So AI is one hell of a huge course. There was lots to learn, lots that could come up and lots that I didn't know! The start and end of the course were my strongest points, the middle part was perhaps my worst section of the course.
Question 1
- A - Definition of Turing Test in AI [4]
- B - Advantages and disadvantages of learning capabilities in intelligent agents [3]
- C - Define problem generator give trade-offs [3]
- D - Define memes and how they play a part in agent design [4]
- E - Compare behavior-based, classical and hybrid approaches to robot design [5]
- F - Issues related to evaluation functions in search-based game players [3]
- G - Alpha-beta pruning, effects on computational cost and quality of results [3]
Question 2
- A - Defining precepts and actions [3]
- B - Three novel features to be included in mobile system [6]
- C - Define utility function and give refinations for the features in part b [4]
- D - Fundamental issues in perception, examples with features in part b. Sensor fusion. [6]
- E - Role of emotions in design of agents, relevance to mobile agent [6]
Question 3
- A - 3 reasons for not using deterministic logic in medical diagnosis [3]
- B - Definition of belief networks. Benefits in terms of computational, knowledge elicitation and interpretability [6]
- C - Define maximum expected utility, relationship with AI and human decision making [4]
- D - Explaining away in belief networks [3]
- E - Probability computations [5]
- F - Same as part E, using natural frequencies [2]
- G - Draw and explain risk-averse utility curve for a lottery [3]
- H - Equation to support decision process [4]
The exam was not as bad as could have been expected. I did manage to draw some random graph for the risk-averse utility curve, with axes labeled risk and averse respectivley. Hope I managed to scrape a decent grade.
- Chris