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2.3. Using the Fig 2.1 below, draw the BFS tree (vertices and tree edges) that results when performing a BFS Traversal starting at node c, also draw DFS tree that results when performing a DFS traversal starting at node g. Include with each tree the traversal path: BFS traversal for BFS tree and preorder for Traversal for DFS tree. [20 Marks] Figure 1.2 6 g

2.3. Using the Fig 2.1 below, draw the BFS tree (vertices and tree edges) that results when performing a BFS Traversal starting at node c, also draw DFS tree that results when performing a DFS traversal starting at node g. Include with each tree the traversal path: BFS traversal for BFS tree and preorder for Traversal for DFS tree. [20 Marks] Figure 1.2 6 g

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module : AFI700

QUESTION 1 (30 MARKS)

1.1 Given a medical diagnosis expert system, the system uses the following rules for diagnosing whether a patient has the flu:

  • Rule 1: If the patient has a fever and cough, then the patient may have the flu.

  • Rule 2: If the patient has a sore throat, then the patient may have a cold.

  • Rule 3: If the patient has a fever, sore throat, and cough, then the patient has the flu.

Using the rules above, explain how the expert system would infer a diagnosis when the patient presents with a fever, cough, and sore throat.

  • How does the inference engine process these rules?

  • What challenges might arise in implementing this expert system? [15 Marks]

1.2 Consider a robot in an autonomous navigation task. The robot needs to navigate from one point to another while avoiding obstacles in its environment.

  • Describe how the robot would behave as a rational agent in this scenario, outlining the steps it would take to ensure it successfully reaches its destination.

  • Assume the robot has access to a map and sensors for real-time updates on its surroundings. How would the robot use this information to make rational decisions?

  • Discuss the possible trade-offs between exploring new paths and exploiting known safe paths in such a scenario. [15 Marks]

Note: All questions must be properly referenced using the in-text Harvard referencing style. Failure to adhere to this requirement will result in a score of zero.

QUESTION 2 (60 MARKS)

2.1 Consider the following two logical expressions involving quantifiers, which might be used to reason about knowledge in an AI system:

∀x(P(x)  ⟹  Q(x))≡∃x(−Q(x)  ⟹  −P(x))∀x(P(x)⟹Q(x))≡∃x(−Q(x)⟹−P(x))

  • Construct the truth tables for both expressions, where P(x)P(x) and Q(x)Q(x) are Boolean predicates that depend on the variable xx.

  • Consider that xx can take values from a finite set (e.g., x∈{1,2,3}x∈{1,2,3} and create the truth tables for each case.

  • Using the truth table approach, evaluate whether these two logical forms are logically equivalent, and explain the relationship between universal and existential quantifiers in this context.

  • Provide a detailed analysis of how such quantifier-based logic could be used in AI systems like automated theorem proving or knowledge representation. [20 Marks]

2.2 In dynamic environments where the knowledge base continuously evolves (e.g., in real-time systems or learning environments), axioms and predicates play an essential role in reasoning about changes and adapting to new information.

  • Discuss how axioms and predicates are used to represent and reason about dynamic environments in AI. Specifically, focus on how systems adapt their reasoning as new facts or rules are introduced, and how they handle changes in the environment that may invalidate or modify existing axioms.

  • Explore the relationship between static and dynamic knowledge bases, and consider the challenges involved in ensuring consistency and maintaining the integrity of logical reasoning when new information is continuously integrated. How can AI systems effectively reason under conditions of uncertainty or incomplete information in such environments?

  • Consider a scenario involving a real-time AI system, such as a self-driving car, a smart home system, or a recommendation engine. Describe how axioms and predicates are employed to model the dynamic relationships between entities within the system and how reasoning mechanisms allow the system to adjust to changing conditions. Focus on how the system handles contradictions or discrepancies between old and new information. What strategies can be used to ensure the system continues to function optimally despite these changes? [20 Marks]

2.3 in the image attatched

2.3. Using the Fig 2.1 below, draw the BFS tree (vertices and tree edges) that results when performing a BFS Traversal starting at node c, also draw DFS tree that results when performing a DFS traversal starting at node g. Include with each tree the traversal path: BFS traversal for BFS tree and preorder for Traversal for DFS tree. [20 Marks] Figure 1.2 6 g
Transcribed Image Text:2.3. Using the Fig 2.1 below, draw the BFS tree (vertices and tree edges) that results when performing a BFS Traversal starting at node c, also draw DFS tree that results when performing a DFS traversal starting at node g. Include with each tree the traversal path: BFS traversal for BFS tree and preorder for Traversal for DFS tree. [20 Marks] Figure 1.2 6 g
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