CSC266 · TU past paper
Artificial Intelligence 2080 question paper
The complete TU 2080 exam paper for Artificial Intelligence (CSC266), all 12 questions with solved model answers written to the mark scheme.
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- 110 marksNumericalInformed SearchHideAnswer
State Space Graph and Greedy Best-First Search Analysis
Initial State: $$\begin{array}{cc}\hline 1 & 2 \\hline \ & \ \\hline \end{array}$$ Goal State: $$\begin{array}{cc}\hline \ & 1 \\hline 2 & \ \\hline \end{array}$$ - Grid: 2×2, with positions labelled TL (top-left), TR (top-right), BL...
- 210 marksNumericalPredicate LogicHideAnswer
Write the rules to convert statements in predicate logic into CNF form. Convert the following sentences into FOPL. All students of BSC CSIT are intelligent person. All friends of intelligent person are smart. Laxmi is a friend of Rojina. Rojina is smart. All beautiful students are girl. Laxmi is beautiful. Using resolution algorithm infer that 'Laxmi is smart'.[10]
Converting Statements to CNF and Resolution in FOPL
Part 1: Rules to Convert FOPL into CNF
- Eliminate implications and biconditionals: replace $P \to Q$ with $\neg P \lor Q$, and $P \leftrightarrow Q$ with $(\neg P \lor Q) \land (\neg Q \lor P)$.
- Move negation inward (De Morgan / quantifier duals):
- $\neg(P \lor Q) \equiv \neg P \land \neg Q$
- $\neg(P \land Q) \equiv \neg P \lor \neg Q$
- $\neg \forall x,P(x) \equiv \exists x,\neg P(x)$
- $\neg \exists x,P(x) \equiv \forall x,\neg P(x)$
- $\neg\neg P \equiv P$
- Standardize variables apart: rename so each quantifier binds a unique variable.
- Move quantifiers to front (prenex form).
- Skolemize: replace existential variables with Skolem constants (no enclosing $\forall$) or Skolem functions of the enclosing universal variables.
- Drop universal quantifiers (remaining variables are implicitly universal).
- Distribute $\lor$ over $\land$: $P \lor (Q \land R) \equiv (P \lor Q) \land (P \lor R)$.
- Write as a set of clauses (each conjunct is a clause), renaming variables apart across clauses.
Part 2: FOPL Representation
Predicates:
- $CSIT(x)$: $x$ is a student of BSc CSIT
- $Intel(x)$: $x$ is an intelligent person
- $Friend(x,y)$: $x$ is a friend of $y$
- $Smart(x)$: $x$ is smart
- $Beautiful(x)$: $x$ is beautiful
- $Girl(x)$: $x$ is a girl
Sentence FOPL All students of BSc CSIT are intelligent $\forall x,(CSIT(x) \to Intel(x))$ All friends of an intelligent person are smart $\forall x,\forall y,((Intel(y) \land Friend(x,y)) \to Smart(x))$ Laxmi is a friend of Rojina $Friend(Laxmi, Rojina)$ Rojina is smart $Smart(Rojina)$ All beautiful students are girls $\forall x,((Beautiful(x) \land CSIT(x)) \to Girl(x))$ Laxmi is beautiful $Beautiful(Laxmi)$ Important data note. The conclusion "Laxmi is smart" uses the rule "all friends of an intelligent person are smart" plus "Laxmi is a friend of Rojina." So we need $Rojina$ to be an intelligent person. This requires $Rojina$ to be a BSc CSIT student, i.e. $CSIT(Rojina)$. This fact is NOT stated in the question. The listed facts ($Smart(Rojina)$, $Beautiful(Laxmi)$, "beautiful students are girls") do not lead to $Smart(Laxmi)$. As written, the proof is not derivable.
To make the intended chain work, the standard exam assumption is that Rojina is a BSc CSIT student ($CSIT(Rojina)$). I proceed with that assumption stated explicitly; without it, the inference fails.
