Bio-inspired Artificial Intelligence Research Articles

Optimum Path Planning Using Dragonfly-Fuzzy Hybrid Controller for Autonomous Vehicle

Navigation poses a significant challenge for autonomous vehicles, prompting the exploration of various bio-inspired artificial intelligence techniques to address issues related to path generation, obstacle avoidance, and optimal path planning. Numerous studies have delved into bio-inspired approaches to navigate and overcome obstacles. In this paper, we introduce the dragonfly algorithm (DA), a novel bio-inspired meta-heuristic optimization technique to autonomously set goals, detect obstacles, and minimize human intervention. To enhance efficacy in unstructured environments, we propose and analyze the dragonfly–fuzzy hybrid algorithm, leveraging the strengths of both approaches. This hybrid controller amalgamates diverse features from different methods into a unified framework, offering a multifaceted solution. Through a comparative analysis of simulation and experimental results under varied environmental conditions, the hybrid dragonfly–fuzzy controller demonstrates superior performance in terms of time and path optimization compared to individual algorithms and traditional controllers. This research aims to contribute to the advancement of autonomous vehicle navigation through the innovative integration of bio-inspired meta-heuristic optimization techniques.

Liquid Metal Memory.

Storage systems are vital components of electronic devices, while significant challenges persist in achieving flexible memory due to the limitations of existing storage methodologies. Inspired by the polarization and depolarization mechanisms in the human brain, here a novel class of storage principles is proposed and achieve a fully flexible memory through introducing the oxidation and deoxidation behaviors of liquid metals. Specifically, reversible electrochemical oxidation is utilized to modulate the overall conductivity of the target liquid metals, creating a substantial 11-order resistance difference for binary data storage. To obtain the best storage performance, systematic optimizations of multiple parameters are conducted. Conceptual experiments demonstrate the memory's stability under extreme deformations (100% stretching, 180° bending, 360° twisting). Further tests reveal that the memory performs better when its unit size gets smaller, warranting superior integrability. Finally, a complete storage system achieves remarkable performance metrics, including rapid storage speed (>33Hz), long data retention capacity (>43200s), and stable repeatable operation (>3500 cycles). This groundbreaking method not only overcomes the inherent rigidity limitations of existing electronic storage units but also opens new possibilities for innovating neuromorphic devices, offering fundamental and practical avenues for future applications in soft robotics, wearable electronics, and bio-inspired artificial intelligence systems.

Joint Reconfiguration after Failure for Performing Emblematic Gestures in Humanoid Receptionist Robot.

This study proposed a strategy for a quick fault recovery response when an actuator failure problem occurred while a humanoid robot with 7-DOF anthropomorphic arms was performing a task with upper body motion. The objective of this study was to develop an algorithm for joint reconfiguration of the receptionist robot called Namo so that the robot can still perform a set of emblematic gestures if an actuator fails or is damaged. We proposed a gesture similarity measurement to be used as an objective function and used bio-inspired artificial intelligence methods, including a genetic algorithm, a bacteria foraging optimization algorithm, and an artificial bee colony, to determine good solutions for joint reconfiguration. When an actuator fails, the failed joint will be locked at the average angle calculated from all emblematic gestures. We used grid search to determine suitable parameter sets for each method before making a comparison of their performance. The results showed that bio-inspired artificial intelligence methods could successfully suggest reconfigured gestures after joint motor failure within 1 s. After 100 repetitions, BFOA and ABC returned the best-reconfigured gestures; there was no statistical difference. However, ABC yielded more reliable reconfigured gestures; there was significantly less interquartile range among the results than BFOA. The joint reconfiguration method was demonstrated for all possible joint failure conditions. The results showed that the proposed method could determine good reconfigured gestures under given time constraints; hence, it could be used for joint failure recovery in real applications.

Bio-Inspired Artificial Intelligence with Natural Language Processing Based on Deceptive Content Detection in Social Networking.

