work with mark: bottom-up approach

after some time mark d'inverno and i shifted focus and decided to simplify our ideas. we agreed to work more from a bottom-up approach - letting the agents live within a grid world and visualise their behaviours. other people have been doing quite some work in this area before, but not particularly many of them have been incorporating sound and music. so we had literature and examples to study and people to ask. it was of great help and i learned a lot about designing multi-agent systems from analysing for example jon mccormack's nice eden.

so starting out writing our own system, i did a set of classes for handling agents running around in a grid world of 1-3 dimensions. all agents were very simple minded. they were visually represented with just dots and oh, they could bleep too.
setting up simple scenarios for these classes helped to pinpoint different system models. it also showed my biggest problems coding this usually boiled down to in which order to do things. the model i tried in turn to 'model' was suggested by rob saunders in an article called 'smash, bam and cell', in were all agents first sense their surroundings and then act. but i constantly had to restructure the code and design. this was harder than i had thought and i think i never came up with an all around solution.

one example scenario we came up with was the runaway test. it is very simple but can help trying out different designs. it works something like this... imagine a grid world of say 2 dimensions (we also coded this in 1 and 3D). agents move about at random speed and direction. if an agent encounters another one blocking its next step, it turns around 180 degrees and flee i.e. moving away in the opposite direction. so far the sense/act cycle is simple: for every update (world tick) it first sense, then acts. but what happens if there's another agent blocking the escape route? so the agents really needs to first sense, then if something is ahead, act and turn around 180, sense again and decide if it is possible to flee. here it'll sense within the act method and that clutters the design. the better solution would probably be to let the agent just turn 180 and wait to flee until the next tick. but perhaps it could also sense behind itself in the first sense round and pause if both escape routes are blocked. there are many possible solutions and creating these small test scenarios helped me to generalise my classes. we also tried the classes by coding the test scenarios as discrete and continuous i.e. if the world was a rigid grid in which the agents only were allowed to move stepwise, or if the world allowed them to move about more smoothly in non-integer directions and speeds.

the supercollider code for version 4, including test scenarios and small examples is attached at the bottom of this post and below is some text trying to describe the classes in more detail.

also see these quicktime movies of some test scenarios...
bounce 1D 2 one dimensional continuous world.
bounce 2D 2 two dimensional continuous world.
runaway 1D 3 one dimensional discrete world.
runaway 1D 4 one dimensional discrete world.
runaway 1D 5 one dimensional discrete world.
runaway 1D 6 one dimensional discrete world.
runaway 2D 1 two dimensional discrete world.


A4 description
there are 3 basic classes. ALocation, AWorld and AnItem. i first describe them and their immediate subclasses. then AnAgent, AProxyAgent and some subclasses of AWorld. then i explain a few of the classes used in the test/example programs. last i write a little about the main loop.
may look at the files and for completion.


a place within a world.
.new takes 2 arguments: world and list
instance variable world is any (sub)class of AWorld. can be get (ie accessed from outside).
instance variable list is a list of coordinates. can be get and set. example: [10] for x=10 in an 1 dimensional world. [10, 20, 30] for x, y, z in a 3d world. the length of the list must correspond to the number of dimensions in the world.

locations can be compared with the == method. it takes another location as an argument. a location is equal to another if they exist in the same world and have the same list of coordinates.

the != method is the negation of ==.

distance between 2 locations can be found with the distance method. it'll return the shortest distance between locations in any dimension.

with the at method one can query single dimensions. eg. in a 2d world, will return which column and will return row. the argument is really just index in list above.

the surroundingLocations method. with arguments exclude(boolean) and area(int) returns a list of new location objects. this is used for collecting locations to be searched for neighbours. if exclude argument flag is false, this (ie current) location will be counted and included in the list. the locations returned are all positioned next to this location in a cube like way, covering an area of size: area steps away. to put it in another way: with an area of 1, only directly adjacent locations are returned. an area of 2 gives adjacent and their adjacent locations (as a set of ALocation objects) and so on.
so in a 1d world a location at (0) sent the message .surroundingLocations(false, 1) will give us [loc[-1], loc[0], loc[1]]. and likewise in a 2d world a location at (4, 5) sent the message .surroundingLocations(false, 1) will return [loc[3, 4], loc[3, 5], loc[3, 6], loc[4, 4], loc[4, 5], loc[4, 6], loc[5, 4], loc[5, 5], loc[5, 6]]. here's the code that resembles this: ALocation(AWorld(2, 10), [4, 5]).surroundingLocations(false, 1). last example: a location within a 3d world asked to return its surroundings with an area of 3 like this: ALocation(AWorld(3, 100), [40, 50]).surroundingLocations(false, 3).size will return a list of 343 unique locations.

when a location object is created it check its world's size and wrap around borders (by doing modulo(size) on the coordinates in the list).

the location class expects the world to be of uniform size in all dimensions.

050712 - distance might need to go in a subclass if we do networked worlds - rob's comment. how to calculate distance between worlds?
050712 - at robs suggestion: i'll try to rewrite this and the world classes using hashtable lookup. the matrix/location duality causes trouble keeping same thing at 2 places.
050712 - guess naming needs to be improved. specially AQLocation - what to call it?
050726 - removed the maxDimension and surroundMaxArea limitations and its classvariable
050726 - now hashtable lookup + c primitives. quite a lot faster overall and easier to keep things at the same place.

