126 research outputs found
Cellular Automata Applications in Shortest Path Problem
Cellular Automata (CAs) are computational models that can capture the
essential features of systems in which global behavior emerges from the
collective effect of simple components, which interact locally. During the last
decades, CAs have been extensively used for mimicking several natural processes
and systems to find fine solutions in many complex hard to solve computer
science and engineering problems. Among them, the shortest path problem is one
of the most pronounced and highly studied problems that scientists have been
trying to tackle by using a plethora of methodologies and even unconventional
approaches. The proposed solutions are mainly justified by their ability to
provide a correct solution in a better time complexity than the renowned
Dijkstra's algorithm. Although there is a wide variety regarding the
algorithmic complexity of the algorithms suggested, spanning from simplistic
graph traversal algorithms to complex nature inspired and bio-mimicking
algorithms, in this chapter we focus on the successful application of CAs to
shortest path problem as found in various diverse disciplines like computer
science, swarm robotics, computer networks, decision science and biomimicking
of biological organisms' behaviour. In particular, an introduction on the first
CA-based algorithm tackling the shortest path problem is provided in detail.
After the short presentation of shortest path algorithms arriving from the
relaxization of the CAs principles, the application of the CA-based shortest
path definition on the coordinated motion of swarm robotics is also introduced.
Moreover, the CA based application of shortest path finding in computer
networks is presented in brief. Finally, a CA that models exactly the behavior
of a biological organism, namely the Physarum's behavior, finding the
minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From
software to wetware. Springer, 201
Computational aspects of cellular intelligence and their role in artificial intelligence.
The work presented in this thesis is concerned with an exploration of the computational aspects of the primitive intelligence associated with single-celled organisms. The main aim is to explore this Cellular Intelligence and its role within Artificial Intelligence. The findings of an extensive literature search into the biological characteristics, properties and mechanisms associated with Cellular Intelligence, its underlying machinery - Cell Signalling Networks and the existing computational methods used to capture it are reported. The results of this search are then used to fashion the development of a versatile new connectionist representation, termed the Artificial Reaction Network (ARN). The ARN belongs to the branch of Artificial Life known as Artificial Chemistry and has properties in common with both Artificial Intelligence and Systems Biology techniques, including: Artificial Neural Networks, Artificial Biochemical Networks, Gene Regulatory Networks, Random Boolean Networks, Petri Nets, and S-Systems. The thesis outlines the following original work: The ARN is used to model the chemotaxis pathway of Escherichia coli and is shown to capture emergent characteristics associated with this organism and Cellular Intelligence more generally. The computational properties of the ARN and its applications in robotic control are explored by combining functional motifs found in biochemical network to create temporal changing waveforms which control the gaits of limbed robots. This system is then extended into a complete control system by combining pattern recognition with limb control in a single ARN. The results show that the ARN can offer increased flexibility over existing methods. Multiple distributed cell-like ARN based agents termed Cytobots are created. These are first used to simulate aggregating cells based on the slime mould Dictyostelium discoideum. The Cytobots are shown to capture emergent behaviour arising from multiple stigmergic interactions. Applications of Cytobots within swarm robotics are investigated by applying them to benchmark search problems and to the task of cleaning up a simulated oil spill. The results are compared to those of established optimization algorithms using similar cell inspired strategies, and to other robotic agent strategies. Consideration is given to the advantages and disadvantages of the technique and suggestions are made for future work in the area. The report concludes that the Artificial Reaction Network is a versatile and powerful technique which has application in both simulation of chemical systems, and in robotic control, where it can offer a higher degree of flexibility and computational efficiency than benchmark alternatives. Furthermore, it provides a tool which may possibly throw further light on the origins and limitations of the primitive intelligence associated with cells
Intelligent Robotics Navigation System: Problems, Methods, and Algorithm
This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agent’s structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments
