The second week for intelligent systems, we learned about the possible algorithms that are suitable for our project. As of right now, we found two possible algorithms that we are still debating on which algorithms that we will use. It is the Random Forest Algorithms and Partially Observable Markov Decision Process.

Random forest algorithm is composed of different decision trees, each with the same nodes, but using different data that leads to different leaves. It merges the decisions of multiple decision trees in order to find an answer, which represents the average of all these decision trees.

Partially Observable Markov Decision Process (POMDP) is a combination of an MDP to model system dynamics with a hidden Markov model that connects unobservant system states to observations. The only difference is in whether or not we can observe the current state of the process. In a POMDP we add a set of observations to the model. So instead of directly observing the current state, the state gives us an observation which provides a hint about what state it is in. The observations can be probabilistic; so we need to also specify the observation model. This observation model simply tells us the probability of each observation for each state in the model.