In the second exercise you will be using the environment simulator from the first assignment, the American 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 speed nut agent.
As before, the environment consists of an undirected weighted graph. Each edge can be either blocked by a flood or clear. Vertices are known to contain vehicles. 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 vehicle it is currently driving.
An agent can apply 2 types of action: drive (like move in the standard CTP), and switch vehicle (the litter takes time Tswitch). The results of a drive (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 (and the vehicle the agent is driving, of course) reaches U. If E is blocked by a flood, and the agent is in an amphibious vehicle, agent traverses E, and again the agent and vehicle reach U. However, if E is blocked but the agent is not in an amphibious vehicle, the action fails, and the agent and vehicle remains at V. The switch action which places the agent in another car in the same vertex, if any is available.
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).
The simulator should query the user about the parameters, the type of game (see below) as well as other initialization parameters for each agent (Tswitch, 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:
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: Friday, December 16.