Artificial Intelligence 2076
Attempt
all questions. (10x6=60)
1. How can you define AI from the dimension of rationality?
2. What is intelligent agent? Design PEAS framework for,
- Soccer playing agent
- Internet shopping assistant
PEAS Framework for:
Soccer Playing Agent
Performance Measure (P): To Play, Make Goal & Win the Game.
Environment (E): Soccer, Team Members, Opponents, Referee, Audience and Soccer Field.
Actuators (A): Navigator, Legs of Robot, View Detector for Robot.
Sensors (S): Camera, Communicators and Orientation & Touch Sensors.
Internet Shopping Assistant
Performance measure: price, quality, appropriateness, efficiency
Environment: current and future WWW sites, vendors, shippers
Actuators: display to user, follow URL, fill in form
Sensors: HTML pages (text, graphics, scripts)
3. Convert following statement into FOPL, every friend of Ramesh has visited pokhara. Everyone who visits Pokhara does boating on Fewalake. Ramesh has done boating on Fewalake. Now using resolution try to infer; some friend of Ramesh has done boating on Fewalake.
4. How iterative depending search is better than DFS and BFS. Consider following state space, use iterative deepening search considering S as start and g as goal.
5. What is script? How knowledge is represented in script? Illustrate component of script with a example.
6. What is machine learning? How genetic algorithm can be used to train agents? Discuss the operations of genetic algorithm.
7. Configure a feed-forward neural network with your own assumptions of inputs and weights and express it mathematically. Write an algorithm for training neural networks using allebbian learning.
8. What is natural language processing? How morphological analysis is done during processing?
9. Consider a following state space representing a game. Use minimax search to find solution and perform alpha-beta pruning, if exists.
10. How facts in uncertain knowledge are represented? Configure a Bayesian network for following:
The probability of having rain is 60%. The chances of getting cold if it will rain is 80%. The probability of not having sunshine is 90%. The probability that it will be hot if it is sunshine is 0.67.
Facts in uncertain knowledge are represented using Bayesian network. Bayesian networks are a type of probabilistic graphical model that uses Bayesian inference for probability computations.
- Bayesian Network is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph.
- Nodes in the graph represent the random variables and the directed edges between nodes represent conditional dependencies.
- The edge exists between nodes if there exists conditional probability i.e. a link from X to Y means Y is dependent of X.
- Each nodes are labelled with probability.
Bayesian network for given statement:
