Graph building algorithm given an infinite walk - graph

Question

I need help writing a resilient, mapping (graph building) algorithm. Here is the problem:
Imagine you have a text oriented virtual reality(TORG/MUD) where you can send movement commands (n, s, w, e, up, down, in, out ... etc.) through telnet to move your character from room to room. And the server sends back corresponding room description after each movement step. Your task is to generate a graph that represents the underlying map structure, so that you can simply do a DFS on the client side to figure out how to get from one room to another. Also you want to design the system so that minimum user input is required
Here are the assumptions:
The underlying graph topology on the server never change.
Room descriptions are not unique. Most of the rooms have distinct descriptions, but some of the rooms have the exact same description. Room description are changed slightly once in a while(days or weeks)
Your movement may fail randomly with a small probability, and you will get an error message instead of the new room description, such as "You stop to wait for the wagon to pass", "The door is locked", and your character will still be in the current room.
You cannot assume the unit spacial distance for each movement. For example you may have a topology like the one shown below, so assuming unit distance for each neighboring room and assigning a hard coordinate to each room is not going to work. However you may assume that the relative direction to be consistent, that is there will be no loop in a topological sort along X(west, east) and Y(south, north).
Objective: given a destination that you have visited before, the algorithm guarantees to eventually move you to that location, and will find the shortest path most of the time. Mistakes are allowed, but the algorithm should be able to detect and correct the mistakes on the fly.
Example graph:
A--B---B
| | <- k
C--D-E-F
I have already implemented a very simple solution that would record the room descriptions and construct a graph. The following is an example of a graph representation my program generates in json. The "exits" are movement direction mapped to node id. -1 represents an un-mapped room. If the user walks in a direction and detect a -1 in the graph representation, the algorithm will attempt to find nodes already in the graph. If nodes with the same description are found, it will prompt the user to decide whether the newly seen room is one of the old nodes. If not, it adds a new node and connect it to the graph.
"node": [
{
"description": "You are standing in the heart of the Example city. There is a fountain with large marble statue in it...",
"exits": {
"east": -1,
"north": 31,
"south": 574,
"west": 42
},
"id": 0,
"name": "cot",
"tags": [],
"title": "Center of Town",
"title_color": "\u001b[1m\u001b[36m Center of Town\u001b[0;37;40m"
},
{
...
This simple solution requires human input detect loops when building the graph. For example, in the graph shown above, assume same letters represent same room descriptions. If you start mapping at the first B, and to left, down, right...till you perform movement k, now you see B again, but mapper cannot determine whether it is the B it has seen before.
In short I want to be able to write a resilient graph building algorithm that takes a walk (possibly infinite) in a hidden target graph and generate(and keep updating) a graph that can (hopefully) as similar as the target graph. We then use the generated graph to help navigate in the target graph. Is there an existing algorithm for this category of problems?
I also thought about applying some machine learning techniques to this problem, but I am unable to write out a concrete model. I am thinking along the lines of defining a list of features for each room we see (room description, exits, neighboring nodes), and each time we see a room we attempt to find the graph node that best fit the features, and based on some update rule(like Winnow or Perceptron) update the description we see based on some mistakes detection metrics.
Any thoughts/suggestions would be very much appreciated!

Answer 1

Many MU*s will give you a way to get a unique identifier for rooms. (On MUSH and its offshoots, that’s think %L.) Others might be set up to describe rooms you’ve already been to in an abbreviated form. If not, you need some way to determine whether you have been in a room before. A simple way would be to compute a hash of enough information about each room to get a unique key. However, a maze might be deliberately set up to trick you into thinking you’re in a different location. Wizardry in particular was designed to make old-school players mapping the dungeon ny hand tear their hair out when they realized their map had to be wrong, and the Zork series had a puzzle where objects you dropped to mark your place in the maze would get moved around while you were elsewhere. In practice, coding your tool to solve these puzzles is unlikely to be worth it.
You should be able to memoize a table of all-pairs-shortest-paths and update it to match the subgraph you’ve explored whenever you search a new exit. This could be implemented as a N×N table where row i, column j tells you the next step on the shortest path from location i to location j. Normally, for a directed graph. Even running Dijkstra’s algorithm each time should suffice, but in practice each move adds one room to the map and doesn’t add a shorter path between many other rooms. You would want to automatically map connections between rooms you’ve already been too (unless they’re supposed to be hidden) and not force the explorer to tediously walk through each individual exit and back to see where it goes.
If you can design the map, you can also lay out the areas so that they’re easy to navigate between, and then you can keep your tables small: each only needs to contain maps of individual areas you’ve deliberately laid out as mazes to explore. That is, if you want to go from one dungeon to another, you just need to look up the nearest exit, and then directions between the dungeons on the world map, not one huge table that grows quadratically with the number of locations in the entire game. For example, if you’ve laid out the world as a nested hierarchy where a room is in a building on a street in a neighborhood of a city in a region of a country on a continent on a planet, you can just store locations as a tree, and navigating from one area to the others is just a matter of walking up the tree until you reach a branch on the path to your destination.
I’m not sure how machine learning with neural networks or such would be helpful here; if the kind of trick you’re looking out for is the possibility that a room that appears to be the same as one you’ve seen before is really a duplicate, the way to handle that would be to maintain multiple possible maps at once on the assumption that apparently-identical rooms are or are not duplicates, a garden of forked paths.