Let’s now go back and write a program in which we calculate the new mean and the new variance term. I really just want you to write a Python program that implements those equations so that we can test them. I’m giving you a skeleton program, which has a function… Continue Reading New Mean and Variance – Artificial Intelligence for Robotics

Here are my answers. The prior for fire is 0.001 times the probability that the neighbor now correctly said, yes, it burns, which is 0.9. He lies with a probability of 0.1, so the complement is 0.9. This gives us 0.0009. For the complement, the prior of no fire, is… Continue Reading Bayes’ Rule Solution – Artificial Intelligence for Robotics

I’m now going to quiz you on Bayes Rule. Say you own a house, and you know that the house might catch fire in your absence, but the probability of it catching fire–“f” over here–is small. It’s a 10th of a percent–0.001. Let’s say every afternoon you talk to your… Continue Reading Bayes’ Rule – Artificial Intelligence for Robotics

Welcome to homework assignment #4 in CS373. To remind you, we covered A-star and dynamic programming in class. Let’s start with an A-star question. We learned in class that we can use heuristics, and a heuristic is a admissible if the heuristic value is no larger than the actual cost… Continue Reading Admissible Heuristic – Artificial Intelligence for Robotics

In the path planning class, we specified paths as a sequence of points in a 2D grid just like this over here. For the smoothing purposes, we will call each point xi. This is a sequence that goes from x0 to xn-1, and each of the x’s is really a… Continue Reading Smoothing Algorithm – Artificial Intelligence for Robotics

I’m now replacing the red by green over here, and I rerun my code and out come these funny numbers. Somewhere in there is the division by 44, but you can see that the 1st, the 4th, and the 5th grid cell have a much larger value than the grid… Continue Reading Test Sense Function Solution – Artificial Intelligence for Robotics

In Kalman filter land, we’re going to build a 2-dimensional estimate. 1 for the location, and 1 for the velocity denoted x dot. The velocity can be zero. It can be negative, or it can be positive. If initially I know my location, but not my velocity, then I represent… Continue Reading Kalman Filter Prediction – Artificial Intelligence for Robotics

Let’s now talk about the second part of this lesson called PID control. PID control is a vast field in control, and many, many classes can be taught about this one subject matter. What I’ll do is I’ll give you the very basics, and I’ll let you implement the very… Continue Reading PID Control – Artificial Intelligence for Robotics

[Narrator] So, let me ask you a second quiz. In particular I would like to know whether distributions that can be represented may be unimodal or can also be multimodal. So, check unimodal if this is all we can do, whereas if we can have multiple bumps in our probability… Continue Reading Belief Modality – Artificial Intelligence for Robotics

Let me ask you Bayes Rule in the context of a completely different example to see if you understand how to apply Bayes Rule. This time it’s about cancer testing. It is an example that is commonly studied in statistics classes. Suppose there exists a certain type of cancer, but… Continue Reading Cancer Test – Artificial Intelligence for Robotics