# The Role of AI in Warehouse Digital Twins: Literature Review +
## 4 Analysis Framework
### Artificial Intelligence  
AI has existed since the Dartmouth Summer Research Project on Artificial Intelligence in 1956, but its evolution has been slow and rocky. Even today, there is yet to be a clear consensus on what can be considered AI [22]. AI methods include analytic hierarchy process (AHP), fuzzy logic (FL), genetic algorithms, neural network (NN), and simulated annealing (SA).  
Modern subsections of AI include machine learning (ML) and deep learning (DL), which are some of the most popular ones [23]. Although the terms are often used interchangeably, DL specifically refers to deep artificial neural networks and sometimes deep reinforcement learning, primarily ML techniques.  
AI developers today have realized that training a system by demonstrating examples of desired input-output behavior is a much simpler and more efficient approach than manually programming the desired response for every conceivable input [23]. Adopting machine learning and deep learning techniques has paved the way for such training. These methods enable systems to learn from large volumes of data and gradually enhance their performance over time. However, the various AI techniques have different strengths and limitations, and the choice of which one to use depends on the specific application and the available data. For example, neural networks are good at recognizing patterns and making predictions based on past data, while genetic algorithms are more suited to optimization problems.  
An examination of the various types of learning employed is deemed essential to garner a deeper understanding of the ML techniques utilized in the field. This will not only aid in summarizing the information surrounding these techniques but also facilitate the identification of prevailing trends and research perspectives. Drawing on the seminal work of [23], three main types of machine learning can be distinguished below. The main discrepancy between these types lies in whether the training dataset is labeled or not.
* Supervised learning (SL): the algorithm is provided with a clearly defined set of input features X and corresponding output labels Y. Supervised learning can be used in intralogistics to predict demand for specific products, to optimize inventory levels, or to predict delivery times.
* Unsupervised learning (UL): the algorithm is provided only input features X. The goal is to find patterns or structures within the data that can be used to group similar data points or to identify outliers using techniques such as cluster analysis. UL is typically used when there is no clear understanding of the underlying structure of the data or when there is no prior knowledge about the data. UL, such as clustering, can be used in intralogistics to identify similar groups of products or to cluster similar customers based on their buying behavior.
* Reinforcement learning (RL): involves an agent that learns by interacting with an environment and receiving rewards or punishments based on its actions. The learner aims to maximize the cumulative reward value over time through trial and error. Reinforcement learning is commonly used in tasks such as game playing, robotics, and autonomous navigation. RL can be applied to train an automated guided vehicle (AGV) in a warehouse to navigate through the facility while avoiding obstacles and maximizing the number of delivered packages. By interacting with the environment, the AGV learns which actions lead to the most desirable outcomes and adjusts its behavior accordingly, gradually improving its performance over time. This allows for a more flexible and adaptive approach to learning in general, which responds to new challenges and changing environments without explicit programming.