In recent research, fake news detection in social networking using Machine Learning (ML) and Deep Learning (DL) models has gained immense attention. The current research article presents the Bio-inspired Artificial Intelligence with Natural Language Processing Deceptive Content Detection (BAINLP-DCD) technique for social networking. The goal of the proposed BAINLP-DCD technique is to detect the presence of deceptive or fake content on social media. In order to accomplish this, the BAINLP-DCD algorithm applies data preprocessing to transform the input dataset into a meaningful format. For deceptive content detection, the BAINLP-DCD technique uses a Multi-Head Self-attention Bi-directional Long Short-Term Memory (MHS-BiLSTM) model. Finally, the African Vulture Optimization Algorithm (AVOA) is applied for the selection of optimum hyperparameters of the MHS-BiLSTM model. The proposed BAINLP-DCD algorithm was validated through simulation using two benchmark fake news datasets. The experimental outcomes portrayed the enhanced performance of the BAINLP-DCD technique, with maximum accuracy values of 92.19% and 92.56% on the BuzzFeed and PolitiFact datasets, respectively.

Mind the matter: Active matter, soft robotics, and the making of bio-inspired artificial intelligence.

Philosophical and theoretical debates on the multiple realisability of the cognitive have historically influenced discussions of the possible systems capable of instantiating complex functions like memory, learning, goal-directedness, and decision-making. These debates have had the corollary of undermining, if not altogether neglecting, the materiality and corporeality of cognition-treating material, living processes as "hardware" problems that can be abstracted out and, in principle, implemented in a variety of materials-in particular on digital computers and in the form of state-of-the-art neural networks. In sum, the matter in se has been taken not to matter for cognition. However, in this paper, we argue that the materiality of cognition-and the living, self-organizing processes that it enables-requires a more detailed assessment when understanding the nature of cognition and recreating it in the field of embodied robotics. Or, in slogan form, that the matter matters for cognitive form and function. We pull from the fields of Active Matter Physics, Soft Robotics, and Basal Cognition literature to suggest that the imbrication between material and cognitive processes is closer than standard accounts of multiple realisability suggest. In light of this, we propose upgrading the notion of multiple realisability from the standard version-what we call 1.0-to a more nuanced conception 2.0 to better reflect the recent empirical advancements, while at the same time averting many of the problems that have been raised for it. These fields are actively reshaping the terrain in which we understand materiality and how it enables, mediates, and constrains cognition. We propose that taking the materiality of our embodied, precarious nature seriously furnishes an important research avenue for the development of embodied robots that autonomously value, engage, and interact with the environment in a goal-directed manner, in response to existential needs of survival, persistence, and, ultimately, reproduction. Thus, we argue that by placing further emphasis on the soft, active, and plastic nature of the materials that constitute cognitive embodiment, we can move further in the direction of autonomous embodied robots and Artificial Intelligence.

Backbone model for reinforced concrete block shear wall components and systems using controlled multigene genetic programming

Reinforced concrete block shear walls (RCBSWs)have been used as an effective seismic force resisting system in low- and medium-rise buildings for many decades. However, attributed to their complex nonlinear behavior and the composite nature of their constituent materials, accurate prediction of their seismic performance, relying solely on mechanics, has been challenging. This study adopts multi-gene genetic programming (MGGP)— a class of bio-inspired artificial intelligence, to uncover the complexity of RCBSW behaviors and develop simplified procedures for predicting the full backbone curve of flexure-dominated RCBSWs under cyclic loading. A piecewise linear backbone curve was developed using five secant stiffness expressions associated with: cracking; yielding; 80% ultimate; ultimate; and 20% strength degradation (i.e., post-peak stage) derived through mechanics-controlled MGGP. Based on the experimental results of large-scale cyclically loaded fully-grouted RCBSWs, compiled from previously reported studies, a variable selection procedure was performed to identify the most influential variable subset governing wall behaviors. Subsequently, the MGGP stiffness expressions were trained and tested, and their accuracy was compared to that of existing models employing various statistical measures. In addition, the predictability of the developed backbone model was assessed at the system-level against experimental results of two two-story buildings available in the literature. This study demonstrates the power of the MGGP approach in addressing the complexity of the cyclic behavior of RCBSWs at both component- and system-level—offering an efficient prediction tool that can be adopted by relevant seismic design standards pertaining to RCBSW buildings.

