This article explores the opinion dynamics of a double coalition opinion against a third opinion under majority rule updates on odd fixed size connected groups. For this purpose, coalition benefit criteria and three opinion formation models which extend the 2-state majority rule model on lattices are introduced. The proposed models focus on the coalition profit of its constituent coalition opinions and cover the possible final scenarios from coalition alliance perspective: either minor opinion or major opinion is favored, or dynamics do not favor to any coalition opinion. Opinion exchanges take place on a torus embedded lattice network of a 3-state system having in consideration tie configurations and two rules to break them: either by random choice or leaving ties unaltered. Models were analyzed in the statistical mechanics spirit through Monte Carlo simulations without node replacement. Estimations for coalition benefits, the growth of coalition ties, and consensus probabilities are reported. The loss of coalition strengths due to coalition ties and its indecision is indicated. In particular, the logistic decay of consensus probability is due to the logistic adaptive growth of coalition ties. Scaling behaviors for consensus time and coalition ties in terms of network size are suggested. The results of numerical simulations are discussed in the context of social influence and social dynamics.

Barrett is to be applauded for attempting to build a case for motivated activity as “reasons that act as causes.” Had he stuck to the plan he set out at the beginning of his essay, and assiduously worked on clarifying the very difficult issues involved in this extremely ambitious venture, he would have had an excellent foundation for further development. Unfortunately, he fell into some conceptual and methodological traps that have claimed many. This commentary aims to suggest some ways forward.

John Stewart commits himself to the defence of a demanding version of enaction. Among its many original features, John’s version of enaction includes a questionable form of anti-representationalism, and leaves room for the Varelian idea that intentionality is a biological property. This stance anticipates contemporary endorsements in 4E cognition of intentionality as a non-representational and non-contentful property. Once it is deprived of its representational tinsels, intentionality appears to us again as a property of object-directedness. Nevertheless, is the autopoietic model of intentionality as object-directedness coherent and convincing? And do we need intentionality when we describe the meaningful relations between organisms and their environments? The article seeks to answer to these questions.

Which kind of complex behavior may arise from self-organizing principles? We investigate this question for the case of snake-like robots composed of passively coupled segments, with every segment containing two wheels actuated separately by a single neuron. The robot is self-organized both on the level of the individual wheels and with respect to inter-wheel coordination, which arises exclusively from the mechanical coupling of the individual wheels and segments. For the individual wheel, the generating principle proposed results in locomotive states that correspond to self-organized limit cycles of the sensorimotor loop. Our robot interacts with the environment by monitoring the state of its actuators, that is, via propriosensation. External sensors are absent. In a structured environment the robot shows complex emergent behavior that includes pushing movable blocks around, reversing direction when hitting a wall, and turning when climbing a slope. On flat grounds the robot wiggles in a snake-like manner, when moving at higher velocities. We also investigate the emergence of motor primitives, namely, the route to locomotion, which is characterized by a series of local and global bifurcations in terms of dynamical system theory.

The extension of the organism into the environment, suggested by some interpretations of autopoiesis, can seem disconcerting. Yet we argue that Villalobos and Razeto-Barry’s attempt to reinscribe the organism inside the physical body, via the criterion of autopoietically produced material coherence, cannot account for the dynamical and highly changeable nature of life. A successful science of life must do justice not only to the life forms that enjoy clear boundaries but also to the squishy, the strange and the technologically modified.

In this opinion, I examine the benefits of the enactivist approach in the study of perception and hallucination, which have traditionally been studied in the context of analytic philosophy. I consider some arguments that rescue the role of objects and the body in the perceptual experience, which allow certain clarities regarding the case of hallucination.

We review issues stemming from current models regarding the drivers of cultural complexity and cultural evolution. We disagree with the implication of the treadmill model, based on dual-inheritance theory, that population size is the driver of cultural complexity. The treadmill model reduces the evolution of artifact complexity, measured by the number of parts, to the statistical fact that individuals with high skills are more likely to be found in a larger population than in a smaller population. However, for the treadmill model to operate as claimed, implausibly high skill levels must be assumed. Contrary to the treadmill model, the risk hypothesis for the complexity of artifacts relates the number of parts to increased functional efficiency of implements. Empirically, all data on hunter-gatherer artifact complexity support the risk hypothesis and reject the treadmill model. Still, there are conditions under which increased technological complexity relates to increased population size, but the dependency does not occur in the manner expressed in the treadmill model. Instead, it relates to population size when the support system for the technology requires a large population size. If anything, anthropology and ecology suggest that cultural complexity generates high population density rather than the other way around.

In this research, real robots are used to model the emergence and development of a symbolic communication system in a group of social agents. It is shown that as the predefined symbols of the language are transmitted among the robots, variations due to embodiment arise in the learned symbols. As a result of these variations, transmissibility of the randomly generated symbols of the language decreases. We introduce multiple cycles of iterated imitation during which the symbols of the language evolve and adapt to the uncertainties in robots’ sensors and actuators. Specific structures emerge in the learned symbols and transmissibility of the language increases. This process is proposed as a model that explains how structural syntax emerges and evolves in a symbolic communication system.

The idea of reusing or transferring information from previously learned tasks (source tasks) for the learning of new tasks (target tasks) has the potential to significantly improve the sample efficiency of a reinforcement learning agent. In this work, we describe a novel approach for reusing previously acquired knowledge by using it to guide the exploration of an agent while it learns new tasks. In order to do so, we employ a variant of the growing self-organizing map algorithm, which is trained using a measure of similarity that is defined directly in the space of the vectorized representations of the value functions. In addition to enabling transfer across tasks, the resulting map is simultaneously used to enable the efficient storage of previously acquired task knowledge in an adaptive and scalable manner. We empirically validate our approach in a simulated navigation environment and also demonstrate its utility through simple experiments using a mobile micro-robotics platform. In addition, we demonstrate the scalability of this approach and analytically examine its relation to the proposed network growth mechanism. Furthermore, we briefly discuss some of the possible improvements and extensions to this approach, as well as its relevance to real-world scenarios in the context of continual learning.

Realizing adaptability to environmental changes requires the robot’s body to change according to the environment. From this perspective, several studies have considered variable compliant actuation to change the physical characteristics of the robot as it interacts with the environment. Robots with this ability show a variety of efficient stable motions during contact with the environment. However, having locally variable compliant parts with independent dynamics under the same body may be insufficient for achieving adaptability in diverse environments. To extend the idea of variable compliant actuation, from being used locally to the level of the entire body’s dynamics, in this study, we use an actuator network system (ANS). As an approximation of bipedal robots, we developed an eight-legged rimless wheel robot with an ANS that allows energy transfer among limbs through passive interactions among mutually interconnected air cylinders mounted on the robot’s legs. We experimentally demonstrated that by using the ANS to realize energy transfer among limbs, the robot performance improved.