Introduction to Artificial Intelligence

Assignment 2

Adversarial games for agents in the Russian traveller problem

In the second exercise you will be using the environment simulator from the first assignment, the Russian traveller problem, as a platform for implementing intelligent adversarial agents. The environment is the same as before, except that now we will assume exacrly one opposing agent - an intelligent agent, rather than the dumb automaton such as the ice removing / fire extinguisher agent.

Game Environment

As before, the environment consists of an undirected weighted graph. Each edge can be either blocked by ice,blocked by fire, or clear. Some vertices are known to contain a supply of salt or water. The environment can contain one or more agents, each with a known starting location, and a required goal location. For each agent, there are state variables designating it current location, and the amount of salt and water it is carrying, initially zero.

An agent can apply 2 types of action: move (as in the standard CTP), and load salt or water. The result of this action is that the amount of salt (resp. water) that the agent is carrying is increased to 1, if the vertex at which load is executed contains this resource. The results of a move (from a current vertex V to a specific adjacent vertex U) action is as follows. If the edge E to be traversed is clear, the agent reaches U. If E is blocked by ice, and the agent carries salt, the salt is deposited on the ice as the agent traverses E, and again the agent reaches U. Side effects of this action are that E is now clear, and the agent no longer carries salt. However, if E is blocked but the agent does not have salt, the action fails, and the agent remains at V. A similar result applies for edges blocked by fire, requiring a water container to pass. In this case the water is used up, but although the agent can pass the edge once, the fire re-ignites and the edge stays on fire afterwards (this is a change from the previous assignment).

Note that in this assignment the agents can be completely adversarial, or semi-cooperating, as discussed below. We will also assume that a user defined horizon T exists, the game stops after T moves by each agent. An agent that has not achieved its goal after this number of steps incurs a penalty of F units (use 100 as a default penalty).

Implementation Steps

The simulator should query the user about the parameters, the type of game (see below) as well as other initialization parameters for each agent (type of agent, position, and goal).

After the above initialization, the simulator should run each agent in turn, performing the actions retured by the agents, and update the world accordingly. Additionally, the simulator should be capable of displaying the world status after each step, with the appropriate state of the agents and their score. Here there are two types of agent programs: human (i.e. read input from keyboard) and game tree search agent.

Each agent program (a function) works as follows. The agent is called by the simulator, together with a set of observations. The agent returns a move to be carried out in the current world state. The agent is allowed to keep an internal state if needed. In this assignment, the agents can observe the entire state of the world. You should support the following types of games:

  1. A zero sum game. The score of an agent is cost of its moves (including penalty if applicable) minus the cost of moves for the opposing agent. ("optimal" agent using mini-max, with alpha-beta pruning).
  2. A non zero-sum game, same as above but the agent score is just the cost of its moves, including penalty if applicable ("optimal" agent using maxi-max). Ties are broken cooperatively.

Since the game tree will usually be too big to reach terminal position in the search, you should also implement a cutoff, and a heuristic static evaluation function for each game. You may use the same heuristic for all games, if you think this is justified.


The program and code sent to the grader, by e-mail or otherwise as specified by the grader, a printout of the code and results. You need to show example scenarios where the optimal behavior differs for the 2 kinds of games (you will need to make the example scenarios very small in order to be able to reach terminal states in the search). A description of your heuristic evaluation functions and their rationale. Set up a time for frontal grading checking of the delivered assignment, in which both members of each team must demonstrate at least some familiarity with their program.

Due date: December 13, 2010.