Optimization of the Mechanical Property of Friction Stir Welded Heat Treatable Aluminum Alloy by using Bio-Inspired Artificial Intelligence Algorithms

The concepts and inspiration of biological evolution in nature are used to create new and effective competing tactics in the burgeoning field of bio-inspired computing optimization algorithms. In the present work, nine specimens of similar alloys i.e., AA6262 were Friction Stir Welded. Spindle Speed (RPM), Traverse Speed (mm/min), and Plunge Depth (mm) were the input parameters while the Ultimate Tensile Strength (MPa) was an output parameter. The main objective of the work is to obtain the maximum optimized Ultimate Tensile Strength (MPa) by using Bio-Inspired Artificial Intelligence Algorithms i.e., Differential Evolution and Max Lipschitz optimization (Max LIPO) Algorithm. The results showed that the Differential Evolution algorithm resulted in a slightly higher value of the Ultimate Tensile Strength in comparison to the Max LIPO algorithm.

Temperature data-driven fire source estimation algorithm of the underground pipe gallery

Since there exists few effective fire detection technologies for underground pipe gallery application, a temperature data-driven bio-inspired artificial intelligence algorithm is developed to detect fire source in 3D space of the underground pipe gallery, in which a simple physical model is used. In the developed algorithm, Ant colony optimization (ACO) is the first time to be used to determine tunnel fire source, and the new and special pheromone evaporation method and heuristic factor are developed for fitting the concerned problem here. Three fire experiments are used to support the ability of the algorithm. Satisfactory results can always be obtained, which shows that the developed algorithm can be used to estimate the tunnel fire source as well as temperature prediction. In addition, since only temperature data at several sensors is necessary in the developed algorithm, it has a very wide popularization and engineering application prospects due to its advantages of the global optimal ability and computational efficiency as well as the low economic cost.

Bioinspired Simultaneous Changes in Fluorescence Color, Brightness, and Shape of Hydrogels Enabled by AIEgens.

Development of stimuli-responsive materials with complex practical functions is significant for achieving bioinspired artificial intelligence. It is challenging to fabricate stimuli-responsive hydrogels showing simultaneous changes in fluorescence color, brightness, and shape in response to a single stimulus. Herein, a bilayer hydrogel strategy is designed by utilizing an aggregation-induced emission luminogen, tetra-(4-pyridylphenyl)ethylene (TPE-4Py), to fabricate hydrogels with the above capabilities. Bilayer hydrogel actuators with the ionomer of poly(acrylamide-r-sodium 4-styrenesulfonate) (PAS) as a matrix of both active and passive layers and TPE-4Py as the core function element in the active layer are prepared. At acidic pH, the protonation of TPE-4Py leads to fluorescence color and brightness changes of the actuators and the electrostatic interactions between the protonated TPE-4Py and benzenesulfonate groups of the PAS chains in the active layer cause the actuators to deform. The proposed TPE-4Py/PAS-based bilayer hydrogel actuators with such responsiveness to stimulus provide insights in the design of intelligent systems and are highly attractive material candidates in the fields of 3D/4D printing, soft robots, and smart wearable devices.

