Our MetaChem framework supports the definition and combination of artificial chemistries. Here we describe an implementation of MetaChem in an object oriented language. We briefly define MetaChem, and provide an example in the form of a toy AChem: StringCatChem. We present the class hierarchy used to define MetaChem such that the implementation can run directly from a graph description of some AChem. This matches the description given by the formal framework definition. We also describe some generic functions of MetaChem that have been implemented and used in StringCatChem. This implementation is available on GitHub.
Life and cognition are inherently circular dynamical processes, and people have difficulty understanding circular causation. We give case studies illustrating some resulting confusions, and propose that the problems may lie in failing to properly distinguish between similar concepts used in different levels of description of a system, typically local and global levels.
Natural chemistry deals with non-deterministic processes, and this is reflected in some artificial chemistries. We can tune these artificial systems by manipulating the functions that define their probabilistic processes. In this work we consider different probabilistic functions for particle linking, applied to our Jordan Algebra Artificial Chemistry. We use five base functions and their variations to investigate the possible behaviours of the system, and try to connect those behaviours to different traits of the functions. We find that, while some correlations can be seen, there are unexpected behaviours that we cannot account for in our current analysis. While we can set and manipulate the probabilities in our system, it is still complex and still displays emergent behaviour that we can not fully control.
A popular hypothesis suggests that the nervous system of different organisms, from neural tissue to whole brains, may operate at or near a critical point. During the last decade, maximum entropy techniques have allowed to go beyond merely finding statistical signatures of criticality, to models directly inferred from data recorded in neural cultures, providing stronger evidence of criticality in neural activity. Nevertheless, these modeling techniques are restricted to neural cultures and have not been extended to neural tissue in living organisms. In this paper, we extend this line of research by analyzing signatures of criticality in a pairwise maximum entropy model inferred from neural recordings of C. elegans during freely-moving locomotion. From the analysis of the inferred models we find some signatures of criticality, as a divergence of the heat capacity of the system. Other indicators, such as Zipf’s distributions, were not found. However, inspecting a similar analysis based in a 2D lattice Ising ...
In this paper I present a general modelling framework for coupled fluid dynamics and chemistry problems, and apply it to the simulation of a series of complex, homeostatic reaction diffusion systems. The model can incorporate any number of chemical species and reactions. Those chemical species diffuse, react and are advected by fluid flows. I illustrate some characteristic results from the modelling of the Gray Scott reaction diffusion system with thermally resolved reactions. Extending my previous work on ecological dynamics of nonliving structures, I demonstrate that thermal homeostasis of reaction diffusion spots can occur in systems without the use of the porous wall boundary condition that has traditionally been used for the Gray Scott system. I present an initial analysis of the parameter space of this system as well as detailing the mechanism behind the thermal homeostasis.
One of the challenges of researching spiking neural networks (SNN) is translation from temporal spiking behavior to classic controller output. While many encoding schemes exist to facilitate this translation, there are few benchmarks for neural networks that inherently utilize a temporal controller. In this work, we consider the common reinforcement problem of animat locomotion in an environment suited for evaluating SNNs. Using this problem, we explore novel methods of reward distribution as they impacts learning. Hebbian learning, in the form of spike time dependent plasticity (STDP), is modulated by a dopamine signal and affected by reward-induced neural activity. Different reward strategies are parameterized and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is used to find the best strategies for fixed animat morphologies. The contribution of this work is two-fold: to cast the problem of animat locomotion in a form directly applicable to simple temporal controllers, and to demonstrate novel methods for reward modulated Hebbian learning.
We use an agent-based social simulation to study the effects of role reassignment on resource management in a human organization. As a model organization we use the 446 Force Support Squadron of the United States Air Force. We simulate different role reassignment policies where members of the organization can be reassigned at their request, at the request of the organization, or as a combination of both of these policies. We also consider at what point in an individual's career is the best time to change roles. We conclude that a compromise between organizational needs and individual needs results in higher productivity. In addition we conclude that mid-career reassignment is better for the organization than early or late career reassignment.
Building robots, even for performing simple tasks, requires the designer to assess performance using various parameters. However, sometimes the best solution is not the one that performs best on average. Hence, other ways of evaluating performance are necessary. We ran a broad parameter sweep for an agent-based simulation of a robotic area coverage task with very simple agent controllers in four different task environments. Analysis of the results emphasizes the importance of considering the entire distribution across randomized starting conditions, and not just the mean overall performance, when assessing the effectiveness of parameter settings. Our findings indicate the potential for robotic system designers to constrain or specify the qualities of system performance distributions.
One of the main goals of robot designers or computer scientists working on artificial intelligence is to give the illusion of life in their creations. For this Graal quest, our first thought would be to look around, study and copy the existing living beings in nature like humans or animals. This has been applied for centuries to solve technical issues like bird wings for planes or four leg walks for robots. But neither biomimicry nor the search for a perfect and accurate replication of reality lead to the illusion of life. Our belief is the following: it seems necessary to go beyond the technical nature of the living creature we want to copy. How and where to find a solution to this problem? Who tried to solve this in first place? In which context? And more importantly, can we adapt these methods in computer or robotics research? The answers given here can be found in the film industry and more particularly in the world of one of the pioneer studios in animation, the Walt Disney studios and its golden age: the late 1930s.
Synthetic Biology follows a Design-Build-Test-Learn cycle for the construction of novel biocircuits with a predefined behavior. Recently, a few simulation and bioCAD tools to assist synthetic biolo...
Experimentation represents today a ‘hot’ topic in computing. If experiments made with the support of computers, such as computer simulations, have received increasing attention from philosophers of...
