Top PDF Creativity and Autonomy in Swarm Intelligence Systems

Creativity and Autonomy in Swarm Intelligence Systems

Creativity and Autonomy in Swarm Intelligence Systems

Similarly, as is discussed in the next section, if the same sketch is repeat- edly given to the hybrid swarm architecture, the output drawings, made by the swarms, are never the same. In other words, even if the hybrid swarm mechanism (of birds and ants) process the same input several times, it will not make two identical drawings; furthermore, the outputs it produces are not merely randomised variants of the input. This can be demonstrated qualita- tively by comparing the output of the hybrid swarm system with a simple randomised tracing algorithm, where each point in the sketch is surrounded with discs (similar to the pAgents) at a Gaussian random distance and direc- tion (contrast Figures 4 and 5). The reason why the hybrid swarm drawings are different from the simple randomised sketch, is that the underlying PSO flock- ing component-algorithm constantly endeavours to accurately trace the input image whilst the SDS foraging component constantly endeavours to explore the wider canvas (i.e. together the two swarm mechanisms ensure high-level fidelity to the input without making an exact low-level copy of the original sketch) 2 .
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Teacher Creativity in Light of Autonomy and Emotional Intelligence

Teacher Creativity in Light of Autonomy and Emotional Intelligence

As to the second research question which delved into the potential relationship between high school teachers' autonomy and their creativity, the results pointed to a positive correlation between the two variables for teachers. As Wang and Zhang (2014) contend, increased teacher autonomy can bring about more creative practices and foster curriculum reform. Hermansen (2017) and Vangrieken et al.'s (2017) emphasis on collective and collaborative autonomy also seems to help open new horizons for practitioners and communities of practice to mull over more creative breakthroughs for more successful teaching. Furthermore, to pave the way for more autonomous and creative practice on the part of teachers, as Nguyen and Wlakinshaw's (2018) study reveals, initially an attempt must be made to remove the constraints (structural, contextual, cultural & individual ones) hampering teachers' sense of autonomy and creativity.
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The Relationship between Learners’ Autonomy and Creativity

The Relationship between Learners’ Autonomy and Creativity

This study aims to investigate the relationship between learner’s autonomy and creativity. Since many studies had been mainly studied autonomy and creativity independently of each other, this study attempt to find whether creativity and autonomy are interrelated. To this end, 100 EFL learners were chosen to participate in the study. To collect data, Self-Directed Learning Readiness Scale (SDLRS) questionnaire to measure their autonomy, and Abedi’s creativity test to measure their creativity were used. The Oxford Quick Placement Test was also administrated to measure the participants’ language proficiency level. To see the relation between autonomy and creativity, the correlation coefficient was applied. The result of the correlation coefficient revealed that there is a relationship between autonomy, creativity. Keywords: creativity, autonomy, language, language proficiency, self-directed Learning
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Are the robots coming? Designing with autonomy & control for musical creativity & performance

Are the robots coming? Designing with autonomy & control for musical creativity & performance

So what do such studies really tell us about the world of autonomy, control and creativity? What these studies tell us is that control and autonomy are situated; this means that they are features of the setting bounded by context [9] and as such are not necessarily uniformly applicable in terms of having an innate ability to be used in a descriptive manner. This in itself is important for the designers of systems, as it means that there are more indicative and personal ways to reference the role and application of autonomy and control as part of a system. This means that more work is needed in order to tease out and to unpack further related issues. Finding out about the practices relating to composition and performance have shown that that not all compositional and performance-based practices are the same. However, being able to understand the workflows, the tools, channels and services that are implicated in this process, whether people use Ableton Live or ‘no-input’ mixing is key to helping us understand more. There are salient features that relate to these, and the skillful practices that people engage in are evident in the music performance/creation process, these can be documented and exist as carefully thought out strategies that are not random, but can take account of randomness as a compositional tool as we have found when carrying out our investigations.
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Beyond swarm intelligence: The Ultraswarm

Beyond swarm intelligence: The Ultraswarm

seemed impossible to achieve in practice because of their notorious instability. However, in the last year or so, two possible solutions have appeared: small (but expensive) gyro-stabilised indoor helicopters [12], and intrinsically stable co-axial helicopters [13]. The latter, currently marketed as remotely controlled toys, are very attractive indeed, and in fact have many of the key advantages of the elements of swarm systems: because they are simple, they are cheap and light - and because they are light, they are extremely robust, and can be crashed with impunity. Their stability is such that, when properly trimmed, they can be flown to a given position and left there to hover hands-off for a time with no adverse effects other than a very slight drift. They are also stable in forward flight, and so autonomy may be achievable with relatively few problems. Although the payload of these small machines is limited, recent developments in electronics make it feasible for them to carry enough computation and communication to serve as a testbed for the major elements of the UltraSwarm concept. Figure 1 shows a modified Proxflyer design, the Bladerunner, fitted with a miniature wireless color video camera.
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Study on Swarm Intelligence on Medical Field

