today i sat on a discussion between Jay and p. and been refreshed for the fact that i should have compiled the spatial allocation models and algorithms data into a first draft. well, so where is my first draft? the answer is, i keep delaying it since got quite many concepts in various field needed to read into. but for the moment, i think its enough. what i got at the moment already satisfying my thirst in querying. since what they say anyway, is when you write, you know what's missing and thus you can push your own knowledge boundary further.

## Thursday, July 17, 2003

## Wednesday, July 16, 2003

Glossaries needed:

1. space syntax by bjorn Klarqvist

2. celullar automaton by paul callahan

3. classifier system by a.m. barry

4. swarming agent by

then, my own glossary

next week task: need to write the voronoi and basic agent codes

1. space syntax by bjorn Klarqvist

2. celullar automaton by paul callahan

3. classifier system by a.m. barry

4. swarming agent by

then, my own glossary

next week task: need to write the voronoi and basic agent codes

what i got at the moment:

1. voronoi working superbly in autocad environment.

the home work is to fix boundary problem, which in same way should also solve "the void" problem.

2. basic brownian swarming agent

this is still so basic, i haven't got to code the agglomeration process. the interpretation is that socially functioning space in the widest space syntax sense is voids' occupation by at least two agents. each agent at the moment has some definition of visual field, but i haven't found a "traffic" in any of paul's swarming code. so this should be another agenda to do.

the home work are to get agglomeration

to manifest idea of traffic in swarming system

thus, yesterday paul reminded me to "get back to what we're doing"

this means, he wants

1. we got voronoi, so what?

my reply to this should be: we know voronoi have been widely used for many kinds of purposes, and it has many useful definition attach to it. voronoi used within the project because it generates divisioning of space, - so useful since each division (or we call it cell) is actually a convex space - of free form. it is actually so similar to space syntax convex map. what is missing in voronoi is how to show connection between these conves spaces.

what to do then is to rule the agent so its now permeable from all sides of voronoi cell.

1. voronoi working superbly in autocad environment.

the home work is to fix boundary problem, which in same way should also solve "the void" problem.

2. basic brownian swarming agent

this is still so basic, i haven't got to code the agglomeration process. the interpretation is that socially functioning space in the widest space syntax sense is voids' occupation by at least two agents. each agent at the moment has some definition of visual field, but i haven't found a "traffic" in any of paul's swarming code. so this should be another agenda to do.

the home work are to get agglomeration

to manifest idea of traffic in swarming system

thus, yesterday paul reminded me to "get back to what we're doing"

this means, he wants

1. we got voronoi, so what?

my reply to this should be: we know voronoi have been widely used for many kinds of purposes, and it has many useful definition attach to it. voronoi used within the project because it generates divisioning of space, - so useful since each division (or we call it cell) is actually a convex space - of free form. it is actually so similar to space syntax convex map. what is missing in voronoi is how to show connection between these conves spaces.

what to do then is to rule the agent so its now permeable from all sides of voronoi cell.

http://www.exploratorium.edu/complexity/CompLexicon/automaton.html

from that page:

Cellular AUtomata (1947 - 1996)

Cellular automata are the simplest models of spatially distributed processes. They consist of an array of cells, each of which is allowed to be in one of a few states. At the same time, each cell looks to its neighbors to see what states they are in. Using this information each cell applies a simple rule to determine what state it should change to. This basic step is repeated over the whole array, again and again. Some of the patterns produced, by several simple cellular automata, are shown on this page.

Cellular automata were invented in the 1940's by the mathematicians John von Neuman and Stanislaw Ulam, while they were working at the Los Alamos National Laboratory in northern central New Mexico. The most famous cellular automaton is the "Game Of Life" invented by mathematician John Conway, in the 1960's. Despite the simplicity of the rules governing the changes of state as the automaton moves from one generation to the next, the evolution of such a system is complex indeed.

For interactive cellular automata simuations, go to Prof. David Griffeath's Java-based page CAffeine. (The images on this page were produced by Prof. Griffeath and his students.)

A great collection of animated simulations is available at the Live Artificial Life Page.

Exhibits || CompLexicon || Timeline

� The Exploratorium, 1996

from that page:

Cellular AUtomata (1947 - 1996)

Cellular automata are the simplest models of spatially distributed processes. They consist of an array of cells, each of which is allowed to be in one of a few states. At the same time, each cell looks to its neighbors to see what states they are in. Using this information each cell applies a simple rule to determine what state it should change to. This basic step is repeated over the whole array, again and again. Some of the patterns produced, by several simple cellular automata, are shown on this page.

Cellular automata were invented in the 1940's by the mathematicians John von Neuman and Stanislaw Ulam, while they were working at the Los Alamos National Laboratory in northern central New Mexico. The most famous cellular automaton is the "Game Of Life" invented by mathematician John Conway, in the 1960's. Despite the simplicity of the rules governing the changes of state as the automaton moves from one generation to the next, the evolution of such a system is complex indeed.

For interactive cellular automata simuations, go to Prof. David Griffeath's Java-based page CAffeine. (The images on this page were produced by Prof. Griffeath and his students.)

A great collection of animated simulations is available at the Live Artificial Life Page.

Exhibits || CompLexicon || Timeline

� The Exploratorium, 1996

## Tuesday, July 15, 2003

some art on voronoi and real people interestingly applied Boundary Functions Scott Snibbe, 1998

human field of view oh say can you see >what animal has the sharpest eyes?