scholarly journals Intelligent System Design Using Hyper-Heuristics

2015 ◽  
Vol 56 ◽  
Author(s):  
Nelishia Pillay
Keyword(s):  
Artificial Intelligence ◽  
Evolutionary Algorithm ◽  
Intelligent System ◽  
Initial Step ◽  
Hill Climbing ◽  
Initial Population ◽  
A Algorithm ◽  
Towers Of Hanoi ◽  
Problem Instances ◽  
Blocks World

Determining the most appropriate search method or artificial intelligence technique to solve a problem is not always evident and usually requires implementation of the different approaches to ascertain this. In some instances a single approach may not be sufficient and hybridization of methods may be needed to find a solution. This process can be time consuming. The paper proposes the use of hyper-heuristics as a means of identifying which method or combination of approaches is needed to solve a problem. The research presented forms part of a larger initiative aimed at using hyper-heuristics to develop intelligent hybrid systems. As an initial step in this direction, this paper investigates this for classical artificial intelligence uninformed and informed search methods, namely depth first search, breadth first search, best first search, hill-climbing and the A* algorithm. The hyper-heuristic determines the search or combination of searches to use to solve the problem. An evolutionary algorithm hyper-heuristic is implemented for this purpose and its performance is evaluated in solving the 8-Puzzle, Towers of Hanoi and Blocks World problems. The hyper-heuristic employs a generational evolutionary algorithm which iteratively refines an initial population using tournament selection to select parents, which the mutation and crossover operators are applied to for regeneration. The hyper-heuristic was able to identify a search or combination of searches to produce solutions for the twenty 8-Puzzle, five Towers of Hanoi and five Blocks World problems. Furthermore, admissible solutions were produced for all problem instances.

Artificial intelligence as a moving force of improvement and innovative development in education and pedagogic

2019 ◽  
pp. 21-30
Author(s):  
M. G. Koliada ◽  
T. I. Bugayova
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Interaction Analysis ◽  
Educational Data Mining ◽  
Main Task ◽  
Intellectual System ◽  
Will Force ◽  
To Come ◽  
Artificial Intelligence Systems

The article discusses the history of the development of the problem of using artificial intelligence systems in education and pedagogic. Two directions of its development are shown: “Computational Pedagogic” and “Educational Data Mining”, in which poorly studied aspects of the internal mechanisms of functioning of artificial intelligence systems in this field of activity are revealed. The main task is a problem of interface of a kernel of the system with blocks of pedagogical and thematic databases, as well as with the blocks of pedagogical diagnostics of a student and a teacher. The role of the pedagogical diagnosis as evident reflection of the complex influence of factors and reasons is shown. It provides the intelligent system with operative and reliable information on how various reasons intertwine in the interaction, which of them are dangerous at present, where recession of characteristics of efficiency is planned. All components of the teaching and educational system are subject to diagnosis; without it, it is impossible to own any pedagogical situation optimum. The means in obtaining information about students, as well as the “mechanisms” of work of intelligent systems based on innovative ideas of advanced pedagogical experience in diagnostics of the professionalism of a teacher, are considered. Ways of realization of skill of the teacher on the basis of the ideas developed by the American scientists are shown. Among them, the approaches of researchers D. Rajonz and U. Bronfenbrenner who put at the forefront the teacher’s attitude towards students, their views, intellectual and emotional characteristics are allocated. An assessment of the teacher’s work according to N. Flanders’s system, in the form of the so-called “The Interaction Analysis”, through the mechanism of fixing such elements as: the verbal behavior of the teacher, events at the lesson and their sequence is also proposed. A system for assessing the professionalism of a teacher according to B. O. Smith and M. O. Meux is examined — through the study of the logic of teaching, using logical operations at the lesson. Samples of forms of external communication of the intellectual system with the learning environment are given. It is indicated that the conclusion of the found productive solutions can have the most acceptable and comfortable form both for students and for the teacher in the form of three approaches. The first shows that artificial intelligence in this area can be represented in the form of robotized being in the shape of a person; the second indicates that it is enough to confine oneself only to specially organized input-output systems for targeted transmission of effective methodological recommendations and instructions to both students and teachers; the third demonstrates that life will force one to come up with completely new hybrid forms of interaction between both sides in the form of interactive educational environments, to some extent resembling the educational spaces of virtual reality.

