Election Prediction Large corporations do not like unpredictability, so they want a better way to predict the outcome of upcoming elections so they can plan appropriately for future governments. Your company has been asked about the feasibility of building a tool for predicting the results of an upcoming election. You will have access to data about the outcome of previous elections, demographic data from the census about the people in the riding at the time of the election and polling data if it was available. The goal is to predict the probability of which party will win each riding if an election were held today. A rival company has proposed solving it by combining hidden Markov models and relational probabilistic models.

1. (a)

   Explain how this problem fits into the abstraction of an agent as studied in this course.

2. (b)

   Explain how the rival company’s solution may work, and explain why they may have chosen the technologies they proposed.

3. (c)

   What is the most challenging part of solving this problem? What would you recommend as a way to solve this? Justify any recommendation made.

Find some AI applications and classify the current state-of-the-art for that application in terms of the dimensions. Does the application automate what Kahneman [2011] calls System 1 or System 2 or neither or both?

There have been proposals made for a global ban on the use of Lethal Autonomous Weapon Systems (LAWS). Investigate and describe the current status of action on a ban on the use of LAWS. Present a brief argument in favor or against such a ban.

Consider the use of robots and companions for the elderly (or for infants). Investigate and describe briefly the current state of the art for these companions. Present three reasons in favor of their use and three reasons against.

Human rights and animal rights are well recognized. Outline a case for or against robot rights. Be specific about the robot rights you are discussing.