Bio Inspired Computing

Cognition is meant as the process of acquiring knowledge from the world. This process is supposed to happen within agents, which build such knowledge with the purpose to use it to determine their actions on the world. Following Peircean ideas, we postulate that such knowledge is encoded by means of signs. According to Peirce, signs are anything that can be used to represent anything else. Also, for Peirce, to represent means to be able to generate another sign, called the interpretant of the original sign, which still holds the same power of interpretability, I.e, its power to be transformed into a new sign, holding this same power. This happens through a process called semiosis, the process by which a sign is transformed into an interpretant. This whole process is performed with the aim of subsidizing the agent in deciding its behavior. So, even though the semiosis process has the power to continue infinitely, it usually stops whenever the generated interpretant brings enough information in order for the agent to effectively act in the world. We take signals to be the substract of signs. Signals are any physical property, which can be measured and captured by the agent, by means of its sensors. This includes any kind of internal memory the agent is able to have access, in order to operate. In this sense, signs can be both in the world (if these
signals come from sensors) and within the own agent’s mind (if signals come from an internal memory). We understand an agent’s mind as the agents’ control system. In either case, signals can be abstracted as numbers. Not simply numbers, but numbers coming from specific sensors or specific memories. Using ideas from Peircean philosophy, in this work we postulate a pathway, in which signals, collected by either sensors or memory, can be organized in such a way that they can be effectively used as knowledge, in order for an agent to be able to decide its actions on the world, on the pursuit of its internal motivations. We postulate that agents identify and create a model of the world based on possibilities, existents, and laws, and based on this model, they are able to decide an action that maximizes the chance for the world to gain a shape, which the agents intend for it to be. This theory is postulated particularly for the case of artificial autonomous agents, meant to be constructed by engineering artifacts.

In this work, we investigate the possibility of using inspiration from the self-organizing property of organisms in nature for providing visual representation of an invisible pollutant profile. We present a novel mathematical model of the bacterium and use it to find pollutants in the environment. This model has the capability of exploring the environment to search for sparsely distributed pollutants or food sources and then subsequently exploiting them upon discovery. We also combine the bacterium model in a bacterium–flock algorithm for the purposes of preventing collisions between robots or organisms in addition to providing coverage to a pollutant. By adjusting the velocity of individuals, we show that we are able to control the coverage provided by the population as a whole. Furthermore, we compare the bacterium–flock algorithm with a novel gradient-ascent-flocking algorithm and the well established Voronoi partition algorithm. Results show that bacterium–flock algorithm and the Voronoi partition algorithm are capable of adapting the distribution of the individuals of a population based upon the underlying pollutant profile while the gradient-ascent-flocking algorithm is not. This shows that the bacterium–flock and the Voronoi partition algorithms can potentially be used to track a spatiotemporal function. On the other hand, the gradient-ascent-flock algorithm has a faster convergence time in some cases with the Voronoi partition algorithm having the slowest convergence time overall.

Artificial Human Optimization is a new field that came into existence on December 2016. All the optimization algorithms that were created and are being created based on Artificial Humans will come under Artificial Human Optimization Field. Just like agents in Ant Colony Optimization are Artificial Ants, agents in Bee Colony Optimization are Artificial Bees, agents in Genetic Algorithms are Artificial chromosomes, agents in Particle Swarm Optimization are Artificial Birds or Artificial Fishes, similarly agents in Artificial Human Optimization Algorithms are Artificial Humans. “Multiple Strategy Human Optimization (MSHO)” is a new algorithm designed recently based on Artificial Humans. The key concept in MSHO is to use more than one strategy in the optimization process. Two strategies are used in MSHO. One strategy is to move towards the best individual in one generation. Another strategy is to move away from the worst individual in next generation. Differential Evolution is a popular algorithm for solving optimization problems in various domains. In this paper “Human Inspired Differential Evolution (HIDE)” is proposed. The idea of HIDE algorithm is to use the concept of Multiple Strategies of MSHO algorithm in Differential Evolution. The mutation operator of Differential Evolution algorithm is modified to incorporate the key concept of MSHO algorithm in Differential Evolution. The proposed HIDE algorithm is tested by applying it on a complex benchmark problem.

The automata theory is the mathematical study of abstract machines commonly studied in the theoretical computer science and highly interdisciplinary fields that combine the natural sciences and the theoretical computer science. In the present review article, as the chemical and biological basis for natural computing or informatics, some plants, plant cells or plant-derived molecules involved in signaling are listed and classified as natural sequential machines (namely, the Mealy machines or Moore machines) or finite state automata. By defining the actions (states and transition functions) of these natural automata, the similarity between the computational data processing and plant decision-making processes became obvious. Finally, their putative roles as the parts for plant-based computing or robotic systems are discussed.