[MUD-Dev] Re: Introduction System
J C Lawrence
claw at kanga.nu
Mon Jul 17 10:24:44 New Zealand Standard Time 2000
On Mon, 17 Jul 2000 07:06:58 -0700
birgit schulte <birgit.schulte at philips.com> wrote:
> Note: This message was written via the list web archives. There
> is no guarantee that the claimed author is actually the author.
> On Fri, 14 Jul 2000 22:57:26 -0700 Colin Coghill
> <C.Coghill at auckland.ac.nz> wrote:
>> I'm curious as to how such systems would handle two unintroduced
>> players in the same room, who happened to have the same
>> Do players have any way of differentiating between two short
>> red-haired dwarves who haven't yet been named/introduced ?
> On a LPMud where I was active the chars had addionally to the
> immediately visible attributes an appearance-value, which was set
> to a random value between 0 and 100 at character generation, and
> was only indirectly displayed (by a string hinting at the
> appearance). So as long as this appearance was different, you'd
> be able to tell one short red-haired dwarf from the other. If you
> wanted to disguise as someone else, you accordingly needed to hit
> that appearance-value exactly to be taken as the person you'd like
> to imposter.
This could get interesting:
Instead of using a 1-100 range, use a much larger value set, or
even a collection of value sets. Say 4 bits per value set (16
values) with 8 value sets in a two-word integer. If you want more
types or larger sets, just increase the widths. Heck, just use an
8bit char per value set -- makes the arithmetic faster and
Now use those value sets to map basic characteristics. Say, all
the basic body types/species are enumerated in one value set, all
the hair colours in another, the skin tones in a third, the ages
in a fourth, etc. The idea is not to make an exact mapping, but
arrive at integer values which loosely *represents* that
character's current appearance.
Now, upon meeting an arbitrary character, scan the recorded
character value sets and compare them to the value set before the
player. If there's a match within N%, ouput a message ala:
There's an oddly familiar dwarf here.
You can then plug in various nearness percentages, history/age
factors (long time since you've seen Bubba == harder to recognise
him for even identical value sets) etc.
This also gives an initial handle on approaching disguises in a
semi-intelligent manner as it allows easy support of both
intentional and unintentional disguise factors.
Bubba has a terrible fright and his hair goes white. He is now
commonly mistaken for Boffo whose hair was always white.
J C Lawrence Home: claw at kanga.nu
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