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JA6. Learning Journal 6

The Learning Journal is a tool for self-reflection on the learning process. The Learning Journal will be assessed by your instructor as part of your Final Grade.

Answer the following questions

1. Describe what you did. You need to describe what you did and how you did it

This was the 6th week of this course; it was all about uncertainty and how it affects reasoning in open or incomplete worlds. I started materials assigned in the learning guide, then I did the discussion assignment which was about analyzing a belief network of a home and electrical connections environment. I also did the graded and self quizzes along with the learning journal assignment.

2. Describe your reactions to what you did

I found the topics presented so far to be of great importance theoretically and practically. However, they seem very complex and condensed to be included in one week. The idea of uncertainty and conditional probability is very big and needs more time to be understood. I also think that an introduction to probability theory would have been helpful before diving into the topic.

3. Describe any feedback you received or any specific interactions you had while participating discussion forum or the assignment Discuss how they were helpful

The discussion assigned asked us to analyze a belief network of a home and electrical connections environment and answer some questions about it. It was a good exercise to understand how the variables are connected and how they affect each other. However, I think this exercise is pretty basic and I -personally- question the added value of it.

4. Describe your feelings and attitudes

I feel that the topic needs more than one week with the necessity to explain more about probability theory, adding more examples, changing the discussion to be more useful, and to add a programming assignment to the topic. It is important that students put the concepts of this week in practice to understand how they are used in real-world applications.

5. Describe what you learned. You can think of one or more topics and explain your understanding in writings

There are many situations where agents must operate in open-world environments where complete knowledge of the environment is not possible; and that is where probability theory comes in to help guide decisions. Conditional probability is the evaluation of the level of belief in a hypothesis that is expressed in terms of a probability of truth; that is, how the probability changes as we get more evidence supporting or contradicting the hypothesis (Taipala, 2014).

We use Bayes’ theorem to compute the probability of a hypothesis based upon all of the factors or axioms that are related to it. A prior probability is an initial probability value obtained before any additional information is obtained. A posterior probability is a probability value that has been revised by using additional information (Taipala, 2014).

A belief network is a directed acyclic graph representing conditional dependence among a set of random variables. The random variables are the nodes. The arcs represent direct dependence. Belief networks provide us with a way of visualizing the conditional probabilities between items or events. Each node has a conditional probability table (CPT) that gives the probability of each of its value given every possible combination of values for its parents (Poole & Mackworth, 2017).

Rationality is not about knowing facts, it is about knowing which facts are relevant. There is an interesting story from 3Blue1Brown about Bayes’ theorem that explains this concept very well; Daniel Kahneman and Amos Tversky presented subjects with a description of an individual characterized as shay, withdrawn, and with a tidy soul; then asked whether this person was more likely to be a librarian or a farmer. Initially, without additional context, respondents considered the probabilities equal (3Blue1Brown, 2019).

However, after learning the individual’s personality traits, 84% of the participants concluded that he was more likely to be a librarian, aligning with the prevailing stereotype. This conclusion ignored the fact that there are approximately 20 times more farmers than librarians in society. The study thus highlights how irrelevant facts can influence decision-making, a phenomenon that is also reflective of certain tendencies in artificial intelligence reasoning, where objective data may lead to a different conclusion than that reached by human intuition (3Blue1Brown, 2019).

6. Did you face any challenges while doing the discussion or the development assignment? Were you able to solve it by yourself?

The concept of probability is complex to me; let alone the concept of conditional probability and belief networks. I had to watch some videos and read some articles to remember probability theory and how to compute it. Things, however, seem easier when sketching specific problems but generalizing them is still a challenge.

References