AQLocation - a quantified location. the coordinates for this class can be flotingpoint but when it places itself in the matrix it rounds of to nearest integer.


a placeholder for items. superclass for APattern, AGrid, ACube, BugWorld etc.
.new takes 3 arguments: dimensions, size and location
instance variable dimensions is an integer specifying the number of dimensions for this world. can be get.
instance variable size will decide size of 1 dimension. the world is then created with uniform size in all dimensions. can be get.
instance variable location if defined, will place the world at a location. if left out - no parent. can be get.

the clear method takes a location object as argument and puts a nil there.

with the remove method - argument: an item - you remove an item from this world.

with put you place an item in this world. argument: item

the get method returns whatever item(s) is in a location. argument: location

method items returns a list of all items in this world.

neighbours - arguments: item, exclude and area. returns a list of any items within an item's area (1=adjcent locations) including or excluding the item's own location. if no items nearby then empty list.

neighboursSparse is similar to neighbours above (same arguments and function) but uses a different algo for finding nearby items. where neighbours calculates locations around the item in question and then check these locations for any items, this method might be quicker in a sparse world. it looks through all items in the world and checks if they're nearby.

running the update method goes through all items in this world, copies them to their own locations. this is to make sure all item's locations and the hashtable stays in sync.

save will write this world, its current settings and all its items and their settings to disk. this allow for backup of longrunning tasks or 'presets' to be loaded.



AWorld2 - remove and put methods are modified to allow for multiple items at the same location. AWorld can only do one item in once location. hopefully i can merge the two classes later.

ASmartWorld - is a world that can resolve location conflicts. there's a resolve method to be called after the sense cycle but before act. this goes through all items and if more than one intend to move to the same location, only let one move - others stay put and their request is ignored. (comment: need to find a better name than ASmartWorld)

and many other subclasses. eg BugWorld, APattern, AGrid, ACube. almost every test program has its own specialised class inheriting from these two subclasses.


lowest level thing that exist in a world. abstract superclass class for ARock, AMoss, AnAgent
.new takes 1 argument: location

the method remove will remove this item from its world.

the abstract init method is used by some subclasses for initialisation.

050726 - is remove needed? will the agent remove itself or will the world handle that eg if energy=0.

ARock - does nothing different. just exists at a location
AMoss - has energy that can be get/set.
AnAgent - is an abstract class. see below


subclass of AnItem but is also an abstract class.
makes sure the sense and act methods are there for all agents to follow.

many. eg ACell, ARunaway, ABounce, Bug. every test program has its own specialised class inheriting from this one. they all do sense and act in their own way.

subclass of AnAgent. it allows to replace sense and act methods while running.

when asked to sense and act, sense and act in this class instead evaluates functions stored in the 2 class variables senseFunc and actFunc. these can be replaced and coded on the fly! so while the system is running, we can try out, completely rewrite or just slightly modify, behaviour for all agents. their state are kept (individually) but behaviour changes.

this is unique to other frameworks i've seen so far. i'd like to explore more and hopefully we can use it in practice too - not just as convenience for developing. with this feature it's easy to replace the rules on the fly.
perhaps i redesign the whole framework to use proxies. so the AnItem class is really a placeholder (proxy) for anything. then one can code whole agents with state and behaviour while running i think. and maybe proxy worlds too but i can't find a reason for that now.


a subclass of AWorld that has 1 dimension.


a subclass of AWorld that has 2 dimension.


a subclass of AWorld that has 3 dimension.

ACell - used in A4_test1_cellautomata.scd

subclass of AnAgent. it doesn't move and is used for cellular automatas and gameoflife.
instance variable value can be 0 or 1. can be get/set.
there's also a rules class variable that contains a dictionary for rule lookup.

the sense method here collects and stores values from nearby neighbours (by asking the world for neighbours) including the cell's own value.
the act method set the cell's own value to what is returned from the rules dictionary.

ALifeCell - used in A4_test2_gameoflife.scd

subclass of ACell. just implements different sense and act methods.

the sense method here is the same as ACell.sense but excludes the cell's own value.
act will first calculate the total sum of all neighbour's values and then do lookup in the rules dictionary. the cell's own values is set to 0 or 1 depending on what the dictionary returns.

ARunaway - used in A4_test3_runaway1D.scd, A4_test4_runaway2D.scd and A4_test8_runaway3D.scd

a subclass of AnAgent that sense if something at next location and if so, bleep, turn around 90 and flee.
instance variable direction is a list of directions in any dimension. in a 2D world: [0, 0] stand still, [-1, 0] go west, [1, 1] go northeast and so on. can be get/set
instance variable freq decides which bleep frequency to play.

the sense method updates the 2 private nextLocation and nextPos instance variables to figure out where to go and if that location is taken.
helper method clearAhead returns true if there's nothing in nextPos
getNextLocation returns a new location object at here + directionlist.
getNewDirection method turns directionlist around 90 degrees.
the move method sets this location to nextLocation
the play method will beep at a frequency. and pan the sound left/right depending on location.

ABounce1D - used in A4_test6_bounce1D.scd

subclass of ARunaway. implements getNextLocation and getNewDirection differently so that the agents bounces of eachother rather that turn 90 degrees.
direction is here a vector of angle and degree.

ABounce2D and ABounce3D for vector math in other dimensions.


the main loop of the program is usually very simple. for the ca and gameoflife examples it just draws a rectangle if the cell's value is 1, then call .sense on all agents and last call .act for all agents.

while {true}, {{|a| if(a.value==1, {a.paintRect})}{|a| a.sense};{|a| a.act};

agents that move around (ie all other examples) need to resolve conflicts and update the world. also they always draw themselves.

while {true}, {{|a| a.paintRect}{|a| a.sense};
        world.resolve;{|a| a.act};

update 061017: bugfix to the distance method in
update 171228: new helpfile and scd instead of rtf

Package icon A4.zip24.02 KB