Introduction to Artificial Intelligence

Assignment 1


Environment simulator and agents for the Syrian traveler problem

In this first exercise you are asked to implement an environment simulator that runs a variant of the Canadian traveller problem (CTP). Then, you will implement some agents that live in the environment and evaluate their performance.

In the Canadian traveller problem, we are given a weighted graph, and the goal is to travel as cheaply as possible from a given start vertex to a given goal vertex. However, unlike standard shortest path problems in graphs, which have easy known solutions (e.g. the Dijkstra algorithm), here the problem is that some of the edges (an edge being an abstraction of a road in the real world) may be blocked by snow or ice, this being the Canadian traveller problem.

In the open research problem version of CTP, the agent can only tell which edges are blocked when visiting an incedent vertex. This leads to a problem of shortest path under uncertainty, with which we are not ready to deal yet. Instead, we will introduce a variant, called the Syria UN Inspectors traveller problem, with certain knowledge but also add ways to unblock paths, making this an interesting search problem. The UN chemical weapon inspection team is supposed to arrive at such sites, and then move chemical weapon materials (chems, for short) to pre-specified locations for removal or controlled destruction. However, the roads are unsafe, and terrorist groups will try to capture the chems on the way. Your task is to plan a path (and later on, a policy) to optimally perform the UN team's task.

Syrian TP Environment

The environment consists of a weighted graph. Each edge can be blocked by the terrorists or other insurgents (a rather common state of affairs in Syria lately), or clear. Some vertices have resevoirs of chems that must be transfered, and some have army units that can be attached to the UN inspector team in order to cross an edge successfully. The environment can contain one or more UN inspector teams (agents), each with a known starting location, and a required goal location. For each agent, there are state variables designating its current location, whether it is carrying chems, and whether it is currently escorted by a military unit.

An agent can apply 2 types of action: drive (like move in the standard CTP), and no-op. The no-op action does nothing. The drive action has 2 additional binary parameter options: carry chems and request escort. 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 reaches U. Side effects depend on the parameters: driving with chems moves the chems to U. Driving with escort also moves the military unit to U. The escort also makes a blocked edge clear, but only if not also carrying chems. If E is blocked by terrorists, and the agent is escorted, the agent succesfully traverses E, and again the agent reaches U, with a side-effect that the chems and the military escort forces are also at U. If the agent traverses a blocked edge carrying chems but unescorted, the terrorists obtain the chems and hell breaks loose! Finally, the agent can traverse a blocked edge if not carrying chems (these terrorists only want the chems). The cost of actions is as follows: no-op has a small cost epsilon determined by the user. drive has a cost equal to the edge weight, doubled for each true value of each of the parameters (and thus quadruple for driving escorted with chems). But driving unescorted with chems across a blocked edge has a cost HUGE determined by the user (the cost of hell breaking loose...).

The simulator should keep track of score, i.e. the number of actions done by each agent, and the total cost encurred by the agent for traversing edges, etc.

Implementation part I: simulator + simple agents

Initially you will implement the environment simulator, and several simple (non-AI) agents. The environment simulator should start up by reading the graph from a file, in a format of your choice. We suggest using a simple adjancency list in an ASCII file, that initially specifies the number of vertices. For example (comments beginning with a semicolon):

#V 4    ; number of vertices n in graph (from 1 to n)

#E 1 2 W1 C   ; Edge from vertex 1 to vertex 2, weight 1, clear
#E 3 4 W1 B   ; Edge from vertex 3 to vertex 4, weight 1, blocked
#E 2 3 W1 B   ; Edge from vertex 2 to vertex 3, weight 1, blocked
#E 1 3 W4 C   ; Edge from vertex 1 to vertex 3, weight 4, clear
#E 2 4 W5 C   ; Edge from vertex 2 to vertex 4, weight 5, clear

#C 1 1  ; One unit of chems at vertex 1
#M 2 1  ; One militry escort unit at vertex 2

The simulator should query the user about the number of agents and what agent program to use for each of them, from a list defined below. Initialization parameters for each agent (initial and goal position) are also to be queried from the user.

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 state of the world after each step, with the appropriate state of the agents and their score. A simple screen display in ASCII is sufficient (no bonuses for fancy graphics - this is not the point in this course!).

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 (for example, a computed optimal path, or anything else desired) if needed. In this assignment, the agents can observe the entire state of the world.

You should implement the following agents:

  1. A human agent, i.e. read the next move from the user, and return it to the simulator.
  2. A terrorist buster automaton. This agent will work as follows: if at a node with military unit, find the terrorist-blocked edge that can be reached with least cost, and move towards it (with escort) in the least-cost path. Otherwise, first get to a vertex with military units. Ties are broken in favor of always going for lower-numbered vertices. If neither action is possible, does no-op.
  3. A greedy agent, that works as follows: the agent should compute the shortest currently unblocked path to its target, and follow it, carrying chems if possible. If there is no such path, do no-op.

At this stage, you should run the environment with two agents participating in each run: terrorist buster, and one other agent that can be chosen by the user. Your program should display and record the scores. In particular, you should run the greedy agent with various initial configurations, and various initial locations of the terrorist buster automaton. Also, test your environment with several agents in the same run, to see that it works correctly. You would be advised to do a good job here w.r.t. program extensibility, modularity, etc. much of this code may be used for some of the following assignments as well.

Implementation part II: search agents

Now, after chapter 4, you will be implementing intelligent agents (this is part 2 of the assignment) that need to act in this environment. Each agent should assume that it is acting alone, regardless of whether it is true. You will be implementing a "search" agent as defined below. All the algorithms will use a heuristic evaluation function of your choice.

  1. A greedy search agent, that picks the move with best immediate heuristic value.
  2. An agent using A* search, with the same heuristic.
  3. An agent using a simplified version of real-time A*.

The performance measure will be composed of two parts: S, the agent's score, and T, the number of search expansion steps performed by the search algorithm. The performance of an agent will be:

   P = f * S + T

Clearly, a better agent will have P as small as possible. The parameter f is a weight constant. You should compare the performance of the three agents (each acting alone) for the following values of f: 1, 100, 10000. Note that the higher the f parameter, the more important it is to expend computational resources in order to get a better score!

Bonus version: construct a search agent as above, but in addition allow one terrorist buster also acting in the environment. Your search agent needs to take this into account. Observe that although this seems to be a multi-agent problem, the fact that the terrorist buster is perfectly predictable makes this in essence a single agent search problem.

Addtional bonus - theoretical (new): What is the computational complexity of the Syrian Traveler Problem (single agent)? This is a minor seemingly open problem. Can you prove that it is NP-hard? Or is it in P? If the latter, can you design an algorithm that solves the problem in polynomial time?

Deliverables

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. A document stating the heuristic function you used and the rationale for selecting this function. 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 for part 1 (recommended): Thursday, October 31.

For the complete assignment: Monday, November 11.