scholarly journals Supervised committee machine with artificial intelligence for prediction of fluoride concentration

2013 ◽  
Vol 15 (4) ◽  
pp. 1474-1490 ◽  
Author(s):  
Ata Allah Nadiri ◽  
Elham Fijani ◽  
Frank T.-C. Tsai ◽  
Asghar Asghari Moghaddam
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Ground Water ◽  
Intelligent System ◽  
Fluoride Concentration ◽  
Mixing Zone ◽  
Ann Model ◽  
Committee Machine ◽  
Hydrogeological Characteristics ◽  
Model Predictions

The study introduces a supervised committee machine with artificial intelligence (SCMAI) method to predict fluoride in ground water of Maku, Iran. Ground water is the main source of drinking water for the area. Management of fluoride anomaly needs better prediction of fluoride concentration. However, the complex hydrogeological characteristics cause difficulties to accurately predict fluoride concentration in basaltic formation, non-basaltic formation, and mixing zone. SCMAI predicts fluoride by a nonlinear combination of individual AI models through an artificial intelligent system. Factor analysis is used to identify effective fluoride-correlated hydrochemical parameters as input to AI models. Four AI models, Sugeno fuzzy logic, Mamdani fuzzy logic, artificial neural network (ANN), and neuro-fuzzy are employed to predict fluoride concentration. The results show that all of these models have similar fitting to the fluoride data in the Maku area, and do not predict well for samples in the mixing zone. The SCMAI employs an ANN model to re-predict the fluoride concentration based on the four AI model predictions. The result shows improvement to the CMAI method, a committee machine with the linear combination of AI model predictions. The results also show significant fitting improvement to individual AI models, especially for fluoride prediction in the mixing zone.

HYBRIDIZATION OF MULTIOBJECTIVE EVOLUTIONARY ALGORITHM WITH COEVOLUTION FOR ENEMY TEAM IN MS. PAC-MAN GAME

2014 ◽  
Vol 2 (1) ◽  
pp. 36-51
Author(s):  
Tse guan Tan ◽  
Jason Teo ◽  
On Chin Kim
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Technique ◽  
Dynamic Game ◽  
Evolution Strategy ◽  
Evolutionary Multiobjective Optimization ◽  
Competitive Coevolution ◽  
Virtual Creatures

AbstrakKini, semakin ramai penyelidik telah menunjukkan minat mengkaji permainan Kecerdasan Buatan (KB).Permainan seumpama ini menyediakan tapak uji yang sangat berguna dan baik untuk mengkaji asasdan teknik-teknik KB. Teknik KB, seperti pembelajaran, pencarian dan perencanaan digunakan untukmenghasilkan agen maya yang mampu berfikir dan bertindak sewajarnya dalam persekitaran permainanyang kompleks dan dinamik. Dalam kajian ini, satu set pengawal permainan autonomi untuk pasukan hantudalam permainan Ms. Pac-man yang dicipta dengan menggunakan penghibridan Evolusi PengoptimumanMultiobjektif (EPM) dan ko-evolusi persaingan untuk menyelesaikan masalah pengoptimuman dua objektifiaitu meminimumkan mata dalam permainan dan bilangan neuron tersembunyi di dalam rangkaianneural buatan secara serentak. Arkib Pareto Evolusi Strategi (APES) digunakan, teknik pengoptimumanmultiobjektif ini telah dibuktikan secara saintifik antara yang efektif di dalam pelbagai aplikasi. Secarakeseluruhannya, keputusan eksperimen menunjukkan bahawa teknik pengoptimuman multiobjektif bolehmendapat manfaat daripada aplikasi ko-evolusi persaingan Abstract Recently, researchers have shown an increased interest in game Artificial Intelligence (AI). Gamesprovide a very useful and excellent testbed for fundamental AI research. The AI techniques, such aslearning, searching and planning are applied to generate the virtual creatures that are able to think andact appropriately in the complex and dynamic game environments. In this study, a set of autonomousgame controllers for the ghost team in the Ms. Pac-man game are created by using the hybridizationof Evolutionary Multiobjective Optimization (EMO) and competitive coevolution to solve the bi-objectiveoptimization problem of minimizing the game's score by eating Ms. Pac-man agent and the number ofhidden neurons in neural network simultaneously. The Pareto Archived Evolution Strategy (PAES) is usedthat has been proved to be an effective and efficient multiobjective optimization technique in variousapplications. Overall, the results show that multiobjective optimizer can benefit from the application ofcompetitive coevolutionary