Study on Swarm Intelligence on Medical Field

model small networks. This model yields good results in solving different evolutionary computation techniques. Yet, there are no models that attain a perfect reconstruction of the network. For optimizing this model, variation of particle swarm optimization (PSO), called dissipative PSO (DPSO), is used. Comparison between the use of an L1 regularizer and other evolutionary computing approaches were also made. To the best of this paper’s knowledge, neither the DPSO nor L1 optimizer has been jointly used to solve the S-System. The grouping of S-System and DPSO offers more advantages than preceding methods, and presents best results for inferencing larger and more complex networks.
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Swarm Intelligence for Routing in Communication Networks

Swarm Intelligence for Routing in Communication Networks

Swarm intelligence routing provides a promising alternative to these approaches. Swarm intelligence utilizes mobile software agents for network management. These agents are autonomous entities, both proactive and reactive, and have the capability to adapt, cooperate and move intelligently from one location to the other in the communication network [4]. Swarm intelligence, in particular, uses stigmergy (i.e. communication through the environment) for agent interaction [5,6,7,9]. Swarm intelligence exhibits emergent behavior wherein simple interactions of autonomous agents, with simple primitives, give rise to a complex behavior that has not been specified explicitly [8].
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Artificial Swarm Intelligence and Cooperative Robotic Systems

Artificial Swarm Intelligence and Cooperative Robotic Systems

In this work, our idea is to integrate this biologically inspired model of adaptive learning into a field of artificial intelligence in order to achieve our goal - to develop an intelligent distributed swarm system that can maintain its sensory states within the physical bounds of its components in face of constant environmental flux. In short terms, this means avoiding sensory surprise, which relates not only to the current state of the swarm system (which cannot be changed), but also to the transition from one state to another (which can be changed). The guiding principle for the mentioned transitions between states, which is compatible with the swarm survival (e.g. the flight of a swarm of drones within a small margin of error), can be considered as a global random attractor [10]. In the learning process we will be optimizing the movements of this attractor by minimizing free-energy bound on the sensory surprise of the swarm.
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Swarm Intelligence Optimization: Editorial Survey

Swarm Intelligence Optimization: Editorial Survey

Abstract— This paper surveys the intersection of two fascinating and increasingly popular domains: swarm intelligence and optimization. Whereas optimization has been popular academic topic for decades, swarm intelligence is relatively new subfield of artificial intelligence which studies the emergent collective intelligence of groups of simple agents. It is based on social behavior that can be observed in nature, such as ant colonies, flock of birds, fish schools and bee hives, where a number of individuals with limited capabilities are able to come to intelligent solutions for complex problems. In recent years the swarm intelligence paradigm has received widespread attention in research, mainly as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Artificial Bee Colony Optimization (ABC).These are the most popular swarm intelligence metaheuristics for Single Objective and Multi Objective Problems. This paper presents a comprehensive review of the various proposals on PSOs and ABCc for single and multi-objective optimization problems as reported in the specialized literature. As part of this review, we have attempted to identify the main features of each proposal. We have also discussed some of the key issues and sub-issues pertaining to PSO and ABC. In the last part of the paper, we have enlisted some of the topics within this field that we consider to be promising areas of future research.
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Swarm Intelligence in Power System Planning

Swarm Intelligence in Power System Planning

Swarm intelligence is an artificial intelligence technique involving the study of collective behavior in decentral- ized system, which is made up by populations of simple individuals interacting locally with each other and with external environment [5-8]. Several examples of these systems can be found in the nature, for example, colonies of ants, flocks of birds, schools of fish, groups of bees, packs of wolves, and so on. An interesting phenomenon of swarms is that collective swarm behavior can emerge on a global scale even when all individuals have only a restricted view of the system and interactions between individuals and their environment occur only on a local scale [9]. Owning to these outstanding characteristics, the principles of swarm behavior have been studied exten- sively and been widely applied into many fields. Com- putational swarm intelligence is the algorithmic models that imitate the principles of large groups of simple swarm individuals working together to achieve a goal through self-learning, self-adjusting, and mutual coop- eration manners. These algorithms have shown to be able to adapt well in changing environments, and are im- mensely flexible and robust [8,10]. Two of the computa- tional swarm intelligence techniques are ant colony op- timization (ACO) [11] and particle swarm optimization (PSO) [6]. In the next section, these two swarm intelli- gence algorithms will be discussed in detail.
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STIMULATION, CUM CREATIVITY AND ARTIFICIAL INTELLIGENCE

