The transformation of work networks from monolithic systems to intricate yet decentralized eWork networks would help individuals transition to cleaner and more efficient environments. Optimization is a large component that is critical for smart cities to develop. Artificial intelligence uses deep learning to determine various efficient options in a smart city. For instance, using ant systems would enable developers to integrate various home nodes and goal nodes based on the systemâs task within the city. The system would have autonomy and gauge the best and shortest path to achieve goals, focusing on this path using increased pheromone development at the best path (Complex systems and AI, 2021). These systems would be reinforcing, constantly adapting based on the task at hand, with a single home node to return once the task is complete (Nof et al., 2016). By allowing the system to seek paths to conduct a task randomly, they would autonomously learn which paths to take to achieve a particular goal. The interconnected nature of this system also helps propagate the ant system as various agents would seek the path of least resistance to task completion.
Gathering a lot of information would aid the machines to integrate deeply into human functions, further boosting their capacity to provide optimal solutions to dynamic activities around the cities. Furthermore, it is important to address ant systemsâ capacity to boost collaboration and information synthesis (Nof et al., 2016). Different agents would provide pheromones (information) to deal with a problem. These systems cannot collapse simultaneously and cause the entire destruction of a smart cityâs infrastructure due to an error. As in the ant colony, the system would involve a collaboration mechanism that allows varying paths to be calculated. The system would correct any errors posited by some agents by limiting information in subsequent periods that would lead to these results. This mechanism would prohibit reinforcement of these traits, allowing the machines to optimize the solutions to a problem while deviating from established errors (Campisi et al., 2021). The dynamic form of these systems would allow the machines to develop complex âthoughtsâ, enabling them to reason and determine the best courses of action for repetitive activities.
The ant colony also exhibits autonomy regarding duty allocation, a concept that would inspire efficient protocols for buildings. Programming different agents to perform particular activities and providing them with pheromones (subnet capacity information) allows them to discern their purpose (Nof et al., 2016). The agent would replicate itself to determine every possible path to the last step in its purpose. Supposing the proceeding step was the acquisition of materials, it would determine the best place to purchase these materials focusing on quality and quantity. It would clone itself and use every possible path to gauge the best supplier of these materials and make a purchase through their system (Alanne & Sierla, 2022). The ant agent would replicate itself from the current step and look for optimal paths to reach the arrival resource, conduct repairs, or build infrastructure. The system becomes more knowledgeable with each clone cycle, enabling it to deal with issues required in the building process dynamically.
Bio-inspired protocols enable risk diversification as the system is fragmented into autonomous units. While every agent has a particular purpose, they coalesce into one unit at the arrival node. The agents can work fast because they do not have to make differing computations and focus on a particular task. Optimization of each section in the system is ensured through a control center that reinforces positive information and weakens erroneous paths. In this way, the system can dynamically shift based on the developerâs needs, a critical notion for smart cities and buildings.
References
Alanne, K., & Sierla, S. (2022). An overview of machine learning applications for smart buildings. Sustainable Cities and Society, 76, 103445.
Campisi, T., Severino, A., Al-Rashid, M. A., & Pau, G. (2021). The development of the smart cities in the connected and autonomous vehicles (cavs) ERA: From Mobility Patterns to scaling in cities. Infrastructures, 6(7), 100.
Complex systems and AI. (2021).Ant System. Complex systems and AI.
Nof, S. Y., Ceroni, J., Jeong, W., & Moghaddam, M. (2016). Revolutionizing collaboration through E-work, e-business, and e-service. Springer Berlin.