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.
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American Journal of Artificial Intelligence (Volume 5, Issue 1)
This article belongs to the Special Issue Emerging Trends in Artificial Intelligence with Machine Learning and Nature Inspired Algorithms |
| DOI | 10.11648/j.ajai.20210501.11 |
| Page(s) | 1-16 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Intelligent Agent, Dynamic Environment, Performance
| [1] | Zeigler, B. P. (2014). Object-oriented simulation with hierarchical, modular models: intelligent agents and endomorphic systems. Academic press. |
| [2] | Wooldridge, M., & Dunne, P. E. (2001, August). The computational complexity of agent verification. In International Workshop on Agent Theories, Architectures, and Languages (pp. 115-127). Springer, Berlin, Heidelberg. |
| [3] | Balduccini, M., & Lanzarone, G. A. (1997). Autonomous semi-reactive agent design based on incremental inductive learning in logic programming. Proc. of the ESSLI, 97, 1-12. |
| [4] | Niels, B. A. (2002). A model for Procedural Knowledge, PhD thesis, University of Nyenrode, Breukelen. |
| [5] | Thorndyke, P. W., & Goldin, S. E. (1983). Spatial learning and reasoning skill. In Spatial orientation (pp. 195-217). Springer, Boston, MA. |
| [6] | Jennifer Herron (2017) Intelligent Agents for the Library, Journal of Electronic Resources in Medical Libraries, 14: 3-4, 139-144, DOI: 10.1080/15424065.2017.1367633. |
| [7] | Den Heijer, F. M., & Goede, R. (2014). Implementing an intelligent agent in a known, observable, discrete and deterministic environment using a scriptable game-engine. In Intelligent Systems and Agents 2014 Conference (ISA 2014), in press, Lisbon, Portugal. |
| [8] | Owaied, H. H., & Abu-A'ra, M. M. (2007, June). Functional Model of Human System as Knowledge Based System. In IKE (pp. 158-164). |
| [9] | Barjtya, S., Sharma, A., & Rani, U. (2017). A detailed study of Software Development Life Cycle (SDLC) models. International Journal Of Engineering And Computer Science, 6 (7), 22097-22100. |
| [10] | Abuhadba, S. (2011). An Intelligent Agent Model and a Simulation for a. Given Task in a Specific Environment. Supervisor. Dr. Hussein H. Owaied. |
| [11] | Shah, S., Lynch, L. M., & Macias-Moriarity, L. Z. (2010). Crossword puzzles as a tool to enhance learning about anti-ulcer agents. American journal of pharmaceutical education, 74 (7). |
| [12] | Chen, B., & Cheng, H. H. (2010). A review of the applications of agent technology in traffic and transportation systems. IEEE Transactions on intelligent transportation systems, 11 (2), 485-497. |
| [13] | Bekey, G. A. (2005). Autonomous robots: from biological inspiration to implementation and control. MIT press. |
| [14] | Dorf, Richard C., "Modern Control Systems, 7th edition" (1995). Books by Marquette University Faculty. Book 184. http://epublications.marquette.edu/marq_fac-book/184. |
| [15] | McCorduck, P. (1983). The fifth generation: artificial intelligence and Japan's computer challenge to the world. Reading, Mass.: Addison-Wesley. |
| [16] | Kumar, P. R., & Varaiya, P. (1986). Stochastic systems: estimation, identification and adaptive control (Prentice-Hall Information & System Sciences Series). |
APA Style
Safa'a Yousef Abu Hadba, Ibtehal Nafea. (2021). An Intelligent Agent Model and a Simulation for a Given Task in a Specific Environment. American Journal of Artificial Intelligence, 5(1), 1-16. https://doi.org/10.11648/j.ajai.20210501.11
ACS Style
Safa'a Yousef Abu Hadba; Ibtehal Nafea. An Intelligent Agent Model and a Simulation for a Given Task in a Specific Environment. Am. J. Artif. Intell. 2021, 5(1), 1-16. doi: 10.11648/j.ajai.20210501.11
AMA Style
Safa'a Yousef Abu Hadba, Ibtehal Nafea. An Intelligent Agent Model and a Simulation for a Given Task in a Specific Environment. Am J Artif Intell. 2021;5(1):1-16. doi: 10.11648/j.ajai.20210501.11
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Download@article{10.11648/j.ajai.20210501.11,
author = {Safa'a Yousef Abu Hadba and Ibtehal Nafea},
title = {An Intelligent Agent Model and a Simulation for a Given Task in a Specific Environment},
journal = {American Journal of Artificial Intelligence},
volume = {5},
number = {1},
pages = {1-16},
doi = {10.11648/j.ajai.20210501.11},
url = {https://doi.org/10.11648/j.ajai.20210501.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20210501.11},
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.},
year = {2021}
}
TY - JOUR T1 - An Intelligent Agent Model and a Simulation for a Given Task in a Specific Environment AU - Safa'a Yousef Abu Hadba AU - Ibtehal Nafea Y1 - 2021/02/10 PY - 2021 N1 - https://doi.org/10.11648/j.ajai.20210501.11 DO - 10.11648/j.ajai.20210501.11 T2 - American Journal of Artificial Intelligence JF - American Journal of Artificial Intelligence JO - American Journal of Artificial Intelligence SP - 1 EP - 16 PB - Science Publishing Group SN - 2639-9733 UR - https://doi.org/10.11648/j.ajai.20210501.11 AB - 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. VL - 5 IS - 1 ER -
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