Reference no: EM133482401
Part 1
Read Section 6.1 in Artificial Intelligence and Problem Solving.
Using this problem:
-Locate two similar online programs and play the puzzle.
-Analyze the efficiency of each one.
Part 2
-Next, build causal graphical models for the scenarios below, providing the precise relationships of cause and effect:
Build a causal graphical model that represents getting to a ball game with the following variables: * T: Traffic, * R: It rains, * L: Low pressure, * D: Roof drips, * B: Ballgame, and * C: Cavity.
-Build a causal graphical model that represents making a 911 call with the following variables: * B: Burglary, * A: Alarm goes off, * M: Mary calls, * J: John calls, and * E: Earthquake!
Then, in one thousand words: For
Part 1:
-Outline which program is more efficient and why. Compare the moves that were made. Are you able to differentiate between good and bad moves?
-Compare the best and least human window-compatible solutions against the best machine solution.
AI has evolved into using algorithms to solve a solution in a manner more akin to human reasoning. Is that demonstrated here? Why or why not? Explain how examining a problem like this can benefit AI development.
For Part 2:
- Present documentation of your attempt to solve the problems.
- Examine various probabilistic modeling solutions (Hidden Markov, Bayesian Network, etc.). How can these prove useful in various applications? How can they be used in algorithms?
- Since the graphical approach to causal inference has led to the discovery of several useful algorithms. Describe a few. Use supporting evidence. Do you feel predictive AI algorithms will have a bigger role to play in AI in the future?