CMSC 471

Artificial Intelligence -- Fall 2011

out 11/1/11; due 11/18/11

Submit homework in class Thursday 11/17, or send a PDF to Yasaman by Friday 11/18 at 2:30 p.m.

1. Planning Representations (25 pts.)

Our agent is hungry and unhappy, and wants to be happy and not hungry (goal state). Our agent starts out rich, but only has limited options, some of which will consume a substantial part of its wealth.

(a) (7 pts.) Describe only the Happy predicate using the situation calculus. You should have one or more possibility axioms (one for each relevant action)  and one or more successor-state axioms (one for each relevant action). These axioms should characterize how the state of the Happy predicate changes as a result of the actions in the domain, in terms of the domain predicates listed above.

(b) (7 pts.) Describe the actions in this domain as STRIPS operators. Be sure to include all preconditions, add lists, and delete lists. 

(c) (5 pts.) Show two different legal plans (sequences of actions) for achieving the goal described above from the given initial state.

(d) (6 pts.) How many legal plans are there for this goal? Explain your answer.  Does the answer change depending on whether or not repeated states are allowed?

2. Partial-Order Planning (25 pts.)

Suppose that the agent starts building a partial-order plan to achieve the goal in the domain from problem #1. The agent decides to drive to Ocean City and eat at Burger King. Draw the partial-order plan at this point in the planning process. You do not need to show the dependencies associated with static predicates. Show all dependencies (ordering links and causal links) associated with dynamic predicates. Ordering links should be drawn as a thin, single arrow; causal links, as a double or thick arrow.

Now suppose that the agent decides to satisfy its Happiness goal using the Sunbathe action. Insert this action into the plan, showing all dependencies. Will this plan succeed? If so, complete the partial plan, resolving any threats that arise. If not, complete the partial plan to the point where planning fails, and explain the source of the failure.

3. Bayesian networks and probability (50 pts.)

A CMSC 471 student notices that people who drive SUVs (S) consume large amounts of gas (G) and are involved in more accidents (A) than the national average.  She has constructed the following Bayesian network:

(a) (5 pts) Compute P(a, ~s, g) using the chain rule.

(b) (8 pts.) Compute P(a) using inference by enumeration.

(c) (9 pts.) Using conditional independence, compute P(~g, a | s) and P(~g, a | ~s).  Then use Bayes' rule to compute P(s | ~g, a).

(d) (5 pts.) The enterprising student notices that two types of people drive SUVs: people from California (C) and people with large families (F). After collecting some statistics, the student arrives at the BN:

Using the chain rule, compute the probability P(~g, a, s, c, ~f).

(e) (5 pts) Write, in summation form, the formula for computing P(a, ~f) using inference by enumeration.  (You do not need to actually compute the probability.)

(f) (4 pts) What is the conditional independence assumption for a node in a BN?

(g) (4 pts) When are two variables conditionally independent of each other in a BN?

(h) (10 pts) Using the rules for determining when two variables are (conditionally) independent of each other in a BN, answer the following (T/F) for the BN given in (c):

  1. I(C,G)
  2. I(F,A | S)
  3. I(C,F)
  4. I(C,F | S)
  5. I(C,F | A)