A Newly Developed Integrative Bio-Inspired Artificial Intelligence Model for Wind Speed Prediction

Accurate wind speed (WS) modelling is crucial for optimal utilization of wind energy. Numerical Weather Prediction (NWP) techniques, generally used for WS modelling are not only less cost-effective but also poor in predicting in shorter time horizon. Novel WS prediction models based on the multivariate empirical mode decomposition (MEMD), random forest (RF) and Kernel Ridge Regression (KRR) were constructed in this paper better accuracy in WS prediction. Particle swarm optimization algorithm (PSO) was employed to optimize the parameters of the hybridized MEMD model with RF (MEMD-PSO-RF) and KRR (MEMD-PSO-KRR) models. Obtained results were compared to those of the standalone RF and KRR models. The proposed methodology is applied for monthly WS prediction at meteorological stations of Iraq, Baghdad (Station1) and Mosul (Station2) for the period 1977-2013. Results showed higher accuracy of MEMD-PSO-RF model in predicting WS at both stations with a correlation coefficient (r) of 0.972 and r = 0.971 during testing phase at Station1 and Station2, respectively. The MEMD-PSO-KRR was found as the second most accurate model followed by Standalone RF and KRR, but all showed a competitive performance to the MEMD-PSO-RF model. The outcomes of this work indicated that the MEMD-PSO-RF model has a remarkable performance in predicting WS and can be considered for practical applications.

A General Approach in Optimization of Heat Exchangers by Bio-Inspired Artificial Intelligence Methods

The paper introduces the artificial intelligence (AI) approach as a general method for the design and optimization study of heat exchangers. Genetic Algorithms (GA) and Artificial Neural Networks (ANN) are applied in the paper. An AGENN model, combining Genetic Algorithms with Artificial Neural Networks, was developed and validated against the desired data on a large falling film evaporator. A broad range of operating conditions and geometric configurations are considered in the study. Four kinds of tubes are deliberated, including plain and enhanced tubes. Different tube pass arrangements, i.e., top-to-bottom, bottom-to-top, and side-by-side, are discussed. Finally, the effects of liquid refrigerant mass flow rate, as well as the number of flooded tubes on the performance of the evaporator, are analyzed. The total heat transfer rate of the evaporator, predicted by the model, is in good agreement with the desired data; the maximum error is lower than ±3%. The highest heat transfer rate of the evaporator is 1140.01 kW and corresponds to Turbo EHP tubes, and bottom-to-top tubes pass arrangements, which guarantee the best thermal energy conversion. The presented approach can be referred to as a complementary technique in heat exchanger design procedures, besides the common rating and sizing tasks. It is an effective and alternative method for the existing approaches, considering the complexity of analytical and numerical techniques as well as the high costs of experiments.

The Evolutionary Optimization of a Company’s Return on Equity Factor: Towards the Agent-Based Bio-Inspired System Supporting Corporate Finance Decisions

Return on Equity (ROE) is an important factor from the perspective of formulating and implementing a company’s financial strategies. It is also one of its evaluation criteria. It presents to investors the effectiveness of using their capital. Increasing profitability may be treated as a symptom of shareholder wealth, while its reduction may be a signal indicating a deterioration of the financial situation of the company. An investment in a company will be attractive to investors if the results obtained by it will enable the benefit from the dividend to be paid and if the share prices will show an upward trend. Therefore, profit and profitability are categories dependent on the company and affect the wealth of its owners. The ROE ratio is synthetic and is linked to, among others with the size of sales, asset use activity, and the size of the company’s debt. However, the decisions regarding the capital structure of a company should be made not only by purely economic and financial analyses but also should take into account the social and environmental effects of economic activities. To take into account not only short-term financial goals but also long-term sustainable development goals during the decision-making process, we need intelligent and creative multi-criteria decision support tools. Bio-inspired artificial intelligence techniques—such as evolutionary algorithms, deep neural networks, or swarm algorithms, to give only a few examples—are gaining more and more popularity in the recent years. Evolutionary algorithms are optimization techniques that are modeled on the processes of evolution that are taking place in natural populations. They can find approximate solutions to the NP-hard global, multi-modal, and multi-objective optimization problems. In this paper, we propose an innovative approach—an agent-based bio-inspired system supporting decisions in the area of corporate finance, which takes into account not only financial goals but also the sustainable development goals. The system will allow for multi-objective optimization with the use of bio-inspired algorithms. In this paper, we will concentrate on one module of the proposed system—the evolutionary algorithm optimizing the ROE factor. During the experiments, we will verify the ability of the proposed algorithm to provide decision makers with reasonable, useful, and, at the same time, also innovative and non-obvious solutions concerning the desired capital structure of a given company, which usually operates in a rapidly changing environment. The proposed system will allow for taking into account more than one criteria and perform multi-objective optimization with the use of an evolutionary algorithm or an agent-based co-evolutionary algorithm, so it will be possible to include also the long-term goals of sustainable development in the future.

