Modelling and analyzing adaptive self-assembling strategies with Maude

Building adaptive systems with predictable emergent behavior is a challenging task and it is becoming a critical need. The research community has accepted the challenge by introducing approaches of various nature: from software architectures, to programming paradigms, to analysis techniques. We recently proposed a conceptual framework for adaptation centered around the role of control data. In this paper we show that it can be naturally realized in a reflective logical language like Maude by using the Reflective Russian Dolls model. Moreover, we exploit this model to specify, validate and analyse a prominent example of adaptive system: robot swarms equipped with self-assembly strategies. The analysis exploits the statistical model checker PVeStA

Modelling and analyzing adaptive self-assembling strategies with Maude

Building adaptive systems with predictable emergent behavior is a challenging task and it is becoming a critical need. The research community has accepted the challenge by introducing approaches of various nature: from software architectures, to programming paradigms, to analysis techniques. We recently proposed a conceptual framework for adaptation centered around the role of control data. In this paper we show that it can be naturally realized in a reflective logical language like Maude by using the Reflective Russian Dolls model. Moreover, we exploit this model to specify, validate and analyse a prominent example of adaptive system: robot swarms equipped with self-assembly strategies. The analysis exploits the statistical model checker PVeStA

Coupling angle variability in healthy and patellofemoral pain runners

Background Patellofemoral pain is hypothesized to result in less joint coordination variability. The ability to relate coordination variability to patellofemoral pain pathology could have many clinical uses; however, evidence to support its clinical application is lacking. The aim was to determine if vector coding's coupling angle variability, as a measure of joint coordination variability, was less for runners with patellofemoral pain than healthy controls as is commonly postulated. Methods Nineteen female recreational runners with patellofemoral pain and eleven healthy controls performed a treadmill acclimation protocol then ran at a self-selected pace for 15 min. 3-D kinematics, force plate kinetics, knee pain and rating of perceived exertion were recorded each minute. Data were selected for the: pain group at the highest pain reached (pain � 3/10) in a non-exerted state (exertion < 14/20), and; non-exerted healthy group from the eleventh minute. Coupling angle variability was calculated over several portions of the stride for six knee-ankle combinations during five non-consecutive strides. Findings 46 of 48 coupling angle variability measures were greater for the pain group, with 7 significantly greater (P <.05). Interpretation These findings oppose the theory that less coupling angle variability is indicative of a pathological coordinate state during running. Greater coupling angle variability may be characteristic of patellofemoral pain in female treadmill running when a larger threshold of pain is reached than previously observed. A predictable and directional response of coupling angle variability measures in relation to knee pathology is not yet clear and requires further investigation prior to considerations for clinical utility. © 2013 Elsevier Ltd

Physical and neural entrainment to rhythm: human sensorimotor coordination across tasks and effector systems.

The human sensorimotor system can be readily entrained to environmental rhythms, through multiple sensory modalities. In this review, we provide an overview of theories of timekeeping that make this neuroentrainment possible. First, we present recent evidence that contests the assumptions made in classic timekeeper models. The role of state estimation, sensory feedback and movement parameters on the organization of sensorimotor timing are discussed in the context of recent experiments that examined simultaneous timing and force control. This discussion is extended to the study of coordinated multi-effector movements and how they may be entrained

Modelling and analyzing adaptive self-assembling strategies with Maude

Building adaptive systems with predictable emergent behavior is a challenging task and it is becoming a critical need. The research community has accepted the challenge by introducing approaches of various nature: from software architectures, to programming paradigms, to analysis techniques. We recently proposed a conceptual framework for adaptation centered around the role of control data. In this paper we show that it can be naturally realized in a reflective logical language like Maude by using the Reflective Russian Dolls model. Moreover, we exploit this model to specify and analyse a prominent example of adaptive system: robot swarms equipped with obstacle-avoidance self-assembly strategies. The analysis exploits the statistical model checker PVesta