cankobanz / hidden-markov-model

Simple hidden markov model is applied.

hidden-markov-model

Reference: This is a school homework. Below explanation is made by the instructor of CmpE 540 in Bogazici University 2021-2022 Spring

Hidden markov model is applied as a solution to problem below.
Take the sequence of sensor measurements from the user and print out the probability distribution of robot’s location at the last time-step.
Input: on on off off
Output: Print out the probability distribution P(X4|on1,on2,off3,off4) as a list of values.

• The robot might be in one of the three grids. The map of the environment is known.
• The robot has a short-range distance sensor directed towards right/east direction. The distance sensor detects whether there is a wall or not on the right with some noise. If there is a wall on its immediate right, the sensor measures ON with 60%. If there is not wall on its immediate right grid, the sensor signals ON with 10%.
• At each step the robot executes move-to-the-right action. This action is successful (moves the robot to the grid on the right) with 80% success, i.e. the robot does not move with 0.2 probability. If the robot is in the right-most grid, move-to-the-right action does not change the robot’s location.
• The initial whereabouts of the robot is completely unknown, i.e. the robot can be placed in any grid initially.