Exploring e-Learning Knowledge Through Ontological Memetic Agents

Acampora, Giovanni; Loia, Vincenzo; Gaeta, Matteo

doi:10.1109/mci.2010.936306

Cited by 52 publications

(28 citation statements)

References 18 publications

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“…At the second phase (careful search), meme activation, meme competition and meme matching are used to find the best matching meme from the best and smallest active subclass (lines 7-9). At the third phase (memotype update), the memetic agent updates its structured memes based on the matching result (lines [10][11][12][13][14][15][16]. If the current vector }S, A, R| matches an existing meme successfully, memotype update is done for the best matching meme.…”

Section: A Meme Assimilation With Structured Memesmentioning

confidence: 99%

“…Nguyen et al [9] study the notion of "Universal Darwinism" and social memetics in search, and investigate on the transmission of memetic material via nongenetic means. Meuth et al [10] show the potential of meme learning and high-order memes for more efficient problem solving while Acampora et al [11] introduce memetic agents as intelligent explorers to create "in time" and personalized experiences for e-Learning. In contrast to memetic algorithms, less study on other manifestations of memes for effective problem solving has been explored, making it a fertile area for further research investigation.…”

Section: Introductionmentioning

confidence: 99%

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Structured Memetic Automation for Online Human-Like Social Behavior Learning

Zeng

Chen

Ong

et al. 2017

IEEE Trans. Evol. Computat.

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Section: A Meme Assimilation With Structured Memesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Structured Memetic Automation for Online Human-Like Social Behavior Learning

Zeng

Chen

Ong

et al. 2017

IEEE Trans. Evol. Computat.

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“…Lees et al [30] addressed a central issue for high level architecture (HLA)-based games to develop the HLA-compliant game agents. Acampora, Gaeta, and Loia [33] proposed an exploring e-learning knowledge through ontological memetic agents. According to [27], 1) An agent physically distributes required knowledge to several locations; 2) An agent can model autonomous entities; 3) Agent-based systems can employ security mechanisms; 4) Agent technology has the ability to communicate and coordinate; 5) Agents can automatically discover and compose e-services; 6) Intelligent agents have…”

Section: Introducing Learning Into Mcts Agentsmentioning

confidence: 99%

Intelligent Agents for the Game of Go

Hoock

Lee

Rimmel

et al. 2010

IEEE Comput. Intell. Mag.

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Monte-Carlo Tree Search (MCTS) is a very efficient recent technology for games and planning, particularly in the high-dimensional case, when the number of time steps is moderate and when there is no natural evaluation function. Surprisingly, MCTS makes very little use of learning. In this paper, we present four techniques (ontologies, Bernstein races, Contextual Monte-Carlo and poolRave) for learning agents in Monte-Carlo Tree Search, and experiment them in difficult games and in particular, the game of Go.

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“…Practical experiences suggest that they reach stagnation after certain number of generations as the population is not converged locally, so they will stop proceeding towards global optimal solutions. The stochastic search methods are proven in reaching global solutions for certain difficult real world optimization problems [18]. Hence this article comes up with a hybrid approach involving PSO-DE and BFOA algorithm for solving non-convex DED problem considering valve-point loading effects, ramp-rate limits, prohibited operating regions and spinning reserve capacity.…”

mentioning

confidence: 99%

Optimal Design of PID Controller for a CSTR System Using BF-PSO

Katyayan¹

2018

IJRASET

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Social foraging behavior of a Escherichia-colibacteria has been explored to develop a novel algorithm for distributed optimization and control. Recently hybrid approach developed, involving PSO and BFOA (bacterial foraging optimization algorithm) algorithm for optimizing multi-model and high dimensional function. This paper presents the optimal design of PID controller based on a Bacterial foraging particle swarm optimization (BF-PSO) approach for continuous stirred tank reactor (CSTR). The mathematical model of experimental system had been approximated near the operating point for the PSO algorithm to adjust PID parameters for the objective function. The results show the adjustment of PID parameters converting into the optimal point. The good control response is obtained based on the optimal values by the BF-PSO technique. Index Terms: PSO (Particle swarm optimization),BF-PSO, optimal control, simulation. I. INTRODUCTIONThe process control techniques in the industry have made great advances during the past decades. A no of control methods such as adaptive control, neural control, and fuzzy control have been studied. Among them, the best known is the proportional-integralderivative (PID) controller, which has been widely used in the industry because of its simple structure and robust performance in a wide range of operating conditions. Unfortunately, it has been quite difficult to tune properly the gains of PID controllers because many industrial plants are often burdened with problems such as high order, time delays, and nonlinearities. It is hard to determine optimal or near optimal PID parameters with the classic tuning method (Ziegler-Nichol's method for instance). For these reasons, it is highly desirable to increase the capabilities of PID controllers by adding new features. Many artificial intelligence (AI) techniques have been employed to improve the controller performances for a wide range of plants while retaining their basic characteristics. AI techniques such as neural network, fuzzy system, and neural-fuzzy logic have been widely applied to proper tuning of PID controller parameters. Particle swarm optimization (PSO) [1], first introduced by Kennedy and Eberhart, is one of the modern heuristic algorithms. It was developed through simulation of a simplified social system, and has been found to be robust in solving continuous nonlinear optimization problems. The PSO technique can generate a high-quality solution within shorter calculation time and stable convergence characteristic than other stochastic methods. PSO method is an excellent optimization methodology and a promising approach for solving the optimal PID controller parameters. Therefore, this study develops the PSO-PID controller to search optimal PID parameters. This controller is called the PSO-PID controller. In this paper, we propose a particle swarm optimization approach for optimal design of PID controller for continuous stirred tank reactor. The Bacterial Foraging Optimization Algorithm (BFOA) is currently gaining popularity...

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Exploring e-Learning Knowledge Through Ontological Memetic Agents

Cited by 52 publications

References 18 publications

Structured Memetic Automation for Online Human-Like Social Behavior Learning

Structured Memetic Automation for Online Human-Like Social Behavior Learning

Intelligent Agents for the Game of Go

Optimal Design of PID Controller for a CSTR System Using BF-PSO

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