<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Planning research in artificial intelligence began earlier, dating back to 1969, and the general problem solver (GPS) designed by Newell and Simon [<xref ref-type="bibr" rid="j_ijanmc-2023-0061_ref_001">1</xref>]. Since then, other researchers have also proposed other planners, such as QA3 system using theorem proving method to construct planning[<xref ref-type="bibr" rid="j_ijanmc-2023-0061_ref_002">2</xref>], Fikes and Nilsson designed STRIPS planning system [<xref ref-type="bibr" rid="j_ijanmc-2023-0061_ref_003">3</xref>], until today STRIPS planning method is still only a very important research method in the field of planning. After more than 50 years of research, there have been many intelligent planning methods such as graph planning method, probability planning, timing planning, heuristic planning and perceptual planning. Although the existing intelligent planning methods have been able to solve some problems, the existing planning mainly focuses on the design of the planning algorithm, and the manual analysis preprocessing workload is large. Research has pointed out that the new generation of intelligent planners should be knowledge-based development of knowledge-based planning systems is a meaningful work [<xref ref-type="bibr" rid="j_ijanmc-2023-0061_ref_004">4</xref>]. Therefore, by analyzing the existing intelligent planning methods and AORBCO model, this paper gives the definition of intelligent planning with self-consciousness, and proposes an intelligent planning method based on self-consciousness based on behavior control.</p>
</abstract>

Planning research in artificial intelligence began earlier, dating back to 1969, and the general problem solver (GPS) designed by Newell and Simon [1]. Since then, other researchers have also proposed other planners, such as QA3 system using theorem proving method to construct planning[2], Fikes and Nilsson designed STRIPS planning system [3], until today STRIPS planning method is still only a very important research method in the field of planning. After more than 50 years of research, there have been many intelligent planning methods such as graph planning method, probability planning, timing planning, heuristic planning and perceptual planning. Although the existing intelligent planning methods have been able to solve some problems, the existing planning mainly focuses on the design of the planning algorithm, and the manual analysis preprocessing workload is large. Research has pointed out that the new generation of intelligent planners should be knowledge-based development of knowledge-based planning systems is a meaningful work [4]. Therefore, by analyzing the existing intelligent planning methods and AORBCO model, this paper gives the definition of intelligent planning with self-consciousness, and proposes an intelligent planning method based on self-consciousness based on behavior control.

Research and Design of Planning Systems in the AORBCO Model

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Research and Design of Planning Systems in the AORBCO Model"}

Planning research in artificial intelligence began earlier, dating back to 1969, and the general problem solver (GPS) designed by Newell and Simon [1]. Since...

Research and Design of Planning Systems in the AORBCO Model

Pubblicato online: 15 mar 2024

Pagine: 1 - 9

DOI: https://doi.org/10.2478/ijanmc-2023-0061

Parole chiave
Cloud Computing, Intelligent Planning, Intelligent Model, Self-Consciousness, AORBCO Model

© 2023 Chongtian Feng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Research and Design of Planning Systems in the AORBCO Model

Pubblicato online: 15 mar 2024

Pagine: 1 - 9

DOI: https://doi.org/10.2478/ijanmc-2023-0061

Parole chiaveCloud Computing, Intelligent Planning, Intelligent Model, Self-Consciousness, AORBCO Model

© 2023 Chongtian Feng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Parole chiave
Cloud Computing, Intelligent Planning, Intelligent Model, Self-Consciousness, AORBCO Model