Delivery in day(s): 4
MCSE003 Artificial Intelligence and Knowledge Management
Artificial Intelligence abbreviated as AI is one of the most disruptive technology and is proved itself in different genres. AI is designed to enable a machine, computer or a software think independently based on the programming installed in it. The aim of the enabling self-thinking is to offer the machines with a capability to make justifiable decisions similar to a human’s decision. The discussed technology has emerged as one of the most vital tools in simplifying the challenges that aa human face due to human errors and their limitation in decision makings. The limitation of machines in decision making forces the human to do the labour and at times process operations manually. However, with the technology, the machines make the decision for the processes based on the data collected by them. Hence, it is being deployed in different sectors. One of the sub-branches that have emerged due to rise I AI is the Automated Planning and Scheduling or AI Planning and Scheduling. Based on the strategies adopted, collected data and the actions taken by the human factors in the past, the machine starts learning and accordingly plans and schedules a project. With time the machine understands the concepts and based on the collected data makes the plans and schedules.
The discussed measure has offered several benefits to the project management team in strategizing for the project. The benefits involve cost reduction, mitigation in the calculative errors due to human factor, an effective scheduling with every task in sight and concern along with multiple others. The benefits of the technology have proven to be immense, however, recently certain errors have been identified in the use of the AI for planning and development raising question over its use in planning and scheduling. One of the most prominent challenge identified is the difference in approach of the project team and AI. The project team believes in the ideology of planning, analysing, designing, building, testing and deploying while the AI on the contrary, supports the ideology of data identification, data collection, data cleansing and data curation. The difference in approach deems need for different skills and mind-sets along with different methodologies which is challenging for both the project team and AI. To attain a common approach ideally either the project team or the AI needs to be educated about the other’s approach. Furthermore, it can be a repetitive process based on the dissimilarities of the projects undertaken. Additionally, as the technology is based solely on the collected data and hence the technology requires a large amount of data with proper quality. The data is collected by the technology to identify patterns and draw frameworks but in case of same data size, the identification may be improper. Another notable point is the quality of the data, with inefficient quality the planning and scheduling will be improper.
Similarly, other flaws have been identified and in the process raising question over the feasibility of the AI in planning and scheduling. Hence, the aim of the paper is to determine the feasibility of the AI in planning and scheduling to identify whether or not should it be included as part of the planning and scheduling process of a project.
To attain the objective of the paper, the assistance of several secondary work that are available on the internet will be adopted. As part of the discussion, a reflection over the perks and the challenges offered by the AI in the planning and scheduling of a project will be accounted for and based on them a conclusive point will be reached to determine the feasibility of the subject in the selected process. The report will initiate with the discussion over the technology and industry, followed by identification and flaws when they associated together. Finally, a comparison between the perks and challenges will be done to conclude on the paper. Secondary sources will be used for attaining the proposed objective and the most crucial of them have been listed in the reference section below along with their Url address. Additional, sources may be included based on the progress over the discussion and will be added with the final submission.
1. Bacchus, F. (2001). Aips 2000 planning competition: The fifth international conference on artificial intelligence planning and scheduling systems. Ai magazine, 22(3), 47. [Url: http://www.aaai.org/ojs/index.php/aimagazine/article/viewFile/1571/1470]
2. Cardin, O., Trentesaux, D., Thomas, A., Castagna, P., Berger, T., & El-Haouzi, H. B. (2017). Coupling predictive scheduling and reactive control in manufacturing hybrid control architectures: state of the art and future challenges. Journal of Intelligent Manufacturing, 28(7), 1503-1517. [Url: https://hal.archives-ouvertes.fr/hal-01182909/document]
3. Chien, S. A., Knight, R., Stechert, A., Sherwood, R., & Rabideau, G. (2000, April). Using Iterative Repair to Improve the Responsiveness of Planning and Scheduling. In AIPS (pp. 300-307). [Url: http://www.aaai.org/Papers/AIPS/2000/AIPS00-032.pdf]
4. Haslum, P., & Geffner, H. (2014, May). Heuristic planning with time and resources. In Sixth European Conference on Planning. [Url: https://www.aaai.org/ocs/index.php/ECP/ECP01/paper/viewFile/7291/8176]
5. Vallati, M., Chrpa, L., Grze?, M., McCluskey, T. L., Roberts, M., & Sanner, S. (2015). The 2014 international Environment Law planning competition: Progress and trends. Ai Magazine, 36(3), 90-98. [Url: http://www.aaai.org/ojs/index.php/aimagazine/article/viewFile/2571/2492