Part 3: Convert to CNF (Clause Form)
- C1: $\neg CSIT(x) \lor Intel(x)$ (from sentence 1)
- C2: $\neg Intel(y) \lor \neg Friend(x,y) \lor Smart(x)$ (from sentence 2)
- C3: $Friend(Laxmi, Rojina)$
- C4: $Smart(Rojina)$
- C5: $\neg Beautiful(z) \lor \neg CSIT(z) \lor Girl(z)$ (from sentence 5)
- C6: $Beautiful(Laxmi)$
- C7 (assumed): $CSIT(Rojina)$
Part 4: Resolution to Infer "Laxmi is Smart"
Negated goal (C8): $\neg Smart(Laxmi)$
Step 1. Resolve C2 with C8, unifier ${x/Laxmi}$: $$\neg Intel(y) \lor \neg Friend(Laxmi, y) \lor Smart(Laxmi) ;\text{ and }; \neg Smart(Laxmi)$$ $$\Rightarrow \textbf{C9: } \neg Intel(y) \lor \neg Friend(Laxmi, y)$$
Step 2. Resolve C9 with C3 $Friend(Laxmi, Rojina)$, unifier ${y/Rojina}$: $$\Rightarrow \textbf{C10: } \neg Intel(Rojina)$$
Step 3. Resolve C10 with C1 $\neg CSIT(x) \lor Intel(x)$, unifier ${x/Rojina}$: $$\Rightarrow \textbf{C11: } \neg CSIT(Rojina)$$
Step 4. Resolve C11 with C7 $CSIT(Rojina)$: $$\Rightarrow \textbf{C12: } \square ;\text{(empty clause)}$$
Since we derived the empty clause (contradiction), the negated goal is false, so the original goal holds: $$\boxed{Smart(Laxmi) \text{ is TRUE}}$$
Caveat: This proof depends on the added premise $CSIT(Rojina)$. With only the six sentences exactly as given (no $CSIT(Rojina)$), the empty clause cannot be derived and "Laxmi is smart" is not provable. The clauses C5, C6 (beauty/girl) are irrelevant to this conclusion.
- 310 marksLearning with Neural NetworksHideAnswer
What is the role of activation function in ANN. How sigmoid function works? Discuss about perceptron learning.[10]
--- An activation function determines the output of a neuron given its input. It introduces non-linearity into the network, enabling ANN to learn complex patterns. Role Description ------------------- Non-linearity Allows the network to ...
- 45 marksAI PerspectivesHideAnswer
What is Turing Test? What properties an agent should have to pass the Turing Test? [5]
Turing Test and Properties Required to Pass It
What is the Turing Test?
The Turing Test was proposed by Alan Turing in 1950 as a way to determine whether a machine can exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human.
Setup of the Turing Test
The test involves three participants:
- A human interrogator (C) who communicates via text only
- A human respondent (A)
- A machine/computer (B)
The interrogator asks questions to both A and B through a terminal (without seeing them). If the interrogator cannot reliably distinguish the machine from the human based on the responses, the machine is said to have passed the Turing Test and is considered to be exhibiting intelligent behaviour.
In simple terms: If a machine can think and respond like a human, it is considered intelligent.
Properties an Agent Should Have to Pass the Turing Test
To pass the Turing Test, an agent must possess the following capabilities:
1. Natural Language Processing (NLP)
- The agent must be able to understand and communicate in human language (e.g., English).
- It should parse questions, understand context, and generate grammatically correct, meaningful responses.
2. Knowledge Representation
- The agent must store and manage a large knowledge base about the world.
- A knowledge base is "a collection of knowledge" that is updated periodically as the agent acquires new knowledge.
3. Automated Reasoning
- The agent must use stored knowledge to draw valid conclusions and answer new questions.
- It must handle logical inference and reasoning under uncertainty using tools like probability theory.
4. Machine Learning
- The agent must have the ability to learn from previous experience and adapt its behaviour.
- As stated in the notes: "An agent has ability to learn from previous experience and to successively adapt its own behaviour to the environment."
