - Robotics and Sensor NetworksRed de Universidades con Carreras en Informática (RedUNCI

Prosody based emotion recognition for MEXI

Abstract — This paper describes the emotion recognition from natural speech as realized for the robot head MEXI. We use a fuzzy logic approach for analysis of prosody in natural speech. Since MEXI often communicates with well known persons but also with unknown humans, for instance at exhibitions, we realized a speaker dependent mode as well as a speaker independent mode in our prosody based emotion recognition. A key point of our approach is that it automatically selects the most significant features from a set of twenty analyzed features based on a training database of speech samples. This is important according to our results, since the set of significant features differs considerably between the distinguished emotions. With our approach we reach average recognition rates of 84 % in speaker dependent mode and 60 % in speaker independent mode. Index Terms — Emotion recognition, prosody, fuzzy rules, robot hea