Summary of Chapter 3 Continued

Iterative Deepening continued

• In essence, iterative deepening pretends that nodes at the cutoff limit, n, have no successors. If you fail to find a goal when the limit is n, use limit n+1 and repeat the process.

Iterative deepening continued

• IDS is complete and optimal
• Space complexity is O(bd)
• time complexity is a little worse that BFS or DFS but still O(bd)
• IDS is the preferred strategy when the search space is large and the solution depth is unknown

State space vs. search tree

Summary of uniformed search

• Recall the basic tree search algorithm; in this algorithm, the only place knowledge is applied is in the queueing function:
• BFS: new nodes are added to the end of the queue
• DFS: new nodes are added to the front of the queue
• Uniform-cost search: the queue is sorted according to path cost

Assignment

Your first programming assignment is to write a Reflex-Agent-With-State, as in Figure 2.10 of your text, to act in the Wumpus World. This is a relatively involved project and it is therefore broken for into sub-assignments as follows:

• Part 1: Due Wed. 11, 1998:
  Define a set of condition-action rules to guide the agent's choice of action at each step of the game
  
• In formulating these rules, you will use information from an internal state representation that you will write later. Indicate the required state information by designating the name of a function that will provide that information. The function name should be indicative of what that function returns. For example, a possible rule for the GRAB action is:

  if ( !holdGold() && CurrentPercept().isGlitter() )
  return GRAB;
  

  Identifying the information that you need from the internal state will help you design that state representation. Be realistic about the functions that you use in formulating your condition-action rules; do not write rules that use functions that would be exceeding difficult or impossible to write.

• Suggestion: Write one or more rules to trigger each of the Agent's possible actions. E.g., a rule that specifies when the agent should GRAB, a rule for CLIMB, one or more rules for GOFORWARD, etc. If it is possible for more that one rule to apply in a given configuration, you could order the rules in terms of priority so that the highest priority rules are triggered first.

• Part 2: Due Mon 16, 1998:
  Define the internal state representation.
  
• The internal state should be written as a class; in Part 2 you are asked to write the definition file (i.e., the ".h" file) for this class. The class should be written in such a way that state information can be retained from one trial to the next. In writing this file, be sure to think about how you will get the information that you need from the simulation code. Be sure to specify the correct types for all parameters.

• Part 3: Due Mon 23, 1998:
  Write the Reflex Agent.
  
• In your csci/373/wumpus directory create a sub-directory called ReflexAgent and copy the Makefile from the directory usr/local/csci/373/players/alpha/HumanPlayer into your new ReflexAgent directory. Use this directory to implement your ReflexAgent player. This player should use the State class from Part 2 to maintain an internal state. It should also use the condition-action rules from Part 1 to select the action that it will perform at each step. See the bullet entitled Requirements for your player notes, in the Feb 2 lecture notes for a discussion of the requirements for the ReflexAgent class. You should use the HumanPlayer class as a guide.
• Your ReflexAgent should compile using the Makefile specified above and run in the Wumpus World simulation. Your agent will be tested in multiple trials in environments ranging from 4 to 8 squares.