Determination of Factors Affecting Safety Stress in Machine Design by Using Artificial Intelligence Technologies

2008 ◽  
Vol 144 ◽  
pp. 232-237
Author(s):  
Durmus Karayel ◽  
Sinan Serdar Ozkan ◽  
Fahri Vatansever
Keyword(s):  
Artificial Intelligence ◽  
Information Technologies ◽  
Intelligent System ◽  
System Model ◽  
Multi Agent System ◽  
Safety Factors ◽  
Agent System ◽  
Factors Affecting ◽  
Classical Design ◽  
Multi Agent

In this study, an intelligent system model that can evaluate experimental material properties and safety factors is developed. The model contains Artificial Intelligence Technologies such as Artificial Neural Network (ANN) and Fuzzy Logic. It consists of sub modules into interaction. Also, the model can obtain more precision values than interpolation techniques used to classical design. The study contributes to define safety factors, design criterions and safety stress according to a new approach based on information technologies. So, this study can be seen as one of the sub modules of Intelligence Multi Agent System and it can be integrated with Multi Agent System Model for design. Also, it can be used for classical design studies so that results can be quickly obtained. It is expected that this approach will be widely used by designers.

Prediction of the fatigue strength of metals at low temperatures based on artificial intelligence

2021 ◽  
pp. 11-14
Author(s):  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Fatigue Strength ◽  
Low Temperatures ◽  
Intelligent System ◽  
Fatigue Cracks ◽  
Test Conditions ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Trained Neural Network

An intelligent system for predicting the fatigue strength of metals in a wide temperature range is developed using a specially trained neural network. The system makes it possible to predict the number of load cycles of a part to failure, as well as the start of formation and growth rate of fatigue cracks for different test conditions, including at low temperatures. Keywords: neural network, prediction of loading cycles, low temperatures, fatigue strength. [email protected]

An Evolutionary Algorithm for Graph Drawing with a Multiobjective Approach

2012 ◽  
pp. 113-140 ◽  
Author(s):  
Sergio Enríquez Aranda ◽  
Eunice E. Ponce de León Sentí ◽  
Elva Díaz Díaz ◽  
Alejandro Padilla Díaz ◽  
María Dolores Torres Soto ◽  
...  
Keyword(s):  
Evolutionary Algorithm ◽  
Hybrid Algorithm ◽  
Quality Index ◽  
Pareto Front ◽  
Graph Drawing ◽  
Search Algorithm ◽  
Hill Climbing ◽  
Edge Crossing ◽  
Multiobjective Approach ◽  
Feasible Solutions

In this chapter a hybrid algorithm is constructed, implemented and tested for the optimization of graph drawing employing a multiobjective approach. The multiobjective optimization problem for graph drawing consists of three objective functions: minimizing the number of edge crossing, minimizing the graph area, and minimizing the aspect ratio. The population of feasible solutions is generated using a hybrid algorithm and at each step a Pareto front is calculated. This hybrid algorithm combines a global search algorithm (EDA — Estimation of Distribution Algorithm) with a local search Algorithm (HC — Hill Climbing) in order to maintain a balance between the exploration and exploitation. Experiments were performed employing planar and non-planar graphs. A quality index of the obtained solutions by the hybrid MOEA-HCEDA (Multiobjective Evolutionary Algorithm - Hill Climbing & Univariate Marginal Distribution Algorithm) is constructed based on the Pareto front defined in this chapter. A factorial experiment using the algorithm parameters was performed. The factors are number of generations and population size, and the result is the quality index. The best combination of factors levels is obtained.