- This includes supervised learning and other learning paradigms.
5. Reactivity
- The agent must be capable of reacting appropriately to information from its environment (the interrogator's questions).
- It should respond in a timely and contextually relevant manner.
6. Computer Vision (for Total Turing Test)
- In the extended or Total Turing Test, the agent must also perceive and interpret images or video, requiring computer vision capabilities.
7. Robotics (for Total Turing Test)
- The agent must be able to manipulate objects and move purposefully, demonstrating physical intelligence.
Summary Table
Property Purpose Natural Language Processing Understand and generate human language Knowledge Representation Store and manage world knowledge Automated Reasoning Draw conclusions from knowledge Machine Learning Adapt and improve from experience Reactivity Respond appropriately to inputs Computer Vision Perceive visual inputs (Total Turing Test) Robotics Physical interaction (Total Turing Test)
Conclusion: The Turing Test remains a foundational benchmark in AI. An agent that successfully integrates NLP, knowledge representation, reasoning, and learning can convincingly mimic human intelligence and pass the test.
- 55 marksProperties of Intelligent AgentsHideAnswer
What are the properties of intelligent agent? How simple reflex agents work? Give an example of simple reflex agent. [5]
Intelligent agents possess the following properties, classified into internal and external characteristics: Property Description ------ Learning An agent has the ability to learn from previous experience and successively adapt its own be...
- 65 marksAlpha-Beta PruningHideAnswer
Why alpha beta pruning is necessary? How alpha beta pruning is done in game search, illustrate with an example. [5]
In a standard Minimax game tree, every node must be evaluated, which leads to exponential time complexity of O(b^m) where b is the branching factor and m is the maximum depth. This becomes computationally infeasible for games like chess ...
- 75 marksTypes of Knowledge Representation SystemsHideAnswer
How knowledge is represented using frames? Represent following knowledge using frames. Ram is name of an employee. His age is 27. He is male. He belongs to the department HR, where the number of employees is 110 and the average salary of the department is Rs. 45000. All departments are under Tribhuvan University. The organization type of Tribhuvan University is Educational. [5]
A frame is a data structure used to represent stereotyped situations or objects. Each frame consists of: - Frame Name: The name of the object or concept being represented - Slots: Attributes or properties of the object - Fillers: Values ...
- 85 marksLearning by Genetic AlgorithmHideAnswer
Define selection, crossover, and mutation operations in genetic algorithm. [5]
--- Selection is the operator that determines which individuals from the current population are chosen as parents to produce the next generation. - Individuals with higher fitness scores are given preference and are more likely to pass t...
- 95 marksPEAS description of AgentsHideAnswer
Using your own assumptions, design PEAS framework for following intelligent agents. a. Covid-19 prediction system b. Vaccine recommender system. [5]
PEAS stands for Performance Measure, Environment, Actuators, and Sensors. To design a rational agent, we must specify its task environment using the PEAS description. (Reference: "To design a rational agent we must specify its task envir...
- 105 marksExpert SystemsHideAnswer
Describe the components of expert system. [5]
An expert system is a computer program designed to solve complex problems and provide decision-making ability like a human expert. It extracts knowledge from its knowledge base using reasoning and inference rules according to user querie...
- 115 marksNatural Language ProcessingHideAnswer
Why pragmatic analysis is necessary in NLP? How pragmatic analysis is done? [5]
Pragmatic analysis is necessary in NLP because language is not always used literally. The same sentence can mean different things depending on the context, situation, speaker's intent, and background knowledge. Without pragmatic analysis...
- 125 marksUninformed SearchHideAnswer
How iterative deepening search is used to find path from initial state to goal state in state space representation of any problem? Illustrate with an example. [5]
Iterative Deepening First Search (IDFS) is a combination of Breadth First Search (BFS) and Depth First Search (DFS) that achieves: - The completeness and optimality of BFS - The low memory requirement of DFS In this strategy, a depth-lim...