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An Intelligent Agent Model and a Simulation for a Given Task in a Specific Environment
Safa'a Yousef Abu Hadba,
Ibtehal Nafea
Issue:
Volume 5, Issue 1, June 2021
Pages:
1-16
Received:
11 November 2020
Accepted:
28 January 2021
Published:
10 February 2021
Abstract: This paper introduces An Intelligent Agent Model for a Given Task in a Specified Environment. The methodology adopted in this work is based on mixing computational methods and functions to build an intelligent agent model. This paper focuses on building an intelligent agent model as a knowledge-based system that interacts with a dynamic environment for performing tasks. The class structure used to represent the environment in the knowledge base relies on three types of knowledge representation forms: production rule, semantic net, and frames. Each object in the environment is an instance of the class environment. Algorithms and functions are used to get knowledge from the state space of an environment to construct a task. The intelligent agent model can understand the environment from any position and can detect many subtasks, arrange them in a queue for execution, and can make decisions at a high scale of thinking. This model is proposed to maintain that an agent which is characterized by sufficiently low computational costs can interact with the environment in real-time but is powerful enough to reach the assigned goals in complex environments and within an acceptable time period. The intelligent agent model can calculate persistent changes in an external dynamic environment and any unexpected change, for example detecting the being of any problem in the environment and avoiding it. The intelligent agent can also learn and take reasonable decisions in the dynamic environment and automatically select an action based on task features. Thus, the intelligent agent can resolve several different kinds of difficulties.
Abstract: This paper introduces An Intelligent Agent Model for a Given Task in a Specified Environment. The methodology adopted in this work is based on mixing computational methods and functions to build an intelligent agent model. This paper focuses on building an intelligent agent model as a knowledge-based system that interacts with a dynamic environment...
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The Role of Artificial Intelligence (AI) in Condition Monitoring and Diagnostic Engineering Management (COMADEM): A Literature Survey
Issue:
Volume 5, Issue 1, June 2021
Pages:
17-37
Received:
27 March 2021
Accepted:
9 April 2021
Published:
30 April 2021
Abstract: Artificial Intelligence (AI) is playing a dominant role in the 21st century. Organizations have more data than ever, so it’s crucial to ensure that the analytics team should differentiate between Interesting Data and Useful Data. Amongst the important aspects in Machine Learning are “Feature Selection” and “Feature Extraction”. We are now witnessing the emerging fourth industrial revolution and a considerable number of evolutionary changes in machine learning methodologies to achieve operational excellence in operating and maintaining the industrial assets efficiently, reliably, safely and cost-effectively. AI techniques such as, knowledge based systems, expert systems, artificial neural networks, genetic algorithms, fuzzy logic, case-based reasoning and any combination of these techniques (hybrid systems), machine learning, biomimicry such as swarm intelligence and distributed intelligence. are widely used by multi-disciplinarians to solve a whole range of hitherto intractable problems associated with the proactive maintenance management of industrial assets. In this paper, an attempt is made to review the role of artificial intelligence in condition monitoring and diagnostic engineering management of modern engineering assets. The paper also highlights that unethical and immoral misuse of AI is dangerous.
Abstract: Artificial Intelligence (AI) is playing a dominant role in the 21st century. Organizations have more data than ever, so it’s crucial to ensure that the analytics team should differentiate between Interesting Data and Useful Data. Amongst the important aspects in Machine Learning are “Feature Selection” and “Feature Extraction”. We are now witnessin...
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Reproducing Musicality: Immediate Human-like Musicality Through Machine Learning and Passing the Turing Test
Aran Samson,
Andrei Coronel
Issue:
Volume 5, Issue 1, June 2021
Pages:
38-45
Received:
17 April 2021
Accepted:
12 May 2021
Published:
26 May 2021
Abstract: Musicology is a growing focus in computer science. Past research has had success in automatically generating music through learning-based agents that make use of neural networks and through model and rule-based approaches. These methods require a significant amount of information, either in the form of a large dataset for learning or a comprehensive set of rules based on musical concepts. This paper explores a model in which a minimal amount of musical information is needed to compose a desired style of music. This paper takes from two concepts, objectness, and evolutionary computation. The concept of objectness, an idea directly derived from imagery and pattern recognition, was used to extract specific musical objects from single musical inputs which are then used as the foundation to algorithmically produce musical pieces that are similar in style to the original inputs. These musical pieces are the product of evolutionary algorithms which implement a sequential evolution approach wherein a generated output may or may not yet be fully within the fitness thresholds of the input pieces. This method eliminates the need for a large amount of pre-provided data as well as the need for long processing times that are commonly associated with machine-learned art-pieces. This study aims to show a proof of concept of the implementation of the described model.
Abstract: Musicology is a growing focus in computer science. Past research has had success in automatically generating music through learning-based agents that make use of neural networks and through model and rule-based approaches. These methods require a significant amount of information, either in the form of a large dataset for learning or a comprehensiv...
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