CSC 411: Artificial Intelligence

 (Fall 2006)

 

Assignment 4: Chapters 8, 9

Due by November 30, Thursday, 2006

 

  1. Expert systems: In Section 8.2 we introduced the rule-based expert systems, especially production systems.  A toy example of production systems is developed for diagnosing automobile problems, where 4 production rules are identified. To build a real-life expert system, the development team may have multiple persons to extract and convert more production rules and decide the system control strategy. For this end,
    1. Identify possible knowledge engineers, domain experts, and potential end users for such an application, and discuss the expectations, abilities, and needs of each of these groups;
    2. Create 10 to 15 if … then … rules in English or pseudocode (other than those prescribed in Section 8.2) to describe relations within this domain, and create a graph to represent the relationships among these rules (a and/or graph, refer Section 3.3);
    3. Recommend and justify your answers to the following questions of choosing control strategies: data-driven or goal-driven search? Breadth-first or depth-first search? In what ways could heuristics assist the search?

 

  1. Planning: In Section 8.4 we introduced planning and used blocks world example to develop the issues of planning, including state representation and transition, atomic actions, and frame problem. Carefully read this section and answer the following questions:
    1. In page 317 we created two frame rules 8 and 9. Create the remaining frame axioms (rules) necessary for the four atomic actions pickup, putdown, stack, and unstuck described in rules 4 through 7 in the same page;
    2. Use the atomic actions and frame rules that you created to generate the search space of Figure 8.19;
    3. Show two more incompatible (precondition) subgoals in the blocks world atomic actions of Figure 8.19.

 

  1. CF Algebra: In Section 9.2.1, we introduced the Stanford Certainty Factor Algebra for uncertainty information processing. Given the following rules in a “back-chaining” expert system application:

A Ù not(B) è C (0.9)

C Ú D è E (0.75)

F è A (0.6)

G è D (0.8)

The system can conclude the following facts (with confidences):

F(0.9)

B(-0.8)

G(0.7)

Use the Stanford certainty factor algebra to determine E and its confidence.

 

  1. Fuzzy Sets: In Section 9.2.2 we introduced the fuzzy sets and applications in control system design – fuzzy controller. Carefully read this section and answer the following questions:
    1. Continue the inverted pendulum example in page 354 with two more iterations of the controller where the output of one iteration is the input value for the next iteration.
    2. Identify another application area where fuzzy control might be appropriate. Present a set of fuzzy rules for this domain.