This homework explores the applications of Temporal Reasoning in Artificial Intelligence. In general, thesolution for a temporal reasoning task involves taking a sequence of actions/observations on an PartiallyObservable Markov Decision Process (POMDP Environment) , applying a temporal-reasoning algorithmthat you learned from this class, and returning the most probable sequence of the hidden states that thePOMDP most-likely went through when experiencing the given sequence of actions/observations.

More specifically, this assignment provides you with two versions of temporal data: a base versioninvolving the “Little Prince” Environment and an advanced version that revolves around speechrecognition and text prediction.

You will be given a list of available percepts, actions and states and the corresponding initial stateweightages, transition and observation weight values in that environment (More on the input structure willbe covered in the sections below) . Your task is to design and implement a temporal-reasoning algorithm,that will take a sequence of actions/observations and determine the most-likely sequence of states that thisPOMDP has gone through, as shown in Figure 2. For example, if the Little Prince’s experience is given as<rose, forward, none, …, turn, rose, backward, volcano, …>

Then, your program should return a sequence of hidden-states that this POMDP is most-likely goingthrough (The following sequence is an example of the final state sequence that one might encounter):