Emergence of behavior in a self-organized living matter network
What is the origin of behavior? Although typically associated with a nervous system, simple life forms also show complex behavior – thus serving as a model to study how behaviors emerge. Among them, the slime mold Physarum polycephalum, growing as a single giant cell, is renowned for its sophisticated behavior. Here, we show how locomotion and morphological adaptation behavior emerge from self-organized patterns of rhythmic contractions of the actomyosin lining of the tubes making up the network-shaped organism. We quantify the spatio-temporal contraction dynamics by decomposing experimentally recorded contraction patterns into spatial contraction modes. Surprisingly, we find a continuous spectrum of modes, as opposed to few dominant modes. Over time, activation of modes along this continuous spectrum is highly dynamic, resulting in contraction patterns of varying regularity. We show that regular patterns are associated with stereotyped behavior by triggering a behavioral response with a food stimulus. Furthermore, we demonstrate that the continuous spectrum of modes and the existence of irregular contraction patterns persist in specimens with a morphology as simple as a single tube. Our data suggests that the continuous spectrum of modes allows for dynamic transitions between a plethora of specific behaviors with transitions marked by highly irregular contraction states. By mapping specific behaviors to states of active contractions, we provide the basis to understand behavior’s complexity as a function of biomechanical dynamics. This perspective will likely stimulate bio-inspired design of soft robots with a similarly rich behavioral repertoire as P. polycephalum
A swarm intelligence based approach to the mine detection problem
This research focuses on the application of swarm intelligence to the problem of mine detection. Swarm Intelligence concepts have captivated the interests of researchers mainly in collective robotics, optimization problems (traveling salesman problem (TSP), quadratic assignment problem, graph coloring etc.), and communication networks (routing) etc [1]. In the mine detection problem we are faced with sub problems such as searching for the mines over the minefield, defusing them effectively, and assuring that the field is clear of mines within the least possible time. In the problem, we assume that the mines can be diffused by the collective action of the robots for which a model based on ant colonies is given. In the first part of the project we study the ant colony system applied to the mine detection problem. The theoretical aspects such as the ant\u27s behavior (reaction of the ants to various circumstances that it faces), their motion over the minefield, and their process of defusing the mines are investigated. In the second section we highlight a certain formulation that the ants may be given for doing the task effectively. The ants do the task effectively when they are able to assure that the minefield is clear of the mines within the least possible time. A compilation of the results obtained by the various studies is tabulated. In the third and final section we talk about our emulations conducted on the Multi Agent Biorobotics Lab-built groundscout robots, which were used for the demonstration of our swarm intelligence-based algorithms at a practical basis. The various projects thus far conducted were a part of the Multi Agent Biorobotics Lab at Rochester Institute of Technology
Mathematical models for chemotaxis and their applications in self-organisation phenomena
Chemotaxis is a fundamental guidance mechanism of cells and organisms,
responsible for attracting microbes to food, embryonic cells into developing
tissues, immune cells to infection sites, animals towards potential mates, and
mathematicians into biology. The Patlak-Keller-Segel (PKS) system forms part of
the bedrock of mathematical biology, a go-to-choice for modellers and analysts
alike. For the former it is simple yet recapitulates numerous phenomena; the
latter are attracted to these rich dynamics. Here I review the adoption of PKS
systems when explaining self-organisation processes. I consider their
foundation, returning to the initial efforts of Patlak and Keller and Segel,
and briefly describe their patterning properties. Applications of PKS systems
are considered in their diverse areas, including microbiology, development,
immunology, cancer, ecology and crime. In each case a historical perspective is
provided on the evidence for chemotactic behaviour, followed by a review of
modelling efforts; a compendium of the models is included as an Appendix.
Finally, a half-serious/half-tongue-in-cheek model is developed to explain how
cliques form in academia. Assumptions in which scholars alter their research
line according to available problems leads to clustering of academics and the
formation of "hot" research topics.Comment: 35 pages, 8 figures, Submitted to Journal of Theoretical Biolog
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