Artificial and Biological Intelligence: Hardware vs Wetware

By starting from physical principles, the paper formulates the basis of the biological intelligence and coscience formation. The work shows that far from equilibrium, the principle of maximum energy dissipation, in a fluid phase, brings the molecules to organize themselves in living systems (ordered stationary states) that sustain energy-matter fluxes that determine the architecture of the dissipative system. The driver of the process is the energy; the matter is the substrate. On this base, and thanks to a “selection rule”, the living systems developed a bi-phasic structure consisting of a solid network permeated by a liquid where fluxes of ions and molecules can be maintained. In this way they realize a “wetware” where the energy and the organization/information are handled, and where a high specialization of functions can be obtained by using the spatial delocalization leading to the development of the complexity of the shape. The outputs of a living system can be divided in two categories: 1) macroscopic outputs: Movements, definition of an organized reaction or plan of action, and 2) microscopic ones: Plastic rearrangement of structures, migration of chemical species and electrochemical dynamics (transportation of matter, rearrangement, sensing, storage and information handling). The biological intelligence is a product of these basic processes where the wetware (matter substrate) is continuously modified by the energy-matter fluxes. On this base, the artificial intelligence of computers can be seen as a subclass of intelligent processes since it owns some limitations due to the invariance of the material substrate (that is not self-modified by the energy fluxes) and by the unique form of the flux of matter that is given by the electron current. Finally, the definition of a bio-inspired artificial intelligence is discussed.

Artificial and Biological Intelligence: Hardware vs Wetware

By starting from physical principles, the paper formulates the basis of the biological intelligence and coscience formation. The work shows that far from equilibrium, the principle of maximum energy dissipation, in a fluid phase, brings the molecules to organize themselves in living systems (ordered stationary states) that sustain energy-matter fluxes that determine the architecture of the dissipative system. The driver of the process is the energy; the matter is the substrate. On this base, and thanks to a “selection rule”, the living systems developed a bi-phasic structure consisting of a solid network permeated by a liquid where fluxes of ions and molecules can be maintained. In this way they realize a “wetware” where the energy and the organization/information are handled, and where a high specialization of functions can be obtained by using the spatial delocalization leading to the development of the complexity of the shape. The outputs of a living system can be divided in two categories: 1) macroscopic outputs: Movements, definition of an organized reaction or plan of action, and 2) microscopic ones: Plastic rearrangement of structures, migration of chemical species and electrochemical dynamics (transportation of matter, rearrangement, sensing, storage and information handling). The biological intelligence is a product of these basic processes where the wetware (matter substrate) is continuously modified by the energy-matter fluxes. On this base, the artificial intelligence of computers can be seen as a subclass of intelligent processes since it owns some limitations due to the invariance of the material substrate (that is not self-modified by the energy fluxes) and by the unique form of the flux of matter that is given by the electron current. Finally, the definition of a bio-inspired artificial intelligence is discussed.

