4,365 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
Towards a Shared Control Navigation Function:Efficiency Based Command Modulation
This paper presents a novel shared control algorithm for robotized
wheelchairs. The proposed algorithm is a new method to extend
autonomous navigation techniques into the shared control domain. It reactively
combines user’s and robot’s commands into a continuous function
that approximates a classic Navigation Function (NF) by weighting input
commands with NF constraints. Our approach overcomes the main drawbacks
of NFs -calculus complexity and limitations on environment
modeling- so it can be used in dynamic unstructured environments. It also
benefits from NF properties: convergence to destination, smooth paths
and safe navigation. Due to the user’s contribution to control, our function
is not strictly a NF, so we call it a pseudo-navigation function (PNF)
instead.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
Constructing Futures: Outlining a Transhumanist Vision of the Future and the Challenge to Christian Theology of its Proposed Uses of New and Future Developments in Technology
Transhumanists arc committed to re-evaluating the entire human condition and offering proposalsfor transcending mortality, principally by augmenting the human body with mechanical components or by transferring the human mind into intelligent hyper-computers. In this essay, the author\'s methodology is to critique the culture oftranshumanism, arguing, with Barbour, that all technology is tool whose use is determined by the cultural and socialframeworks within which it is utilized. Transhumanism is characterized as morally ambiguous, extremely individualistic, fixated upon health, vitality, and power, ideological, reductionist, and self-deluded. Its proposed use of technology is, thus, highly suspect and deserves a robust theological response
Collective decision-making
Collective decision-making is the subfield of collective behaviour concerned with how groups reach decisions. Almost all aspects of behaviour can be considered in a decision-making context, but here we focus primarily on how groups should optimally reach consensus, what criteria decision-makers should optimise, and how individuals and groups should forage to optimise their nutrition. We argue for deep parallels between understanding decisions made by individuals and by groups, such as the decision-guiding principle of value-sensitivity. We also review relevant theory and empirical development for the study of collective decision making, including the use of robots
Collective decision-making
Collective decision-making is the subfield of collective behaviour concerned with how groups reach decisions. Almost all aspects of behaviour can be considered in a decision-making context, but here we focus primarily on how groups should optimally reach consensus, what criteria decision-makers should optimise, and how individuals and groups should forage to optimise their nutrition. We argue for deep parallels between understanding decisions made by individuals and by groups, such as the decision-guiding principle of value-sensitivity. We also review relevant theory and empirical development for the study of collective decision making, including the use of robots
Bio-Inspired Obstacle Avoidance: from Animals to Intelligent Agents
A considerable amount of research in the field of modern robotics deals with mobile agents and their autonomous operation in unstructured, dynamic, and unpredictable environments. Designing robust controllers that map sensory input to action in order to avoid obstacles remains a challenging task. Several biological concepts are amenable to autonomous navigation and reactive obstacle avoidance.
We present an overview of most noteworthy, elaborated, and interesting biologically-inspired approaches for solving the obstacle avoidance problem. We categorize these approaches into three groups: nature inspired optimization, reinforcement learning, and biorobotics. We emphasize the advantages and highlight potential drawbacks of each approach. We also identify the benefits of using biological principles in artificial intelligence in various research areas
Towards Swarm Calculus: Urn Models of Collective Decisions and Universal Properties of Swarm Performance
Methods of general applicability are searched for in swarm intelligence with
the aim of gaining new insights about natural swarms and to develop design
methodologies for artificial swarms. An ideal solution could be a `swarm
calculus' that allows to calculate key features of swarms such as expected
swarm performance and robustness based on only a few parameters. To work
towards this ideal, one needs to find methods and models with high degrees of
generality. In this paper, we report two models that might be examples of
exceptional generality. First, an abstract model is presented that describes
swarm performance depending on swarm density based on the dichotomy between
cooperation and interference. Typical swarm experiments are given as examples
to show how the model fits to several different results. Second, we give an
abstract model of collective decision making that is inspired by urn models.
The effects of positive feedback probability, that is increasing over time in a
decision making system, are understood by the help of a parameter that controls
the feedback based on the swarm's current consensus. Several applicable
methods, such as the description as Markov process, calculation of splitting
probabilities, mean first passage times, and measurements of positive feedback,
are discussed and applications to artificial and natural swarms are reported
Collective Complexity out of Individual Simplicity
The concept of Swarm Intelligence (SI) was first introduced by Gerardo Beni, Suzanne
Hackwood, and Jing Wang in 1989 when they were investigating the properties of
simulated, self-organizing agents in the framework of cellular robotic systems [1]. Eric
Bonabeau, Marco Dorigo, and Guy Theraulaz extend the restrictive context of this
early work to include “any attempt to design algorithms or distributed problem-solving
devices inspired by the collective behavior of social insect colonies,” such as ants,
termites, bees, wasps, “and other animal societies.” The abilities of such systems appear
to transcend the abilities of the constituent individuals. In most biological cases studied
so far, robust and capable high-level group behavior has been found to be mediated
by nothing more than a small set of simple low-level interactions between individuals,
and between individuals and the environment. The SI approach, therefore, emphasizes
parallelism, distributedness, and exploitation of direct (agent-to-agent) or indirect (via
the environment) local interactions among relatively simple agents
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