Learning behaviour-performance maps with meta-evolution

International audienceThe MAP-Elites quality-diversity algorithm has been successful in robotics because it can create a behaviorally diverse set of solutions that later can be used for adaptation, for instance to unanticipated damages. In MAP-Elites, the choice of the behaviour space is essential for adaptation, the recovery of performance in unseen environments , since it defines the diversity of the solutions. Current practice is to hand-code a set of behavioural features, however, given the large space of possible behaviour-performance maps, the designer does not know a priori which behavioural features maximise a map's adaptation potential. We introduce a new meta-evolution algorithm that discovers those behavioural features that maximise future adaptations. The proposed method applies Covari-ance Matrix Adaptation Evolution Strategy to evolve a population of behaviour-performance maps to maximise a meta-fitness function that rewards adaptation. The method stores solutions found by MAP-Elites in a database which allows to rapidly construct new behaviour-performance maps on-the-fly. To evaluate this system , we study the gait of the RHex robot as it adapts to a range of damages sustained on its legs. When compared to MAP-Elites with user-defined behaviour spaces, we demonstrate that the meta-evolution system learns high-performing gaits with or without damages injected to the robot

Learning behaviour-performance maps with meta-evolution

International audienceThe MAP-Elites quality-diversity algorithm has been successful in robotics because it can create a behaviorally diverse set of solutions that later can be used for adaptation, for instance to unanticipated damages. In MAP-Elites, the choice of the behaviour space is essential for adaptation, the recovery of performance in unseen environments , since it defines the diversity of the solutions. Current practice is to hand-code a set of behavioural features, however, given the large space of possible behaviour-performance maps, the designer does not know a priori which behavioural features maximise a map's adaptation potential. We introduce a new meta-evolution algorithm that discovers those behavioural features that maximise future adaptations. The proposed method applies Covari-ance Matrix Adaptation Evolution Strategy to evolve a population of behaviour-performance maps to maximise a meta-fitness function that rewards adaptation. The method stores solutions found by MAP-Elites in a database which allows to rapidly construct new behaviour-performance maps on-the-fly. To evaluate this system , we study the gait of the RHex robot as it adapts to a range of damages sustained on its legs. When compared to MAP-Elites with user-defined behaviour spaces, we demonstrate that the meta-evolution system learns high-performing gaits with or without damages injected to the robot

Mapping the emotional journey of teaching

This paper will explore the use of Novakian concept mapping as a means of visualising and tracing the range of emotions inherent within any teaching experience. It will focus in particular on its use within higher education, where the presence of emotion has traditionally been disregarded or seemingly suppressed. The example of undergraduate teaching of the law degree will be used as an area where the role of emotion is particularly under-theorised. This paper will assess the effectiveness of concept mapping as a tool to enable academics to explicitly acknowledge, and reflect upon, the existence of emotion, both in terms of their individual teaching experiences, their collective teaching journey through a course or qualification and their students’ learning journey. It will also consider how use of this technique at a collective level could identify areas of pedagogic frailty, which may arise due to the misinterpreting, mishandling or suppression of emotion. The various opportunities and challenges arising from this application of concept mapping techniques will be discussed, drawing on a small, empirical pilot study, and leading to the conclusion that it has a useful and significant role to play within an emerging field of enquiry

TRIZ: an alternate way to solve problem for student

Inventive problem solving is an alternative way of solving problem for student. 60 years ago, in 1956, Altshuller published a new, constructive and methodical approach ideas on problem solving to offer to the world. Even though TRIZ originally meant for fields of industry domains, it has solved problems in other field using some common principles. Thus, this methodology has spread to over 35 countries across the world. It is now being taught in several universities and it has been applied by a number of global organizations who have found it particularly useful to solve their problem. In 2017, KSSM curriculum has been revised to cope up this 21st century demand. This paper is a potentially useful for TRIZ beginner, as an alternative to solve problem comparable to common problem solving method. This paper also discussed the limitation of other common problem method which leads the advantages of using TRIZ