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COS3751 - Combined Tut201,202, 203 Letters ()
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Combined Tut 201, 202, 203 letters (Questions & Answers) arranged in descending order from 2021 to 2018. Great for exam practice and revision
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COS3751Assignment PACK
2018-2021
Tut 201, 202, 203 Letters
Questions. Answers
, COS3751/201/1/2021
Tutorial Letter 201/1/2021
Techniques of Artificial Intelligence
COS3751
Semester 1
School of Computing
This tutorial letter contains assignment 01
BAR CODE
university
Define Tomorrow. of south africa
, ASSIGNMENT 1
Solution
Total Marks: 150
Unique Assignment Number: 848630/772589
Study material: Chapters 1 through 4. You may skip sections 4.2 and 4.5.
Important: When we use the phrase ‘define’ (particularly in Question 2), we are looking for a formal
definition using some form of formal notation, and not simply an English description or definition.
For example: ‘Define the initial state for an agent in Johannesburg’. Answer: In(Johannesburg).
‘Define the actions available to this agent given that the agent simply moves between major
metropolitan areas’. Answer: Actions(In(Johannesburg)) = {Go(Bloemontein), Go(Durban), . . . }.
When we want an English definition we will explicitly ask for it.
Question 1: 13 Marks
(1.1) Explain the difference between a single and multi-agent environment. (6)
An agent solving a problem by itself is a single agent environmentX. The key dis-
tinction is if an agent X’s behavior is best described as maximizing a performance
measure whose value depends on agent Y’s behaviorXX. For example in a chess
game the opponent agent X is trying to maximize its performance measure, which by
the rules of chess minimises agent Y’s performance measureX. Thus chess is a com-
petitive multi agent environment. In multiagent environments communication emerges
as a rational behaviorXwhile non-existent in single agent environmentsX. Pg 42-43
(1.2) Explain the difference between a Deterministic and Stochastic environment. (4)
In a deterministic environment the next state is completely Xdetermined by the current
state and the agent’s action X. In a stochastic environment one cannot completely
Xdetermine the next state based solely on the current environment and on the agent’s
actions X.
(1.3) Consider a game of chess. Is this a fully observable, partially observable, or unob- (3)
servable environment? Clearly explain your answer.
Fully observable X. The entire stateXcan be observed at each distinct state in the
state spaceX.
Question 2: 22 Marks
(2.1) List the 5 components that can be used to define a problem. (5)
1. Initial stateX
2. ActionsX
3. Transition modelX
2
, COS3751/201/1/2021
4. Goal testX
5. Path costX
(2.2) Differentiate between search space and goal space. (2)
The search space is the set of states that have to be searched for a solutionX,
whereas a goal space is a set of goal statesX.
(2.3) What is the purpose of the explored set? (1)
Avoids infinite loops since it holds the list of nodes that have already been explored.
X
(2.4) List and discuss three types of queues that may be employed in a search. (6)
1. FIFOX: used in DFS searches, nodes are added in reverse order to ensure that
the last node added will be the first node to be explored.X
2. LIFOX: Typically used in BFS searches: nodes are added in the order they are
generated.X
3. Priority queueX: Nodes are added and sorted based on some key, this ensures
that certain states take priority over others during the expansion phase.X
(2.5) List and explain the measures used to determine problem solving performance. (8)
1. CompletenessX: Will the algorithm find a solution if it exists? X
2. OptimalityX: Will the algorithm find the best solution (optimal path cost among
all solutions)? X
3. Time complexityX: How long does the algorithm take to find a solution? X
4. Space complexityX: How much memory is needed to perform the search for a
solution? X
3
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