Review Questions 2

Expert Systems:-

7.1. Given a first order logic knowledge base constructed as follows:-

TELL(KB, AT(1,1))
TELL(KB, AT(3,1) AND NOT DEAD( ) => GOAL( ))
TELL(KB,  X,  Y, AT(X,Y) AND MOVE(A,B)
AND ( A = 1 OR A = -1 OR A = 0)
AND ( B = 1 OR B = -1 OR B = 0) => AT(X+A, Y+B) )

What is the solution to the following query ?

7.2. Given a situation calculus knowledge base as shown:-

TELL(KB, AT(1,1, S0))
TELL(KB, AT(2,1, Sn) => DEAD(Sn ))
TELL(KB, AT(3,1,Sn) AND NOT DEAD( Sn) => GOAL(Sn ))
TELL(KB,  X,  Y, AT(X,Y,Sn) AND MOVE(A,B,Sn)
AND ( A = 1 OR A = -1 OR A = 0)
AND ( B = 1 OR B = -1 OR B = 0) => AT(X+A, Y+B,Sn+1) )

What is the solution to the following query ?

Planning:-

8.1. Given a situation calculus knowledge base as shown:-

TELL(KB, AT(1,1, S0))
TELL(KB, AT(1,2, Sn) => DEAD(Sn ))
TELL(KB, AT(3,1, Sn) => DEAD(Sn ))
TELL(KB, AT(2,3, Sn) => DEAD(Sn ))
TELL(KB, AT(1,3,Sn) AND NOT DEAD( Sn) => GOAL(Sn ))
TELL(KB,  X,  Y, AT(X,Y,Sn) AND MOVE(A,B,Sn)
AND{ ( A = 1 AND B = 1) OR (A = -1 AND B = 0)
OR (A = -1 AND B = 0) OR (A = 0 AND B = 1)}
=> AT(X+A, Y+B,Sn+1) )

Is there a solution to the following query ?

ASK(KB,  p, GOAL(PlanResult(p, S0 ) )

8. 2. What is the difference between the hierarchical decomposition (HD) planner and the HD partial order planner (POP) ?

8. 3. Explain why the downward and upward solution property must hold for the HD-POP algorithm to be efficiently implemented.

Machine Learning:-
Past Exam Questions

Natural Language:-

10.1. Briefly define the terms speaker, listener and utterance in terms of natural language communication.

10.2. What are the categories of utterance that agents may generate ?

10.3. Discuss the ways that communication can be implemented between two agents.

10.4. Discuss the problems associated with general natural language identifying the reasons why it is a research problem that still has to be solved.

Machine Vision & Pattern Recognition:-

11.1. In terms of pattern recognition define the terms feature extraction and classification.

11.2. Define the terms supervised training and unsupervised training.

11.3. What is overtraining and undertraining and how can it be avoided.

11.4. What is the problem with using Bayesian Theory in pattern classification ?

11.5. What are the main features of the nearest neighbour classification algorithm ? Discuss the efficiency and optimality of this algorithm.

11.6. How does the K - nearest neighbour algorithm improve on the nearest neighbour classification algorithm ?