Artificial Intelligence Review

2019 ◽  
pp. 23-39
Author(s):  
Amal Kilani ◽  
Ahmed Ben Hamida ◽  
Habib Hamam
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Intelligent System ◽  
Route Planning ◽  
Turing Test ◽  
Future Research ◽  
Interaction Data ◽  
Research Directions ◽  
Future Research Directions

In this chapter, the authors present a profound literature review of artificial intelligence (AI). After defining it, they briefly cover its history and enumerate its principal fields of application. They name, for example, information system, commerce, image processing, human-computer interaction, data compression, robotics, route planning, etc. Moreover, the test that defines an artificially intelligent system, called the Turing test, is also defined and detailed. Afterwards, the authors describe some AI tools such as fuzzy logic, genetic algorithms, and swarm intelligence. Special attention will be given to neural networks and fuzzy logic. The authors also present the future research directions and ethics.

Combining Artificial Intelligence and NetMedicine for Ambient Assisted Living

2020 ◽  
pp. 1652-1666
Author(s):  
Paolo Sernani ◽  
Andrea Claudi ◽  
Aldo Franco Dragoni
Keyword(s):  
Artificial Intelligence ◽  
Assisted Living ◽  
Local Governments ◽  
Intelligent System ◽  
Ambient Assisted Living ◽  
World Population ◽  
Distributed Sensor ◽  
Distributed Sensor Network ◽  
Multi Agent ◽  
Analyse Data

World population is shifting towards older ages: according to recent estimates there will be 1.5 billion people over 65 years old in 2050. Local governments, international institutions, care organizations and industry are fostering the research community to find solutions to face the unprecedented challenges raised by population ageing. A combination of Artificial Intelligence and NetMedicine could be ideal to face these challenges: they provide the means to develop an intelligent system and simultaneously to distribute it over a network, allowing the communication over the internet, if needed. Hence, the authors present a Multi-Agent Architecture for Ambient Assisted Living (AAL): it is the model for a system to manage a distributed sensor network composed by ambient and biometric sensors. The system should analyse data and pro-actively decide to trigger alarms if anomalies are detected. The authors tested the architecture implementing a prototypical Multi-Agent System (MAS), based on Belief-Desire-Intention (BDI) paradigm: the Virtual Carer.

Combining Artificial Intelligence and NetMedicine for Ambient Assisted Living

2015 ◽  
Vol 6 (4) ◽  
pp. 62-76 ◽  
Author(s):  
Paolo Sernani ◽  
Andrea Claudi ◽  
Aldo Franco Dragoni
Keyword(s):  
Artificial Intelligence ◽  
Assisted Living ◽  
Local Governments ◽  
Intelligent System ◽  
Ambient Assisted Living ◽  
World Population ◽  
Distributed Sensor ◽  
Distributed Sensor Network ◽  
Multi Agent ◽  
Analyse Data

World population is shifting towards older ages: according to recent estimates there will be 1.5 billion people over 65 years old in 2050. Local governments, international institutions, care organizations and industry are fostering the research community to find solutions to face the unprecedented challenges raised by population ageing. A combination of Artificial Intelligence and NetMedicine could be ideal to face these challenges: they provide the means to develop an intelligent system and simultaneously to distribute it over a network, allowing the communication over the internet, if needed. Hence, the authors present a Multi-Agent Architecture for Ambient Assisted Living (AAL): it is the model for a system to manage a distributed sensor network composed by ambient and biometric sensors. The system should analyse data and pro-actively decide to trigger alarms if anomalies are detected. The authors tested the architecture implementing a prototypical Multi-Agent System (MAS), based on Belief-Desire-Intention (BDI) paradigm: the Virtual Carer.

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