On the Relationships between Generative Encodings, Regularity, and Learning Abilities when Evolving Plastic Artificial Neural Networks

A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1) the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2) synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected. Using a simple operant conditioning task and a classic evolutionary algorithm, we compare three ways to encode plastic neural networks: a direct encoding, a developmental encoding inspired by computational neuroscience models, and a developmental encoding inspired by morphogen gradients (similar to HyperNEAT). Our results suggest that using a developmental encoding could improve the learning abilities of evolved, plastic neural networks. Complementary experiments reveal that this result is likely the consequence of the bias of developmental encodings towards regular structures: (1) in our experimental setup, encodings that tend to produce more regular networks yield networks with better general learning abilities; (2) whatever the encoding is, networks that are the more regular are statistically those that have the best learning abilities.

MIANN Models in Medicinal, Physical and Organic Chemistry

Reducing costs in terms of time, animal sacrifice, and material resources with computational methods has become a promising goal in Medicinal, Biological, Physical and Organic Chemistry. There are many computational techniques that can be used in this sense. In any case, almost all these methods focus on few fundamental aspects including: type (1) methods to quantify the molecular structure, type (2) methods to link the structure with the biological activity, and others. In particular, MARCH-INSIDE (MI), acronym for Markov Chain Invariants for Networks Simulation and Design, is a well-known method for QSAR analysis useful in step (1). In addition, the bio-inspired Artificial-Intelligence (AI) algorithms called Artificial Neural Networks (ANNs) are among the most powerful type (2) methods. We can combine MI with ANNs in order to seek QSAR models, a strategy which is called herein MIANN (MI & ANN models). One of the first applications of the MIANN strategy was in the development of new QSAR models for drug discovery. MIANN strategy has been expanded to the QSAR study of proteins, protein-drug interactions, and protein-protein interaction networks. In this paper, we review for the first time many interesting aspects of the MIANN strategy including theoretical basis, implementation in web servers, and examples of applications in Medicinal and Biological chemistry. We also report new applications of the MIANN strategy in Medicinal chemistry and the first examples in Physical and Organic Chemistry, as well. In so doing, we developed new MIANN models for several self-assembly physicochemical properties of surfactants and large reaction networks in organic synthesis. In some of the new examples we also present experimental results which were not published up to date.

Directions for Bio-Inspired Artificial Intelligence

Directions for Bio-Inspired Artificial Intelligence

Erratum to: Dario Floreano and Claudio Mattiussi: Bio-inspired artificial intelligence: theories, methods, and technologies

Erratum to: Dario Floreano and Claudio Mattiussi: Bio-inspired artificial intelligence: theories, methods, and technologies

Dario Floreano and Claudio Mattiussi (eds): Bio-inspired artificial intelligence: theories, methods, and technologies

Dario Floreano and Claudio Mattiussi (eds): Bio-inspired artificial intelligence: theories, methods, and technologies

Bio-inspired artificial intelligence: theories, methods, and technologies

New approaches to artificial intelligence spring from the idea that intelligence emerges as much from cells, bodies, and societies as it does from evolution, development, and learning. Traditionally, artificial intelligence has been concerned with reproducing the abilities of human brains; newer approaches take inspiration from a wider range of biological structures that that are capable of autonomous self-organization. Examples of these new approaches include evolutionary computation and evolutionary electronics, artificial neural networks, immune systems, biorobotics, and swarm intelligenceto mention only a few. This book offers a comprehensive introduction to the emerging field of biologically inspired artificial intelligence that can be used as an upper-level text or as a reference for researchers. Each chapter presents computational approaches inspired by a different biological system; each begins with background information about the biological system and then proceeds to develop computational models that make use of biological concepts. The chapters cover evolutionary computation and electronics; cellular systems; neural systems, including neuromorphic engineering; developmental systems; immune systems; behavioral systemsincluding several approaches to robotics, including behavior-based, bio-mimetic, epigenetic, and evolutionary robots; and collective systems, including swarm robotics as well as cooperative and competitive co-evolving systems. Chapters end with a concluding overview and suggested reading.