CS607 Midterm Solved Subjective with References by Moaaz

Solved Subjective · From Midterm Papers · 19 – May - 2013 · PSMD01 · Fall 2012

Q1. Differentiate between Mutation and Crossover. (2 Marks)

Answer: (Page 81) In Mutation, each “Individual” (or “solution”) has one parent. In Inheritance or Crossover, each “Individual” (or “solution”) has two parents.

Q2. Write CLIPS command for "remove only Facts”. (2 Marks)

Answer: (Page 134) The retract command is used to remove or retract facts. For example: (retract 1) removes fact 1 (retract 1 3) removes fact 1 and 3

Q3. Bike is heavy! This statement is uncertain fact or not? Elaborate. (3 Marks)

Answer: (Page 94) No, this statement belongs to Fuzzy facts. Fuzzy facts are ambiguous in nature, e.g. the book is heavy/light. Here it is unclear what heavy means because it is a subjective description. Fuzzy representation is used for such facts. While defining fuzzy facts, we use certainty factor values to specify value of “truth”. We will look at fuzzy representation in more detail later.

Q4. Elaborate the importance of Knowledge Base in ES. (3 Marks)

Answer: (Page 117) The knowledge base is the part of an expert system that contains the domain knowledge, i.e.

• Problem facts, rules
• Concepts
• Relationships

As we have emphasized several times, the power of an ES lies to a large extent in its richness of knowledge. Therefore, one of the prime roles of the expert system designer is to act as a knowledge engineer. As a knowledge engineer, the designer must overcome the knowledge acquisition bottleneck and find an effective way to get information from the expert and encode it in the knowledge base, using one of the knowledge representation techniques we discussed in KRR.

Q5. Conflict Resolution Strategies (5 Marks)

Answer: (Page 126) To overcome the conflict problem stated above, we may choose to use on of the following conflict resolution strategies:

• Fire first rule in sequence (rule ordering in list). Using this strategy all the rules in the list are ordered (the ordering imposes prioritization). When more than one rule matches, we simply fire the first in the sequence
• Assign rule priorities (rule ordering by importance). Using this approach we assign explicit priorities to rules to allow conflict resolution.
• More specific rules (more premises) are preferred over general rules. This strategy is based on the observation that a rule with more premises, in a sense, more evidence or votes from its premises, therefore it should be fired in preference to a rule that has less premises.
• Prefer rules whose premises were added more recently to WM (timestamping). This allows prioritizing recently added facts over older facts.
• Parallel Strategy (view-points). Using this strategy, we do not actually resolve the conflict by selecting one rule to fire. Instead, we branch out our execution into a tree, with each branch operation in parallel on multiple threads of reasoning. This allows us to maintain multiple view-points on the argument concurrently

Q6. "Riding a Horse is same as Riding a Donkey" , This Statement belongs which reasoning...elaborate it (5 Marks)

Answer: (Page 89) This statement belongs to Knowledge Representation and Reasoning. Knowledge representation (KR) and reasoning are closely coupled components; each is intrinsically tied to the other. A representation scheme is not meaningful on its own; it must be useful and helpful in achieve certain tasks. The same information may be represented in many different ways, depending on how you want to use that information. For example, in mathematics, if we want to solve problems about ratios, we would most likely use algebra, but we could also use simple hand drawn symbols. To say half of something, you could use 0.5x or you could draw a picture of the object with half of it colored differently. Both would convey the same information but the former is more compact and useful in complex scenarios where you want to perform reasoning on the information. It is important at this point to understand how knowledge representation and reasoning are interdependent components, and as AI system designer, you have to consider this relationship when coming up with any solution.

Fall 2012

1. GA using mutation procedure of 32-bit word that have first 16 bits 0s and last 16-bits 1s

Q2. step by step procedure of backward chaining (5 marks)

Answer: (Page 126)

1. Start with the goal.
2. Goal may be in WM initially, so check and you are done if found!
3. If not, then search for goal in the THEN part of the rules (match conclusions, rather than premises). This type of rule is called goal rule.
4. Check to see if the goal rule‟s premises are listed in the working memory.
5. Premises not listed become sub-goals to prove.
6. Process continues in a recursive fashion until a premise is found that is not supported by a rule, i.e. a premise is called a primitive, if it cannot be concluded by any rule
7. When a primitive is found, ask user for information about it. Back track and use this information to prove sub-goals and subsequently the goal.
8. how knowledge representation and reasoning is closely coupled and independent too in AI cycle (3

Marks)

Answer: (Page 89) Rep

to the other. A representation scheme is not meaningful on its own; it must be useful and helpful in achieve certain tasks. The same information may be represented in many different ways, depending on how you want to use that information. For example, in mathematics, if we want to solve problems about ratios, we would most likely use algebra, but we could also use simple hand drawn symbols. To say half of something, you could use 0.5x or you could draw a picture of the object with half of it colored differently. Both would convey the same information but the former is more compact and useful in complex scenarios where you want to perform reasoning on the information. It is important at this point to understand how knowledge representation and reasoning are interdependent components, and as AI system designer, you have to consider this relationship when coming up with any solution.

Q2. structure of Expert system explain the analogy with the real world example (3 marks)

Answer: (Page 115) Having discussed the scenarios and applications in which expert systems may be useful, let us delve into the structure of expert systems. To facilitate this, we use the analogy of an expert (say a doctor) solving a problem. The expert has the following:

• Focused area of expertise
• Specialized Knowledge (Long-term Memory, LTM)
• Case facts (Short-term Memory, STM)
• Reasons with these to form new knowledge
• Solves the given problem