Artificial Intelligence 2075
Attempt
all questions. (10x6=60)
1. What do you mean by rational agents? Are the rational agents intelligent? Explain.
A rational agent is an agent which has clear preferences and models uncertainity via expected value. It always performs right action, right action means the action that causes the agent to be most successful in the given percept sequence.
Rational agent is capable of taking best possible action in any situation.
For each possible percept sequence, a rational
agent should select an action that is expected to
maximize its performance measure, given the
evidence provided by the percept sequence and
whatever built-in knowledge the agent has.
The agents rational behavior depends upon:
- Performance measures
- Prior environment knowledge
- Actions
- Percept sequence upto now
2. What is Bayes’s theorem? Explain its applications.
Baye's theorem is a way to apply conditional probability for prediction. Conditional probability is the probability of an event happening, given that it has some relationship to one or more other events.
Mathematically, Baye's theorem is defined as:
Bayes theorem provides a way to resolve existing predictions or theories (update probabilities) given new or additional evidence. This, in turn, makes the prediction more accurate.
Applications of Bayes theorem:
1. Medical science: Baye's rule is used for predicting a particular disease based on the symptoms and physical condition of the patient.
2. Weather forcasting: Baye's rule is a powerful algorithm for predictive modeling weather forcast.
3. Robotics: Baye's rule is used to calculate the probability of a robot's next steps given the steps the robot has already executed.
4. Finance: Baye's theorem can be used to rate the risk of lending money to potential borrowers.
etc.
3. How can you construct expert system? Explain knowledge engineering with a block diagram.
An expert system is a set of program that manipulate encoded knowledge to solve problem in a specialized domain that normally requires human expertise.
We can construct expert system using given steps as below:
Knowledge Engineering
Knowledge engineering is a field of AI that creates rules to apply data in order to initiate the thought process of human expert. Knowledge engineering attempts to take challenges and solve problems that would usually require a high level of human expertise to solve.
In general, knowledge engineering is the process of understanding and representing a human knowledge in a computer as a program. Knowledge engineering includes:
1) Knowledge acquisition
2) Knowledge representation
3) Knowledge validation
4) Inferencing
5) Explanation and justification
The interaction between these stages and source of knowledge is shown in the figure below:
4. Consider the following map of French cities:
Apply the A* algorithm to find out a route from Bordeaux to Grenoble.
The value v associated with a route between two neighboring cities M and N is
the length (in kilometers) of that route. The value [w] associated with a city
M is the straight line distance between M and Grenoble. Your solution should
show each step of the algorithm.
All cats like fish, cats eat everything they like, and
Ziggy is a cat.
a) Translate the sentences into FOL.
b) Convert the sentences into clausal normal
form.
c) Answer using FOL, if Ziggy eats fish?
6. Consider the following a production system characterized by
- Initial short term : C5, C1, C3
- Production rules: C1 & C2 àC4
C3 àC2
C1 & C3 àC6
C4 àC6
C5 àC1
Show a
possible sequence of two recognize-art cycles. Which will be the new content of
the short-term memory after these two cycles?
7. What do you mean by causal network? Explain it with practical application.
Causal Networks / Bayesian networks are a type of probabilistic graphical model that uses Bayesian inference for probability computations.
- Causal 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.
E.g.
P(x) = 0.5
P(y/x) = 0.7
P(z) = 0.8
P(u/y) = 0.47
Applications of Causal / Bayesian Network:
1. Spam Filter: You
must be lying if you say that you’ve never wondered how Gmail filters spam
emails (unwanted and unsolicited emails. It uses Bayesian spam filter, which is
the most robust filter.
2. Turbo Code: Bayesian
Networks are used to create turbo codes that are high-performance forward error
correction codes. These are used in 3G and 4G mobile networks.
3. Image Processing: Bayesian
Networks use mathematical operations to convert images into digital format. It
also allows image enhancement.
4. Biomonitoring: Quantifying the concentration of chemicals couldn’t get any easier than with Bayesian Networks. In this, the amount of blood and tissue in humans is measured using indicators.
5. Gene Regulatory Network (GNR): A GNR contains various DNA segments of a cell that interact with other cell contents through protein and RNA expression products. The predictions of its behavior can be analyzed using Bayesian Networks.
8. What is neural network? Explain the neural net learning methods.
9. Represent the following sentences into a semantic network.
Birds are animals.
Birds have feathers, fly and lay eggs.
Albatros is a bird.
Donald is a bird.
Tracy
is an albatross.
10. Differentiate between natural language understanding (NLU) and natural language generation (NLG).
Natural Language Understanding(NLU): It is the process of mapping the given inputs in natural language into useful representation and analyzing different aspects of the language.
Natural Language Generation(NLG): It is the process of producing meaningful phrase and sentences in the form a machine representation system such as KB or logical form.