STIMULATION, CUM CREATIVITY AND ARTIFICIAL INTELLIGENCE

of creativity, but is especially problematic with respect to the third (see Section 4, below). Combinational creativity is studied in AI by research on (for instance) jokes and analogy. Both of these require some sort of semantic network, or inter-linked knowledge-base, as their ground. Clearly, pulling random associations out of such a source is simple. But an association may not be telling, or appropriate in context. For all combinational tasks other than “free association”, the nature and structure of the associative linkage is important too. Ideally, every product of the combinational program should be at least minimally apt, and the originality of the various combinations should be assessable by the AI-system. A recent, and relatively successful, example of AI-generated (combinational) humour is Jape, a program for producing punning riddles Jape produces jokes based on nine general sentence-forms, such as: What do you get when you cross X with Y? What kind of X has Y? What kind of X Can Y? What’s the difference between an X and a Y? The semantic network used by the program incorporates knowledge of phonology, semantics, syntax, and spelling. Different combinations of these aspects of words are used. in distinctly structured ways, for generating each joke-type.
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Emotional Intelligence and Creativity in Teacher Education

Emotional Intelligence and Creativity in Teacher Education

EI skills are a key factors in the appearance of disruptive behaviours based on an emotional deficit. It is logical to expect that students with low levels of EI show greater levels of impulsiveness and poorer interpersonal and social skills, all of which encourage the development of various antisocial behaviours (Petrides et al., 2004). Some researchers suggest that people with lower emotional intelligence are more involved in self-destructive behaviour such as tobacco consumption (Brackett, Mayer & Warner, 2004; Chou & Johnson, 2005). Adolescents with a greater ability to manage their emotions are more able to cope with them in their daily life, facilitating better psychological adjustment, and so they present less risk for substance abuse. Specifically, adolescents with a wider repertoire of affective competencies based on the understanding, management and regulation of their own emotions do not require other types of external regulators (e.g. tobacco, alcohol and illegal drugs) in order to recover from negative states of mind provoked by the wide range of stressful life events which they are exposed to at this age (Ruiz-Aranda, Fernandez-Berrocal, Cabello & Extremera, 2006).
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Swarm Intelligence Techniques focusing Particle Swarm Optimization (PSO)

Swarm Intelligence Techniques focusing Particle Swarm Optimization (PSO)

continuous non-linear functions. The Particle Swarm Optimization algorithm is similar to many population based algorithms such as Genetic Algorithm but they don’t have any direct re-combination of individuals of the population. It has become popular due to its simplicity and effectiveness in wide range of applications along with its low computational costs. Like all other evolutionary algorithms, Particle Swarm Optimization (PSO) is appropriate for the problems with immense search spaces that present many local minima. [3]

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Emotional intelligence and creativity in teacher education

Emotional intelligence and creativity in teacher education

It has been often observed by the author, with deep regret, that educability of the intelligence is often prevented. The idea of once a dunce, always a dunce seems to go unchallenged by teachers; these teachers lose interest in students who lack intelligence-they show them neither sympathy nor respect, using such unmeasured language in front of the children that they say things like: “This boy will never be good for anything… he has no gifting, no intelligence”. Many times, the author has heard such careless words. They are repeated daily in primary schools and also in secondary. The author remembers that during his Baccalaureate exam in Letters, Martha the examiner became indignant over one of his answers (he confused the name of a Greek philosophy with one of the character names from La Bruyere). She declared that he would never have the philosophic spirit. “Never!” What a daring word! Some recent philosophers seem to give moral support to such deplorable verdicts, affirming that an individual’s intelligence is a fixed quantity, a quantity that cannot increase. We must protest and counteract this brutal pessimism; let us demonstrate that it has no basis whatsoever” (Alfred, 1909). A century after these thoughts from Alfred (1990), we continue to be concerned about how to get pupils to improve both their intellectual
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Swarm intelligence   James Kennedy pdf

Swarm intelligence James Kennedy pdf

Fukuda’s lab is developing robot swarms. Where Brooks’ subsump- tion methodology took the cognitive executive control out of the pic- ture, decentralizing the individual robot’s behavior, Fukuda’s robots re- place the individual’s control with reflex and reactive influence of one robot by another. These decentralized systems have the advantages that the task load can be distributed among a number of workers, the design of each individual can be much simpler than the design of a fully auton- omous robot, and the processor required—and the code that runs on it— can be small and inexpensive. Further, individuals are exchangeable. We think back to 1994 when the NASA robot named Dante II ambled several hundred feet down into the inferno of Mt. Spurr, a violent Alaskan vol- cano, to measure gases that were being released from the bowels of the earth. The 1,700-pound Dante II toppled into those self-same bowels and would now be just an expensive piece of molten litter if helicopters bear- ing earthlings had not saved it—for instance, if the accident had hap- pened on another planet, which was what Dante was being developed for. If, instead, a swarm of cheap robots had been sent into the crater, per- haps a few would have been lost, but the mission could have continued and succeeded. (Dante II was named after Dante I, whose tether snapped only 21 feet down into Mount Erebus in 1993—another potentially ex- pensive loss.)
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Clustering analysis using Swarm Intelligence

Clustering analysis using Swarm Intelligence

We applied the locus-based adjacency genetic scheme proposed in Park and Song (1998) to construct particles in a swarm. In this graph-based scheme, shown in Figure 2, each particle is presented as a vector consisting of 𝑁 elements where 𝑁 is the number of data points. These elements can take values in the range {1, 2, ..., 𝐾}, where 𝐾 is the number of clusters. Let 𝑎 be the value in the connections vector that is assigned to the data point 𝑏. This assignment is interpreted as a link between data points 𝑎 and 𝑏 which means they belong to the same cluster in the clustering solution. All connected data points are then placed inside the same cluster and are assigned to their cluster number in the particle vector (Figure 4.2). The main advantage of using this scheme is that the number of clusters can be determined automatically for each particle. This particle is indeed representative of a candidate clustering solution. Therefore, it is possible for the algorithm to compare particles as clustering solutions with different number of clusters and lead them toward global optimum in just one run.
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Deploying swarm intelligence in medical imaging

Deploying swarm intelligence in medical imaging

In the test phase, SDS checks whether the agent hypothesis is successful or not by performing a hypothesis evaluation which returns a boolean value. Later in the iteration, contingent on the precise recruitment strategy employed (in the diffusion phase), successful hypotheses diffuse across the population and in this way information on potentially good solutions spreads throughout the entire population of agents. In other words, each agent recruits another agent for interaction and potential communication of hypothesis. This algorithm has been used alongside other swarm intelligence algorithms in several research topics including numerical optimisation and clustering.
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Swarm intelligence in evacuation problems: A review

Swarm intelligence in evacuation problems: A review

Swarm intelligence takes inspiration from the social behaviors of insects and other animals [13].The first studies regarding swarm intelligence date back to early nineties: it’s a relatively new approach to problem solving. The most rel- evant algorithms based on swarm intelligence concept are ACO algorithm (Ant Colony Optimization) and PSO algorithm (Particle Swarm Optimization) [14, 15]. ACO algorithm takes inspiration from the behavior of ant when searching food. More specifically on ants’ ability to find always the shortest path between their nest and food sources [16, 17]. PSO is a population-based stochastic ap- proach and it uses swarm intelligence to solve continuous and discrete problems [18]. The PSO is inspired by the natural behavior of fish schools and birds flocks [19]. Their original idea was to simulate the social behavior of a bird flock trying to reach an unknown destination [4, 20]. Due to their flexibility, PSO algorithms were developed as interesting candidates to address complex problems such as the optimization of multi modal functions in various areas of interest. It’s fun- damental to observe that the modeling of evacuation becomes more complicated when considering some aspects of human behavior, such as the queuing behavior, self-organization, crowd psychology and sub-group phenomena [21]. The under- standing of occupants’ responses during an evacuation is crucial because our first goal is to determine the optimum evacuation route toward safe areas [19]. The algorithm of standard PSO is described by the following equations:
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Swarm Intelligence in Big Data Analytics

Swarm Intelligence in Big Data Analytics

Generally, there are two kinds of approaches that apply swarm intelligence as data mining techniques [27]. The first category consists of techniques where indi- viduals of a swarm move through a solution space and search for solution(s) for the data mining task. This is a search approach; the swarm intelligence is applied to optimize the data mining technique, e.g., the parameter tuning. In the second category, swarms move data instances that are placed on a low-dimensional fea- ture space in order to come to a suitable clustering or low-dimensional mapping solution of the data. This is a data organizing approach; the swarm intelligence is directly applied to the data samples, e.g., dimensionality reduction of the data. Swarm intelligence, especially particle swarm optimization or ant colony op- timization algorithms, is utilized in data mining to solve single objective [1] and multi-objective problems [9]. Based on the two characters of particle swarm, the self-cognitive and social learning, the particle swarm has been utilized in data clustering techniques [10], document clustering, variable weighting in clustering high-dimensional data [25], semi-supervised learning based text categorization, and the Web data mining [28].
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Trusted Autonomy: Autonomy and Autonomous Systems at NASA LaRC's Autonomy Incubator

Trusted Autonomy: Autonomy and Autonomous Systems at NASA LaRC's Autonomy Incubator

safe reliable mobility and manipulation in dynamic, unstructured, and data- deprived environments.[r]

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