The human brain is a remarkable computing machine, i.e. vastly parallel, self-organizing, robust, and energy efficient. To gain a better understanding into how the brain works, a cyborg (cybernetic...
Collective behavior, swarming and mutual interactions between living entities are well-known phenomena in biology where, for example, flocks of birds or schools of fish are extensively studied. Swarming has also been observed in bacteria or tumor cell populations. This kind of collective behavior can be implemented also in non-living systems, namely in biologically-inspired swarm robotics. However, the collective behavior of chemical droplets (mutual interactions of multiple "liquid robots") has not been studied before and the present paper reports the experimentally observed phenomena and modes of behavior in such system. We show how multiple decanol droplets in a thin layer of decanoate solution behave and interact. We report, for the first time, several life-like features such as collective chemotaxis and number-dependent group formation.
The sudden transition from egalitarian groups to hierarchical societies that occurred with the origin of agriculture is one of the most striking features of the evolution of human societies. Hierarchy is reflected by the evolution of an asymmetrical distribution of the influence of individuals. Although the benefits to leaders themselves are easily justified, it is still hard to identify the causes for the evolution of exploited followers. However, leaders also play an important role in solving coordination problems, a role which would have been amplified by the increase in group size induced by the advent of agriculture. Can this lead to the emergence of leadership directly from the evolution of traits affecting individual influence in group decisions? This question is yet unanswered mainly because of a lack of a mechanistic model linking individual influence to group productivity. Here we fill this gap by explicitly describing the organization of group by a decision-making process. We have developed an evolutionary model where individuals organize to carry out a collective task that produces surplus resources. These surplus resources then drive a demographic expansion of group size. Our results show that a stable distribution of leaders and followers can emerge from the evolution of traits affecting individual influence in decision making, even in the presence of inequality. In addition, our model highlights the conditions and dynamics underlying the development of hierarchy. In line with theoretical work on the evolutionary origins of leadership, this model contributes to understanding the interactions between individual evolution and social structure.
The work presented in this paper aims to address the problem of autonomous driving (especially along ill-defined roads) by using convolutional neural networks to predict the position and width of roads from camera input images. The networks are trained with supervised learning (i.e., back-propagation) using a dataset of annotated road images. We train two different network architectures for images corresponding to six colour models. They are tested "off-line" on a road detection task using image sequences not used in training. To benchmark our approach, we compare the performance of our networks with that of a different image processing method that relies on differences in colour distribution between the road and non-road areas of the camera input. Finally, we use a trained convolutional network to successfully navigate a Pioneer 3-AT robot on 5 distinct test paths. Results show that the network can safely guide the robot in this navigation task and that it is robust enough to deal with circumstances much different from those encountered during training.
Developing a comprehensive theory of open-ended evolution (OEE) depends critically on understanding the mechanisms underlying the major evolutionary transitions; such periods of rapid innovation, s...
The EvoMove system is a motion-based musical companion that relies on a commensal computing scheme. The system relies on wireless sensors to detect dancer moves. The sensor information is sent to KymeroClust, an evolutionary algorithm that identifies and maintains a clustering model of the move categories. The system uses this information to play audio samples according to the detected categories. These categories are not predefined, but are built dynamically by clustering the stream of data coming from the motion sensors. The EvoMove system has been tested by different users and subjective promising experiences are reported.
Stable bacterial cross-feeding interactions, where one strain feeds on the waste of the other, are important to understand, as they can be a first step towards bacterial speciation. Their emergence is commonly observed in laboratory experiments using Escherichia coli as a model organism. Yet it is not clear how cross-feeding interactions can resist the invasion of a fitter mutant when the environment contains a single resource since there seems to be a single ecological niche. Here, we used digital organisms to tackle this question, allowing for detailed and fast investigations, and providing a way to disentangle generic evolutionary mechanisms from specificities associated with E. coli. Digital organisms with evolvable genomes and metabolic networks compete for resources in conditions mimicking laboratory evolution experiments. In chemostat simulations, although cross-feeding interactions regularly emerged, selective sweeps regularly purged the population of its diversity. By contrast, batch culture allowed for much more stable cross-feeding interactions, because it includes seasons and thus distinct temporal niches, thereby favoring the adaptive diversification of proto-species.
Being able to measure time, whether directly or indirectly, is a significant advantage for an organism. It permits it to predict regular events, and prepare for them on time. Thus, clocks are ubiquitous in biology. In the present paper, we consider the most minimal abstract pure clocks and investigate their characteristics with respect to their ability to measure time. Amongst other, we find fundamentally diametral clock characteristics, such as oscillatory behaviour for local time measurement or decay-based clocks measuring time periods in scales global to the problem. We include also cascades of independent clocks ("clock bags") and composite clocks with controlled dependency; the latter show various regimes of markedly different dynamics.
Reservoir Computing has been highlighted as a promising methodology to perform computation in dynamical systems. This makes Reservoir Computing particularly interesting for exploiting physical systems directly as computing substrates, where the computation happens "for free" in the rich physical domain. In this work we consider a simple chaotic circuit as a reservoir: the Driven Chua's circuit. Its rich variety of available dynamics makes it versatile as a reservoir. At the same time, its simplicity offers insight into what physical properties can be useful for computation. We demonstrate both through simulation and in-circuit experiments, that such a simple circuit can be readily exploited for computation. Our results show excellent performance on two non-temporal tasks. The fact that such a simple nonlinear circuit can be used, suggests that a wide variety of physical systems can be viewed as potential reservoirs.
