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By Dennis Merritt

AI Expert Newsletter

AI - The art and science of making computers do interesting things that are not in their nature.

March 2005

• Introduction

• Neural Nets on the Web

  • The artificial neuron
  • Perceptrons, and biologically realistic neurons
  • Hopfield networks
  • Other sites
  • Links

• The Logical Languages Loglan and Lojban

  • The Sapir-Whorf hypothesis, or,
    how our language determines our thought
  • James Cooke Brown and Loglan
  • How Loglan was constructed
  • The great Loglan-Lojban schism
  • The grammar of Loglan - names
  • The grammar of Loglan - predicates
  • The grammar of Loglan - more predicates
  • The grammar of Loglan - describing objects
  • The grammar of Loglan - sets of objects and typical objects
  • Some general points
  • The many implications of "no"
  • Loglan and man-machine interaction
  • Loglan and machine translation
  • Loglan, RDF, and ontologies
  • Links and other references

• Leibniz's Calculus of Reason

  • Links

• The Resurrection of Etaoin Shrdlu

  • Links

Introduction

Back in 1995, applets seemed so wonderful. James Gosling,

one of Java's developers,

recalls

a demonstration he gave to a group of Internet and entertainment

professionals:

As the talk began, Gosling noticed

that many people were only casually paying attention. After all,

what was so exciting about a new language driving a page of text

and illustrations in a clone of Mosaic?

Then Gosling moved the mouse over an illustration of a 3D molecule

in the middle of the text. The 3D molecule rotated with the mouse movement.

Back and forth, up and around. "The entire audience went 'Aaaaaaah!'"

says Gosling. "Their view of reality had completely changed because it MOVED."

Ten years later, applets haven't become as popular as everyone hoped.

However, there are some nice ones around, especially for

teaching, and I start this month's issue with a look

at what's available for demonstrating neural nets.

The rest of the issue has a linguistic flavour, with

a long feature on Loglan and Lojban, artificial

speakable languages designed around logic; a quote from

Leibniz on his dreams for such a language; and a glance

at the resurrection of Terry Winograd's natural-language

program

Shrdlu.

Neural Nets on the Web

More than symbolic AI, neural nets seem to

lend themselves to applet demonstrations - perhaps

because compared with trees, graphs, and other

structures, displaying the output is relatively

straightforward. In this article, I want to talk

about some of the applets I found on the Web.

The artificial neuron

I did a lot of searching before starting to write. The

best site I found was

Neural

Java: Neural Networks Tutorial with Java Applets

at the École Polytechnique Fédéral de Lausanne.

(As usual, the list at the end of the feature

shows all the URLs referred to.) This

page links to a lot of demos, of which

one of the

best was also one of the simplest,

conceptually speaking. This is the

artificial neuron,

which demonstrates the structure and behaviour

of a single neuron. This is the biologically

unrealistic kind, as used in perceptrons, back-prop,

and most other neural nets.

The original applet was written by Fred Corbett - link-rot

has swallowed his page - and modified by

Olivier Michel and Alix Herrmann. It shows a single neuron

running up the applet area, with inputs, synaptic weights, weighted sum,

threshold, activation function, and output, all clearly labelled;

there's a checkbox to hide the labels if you find them

distracting. Text fields display the current value of

inputs, weights and threshold; you can type new

values into these and hit RETURN, and the

applet will immediately recalculate and display new results for

the following stages, without you needing to find and press

the CALCULATE button.

The neuron's activation

function is plotted in a neat little graph just below the

output; clicking on this cycles through a range of

functions - linear, Gaussian, step, sigmoid. After

changing the activation function, you do need to press

CALCULATE. This is, to my mind, a flaw: the controls would be

more consistent if the applet recalculated immediately the

graph were changed.

The colours of background, neuron, buttons, labels, and other

components have been well chosen, so that they're not

garish or distracting - with the possible exception

of the pink arrows linking the

labels to what they label.

The applet is embedded in

the middle of a page which has some exercises below it, and

a brief theory section above. This lists the neuron's parts

- inputs, weights, and so on - and is nice because

you can fit the applet and the list onto the

screen at the same time. Each item in the list is linked to

a glossary entry. That for "activation function" shows

the graph of each function accompanied by its equation; that for

"neuron output" has the formula for weighted sum. Just above

the applet, there's a final link, to the instructions for use.

To bring in a bit of history, always worth doing, the

Neural

Java page also has a

McCulloch-Pitts

neuron.

Simpler than the applet above, this has the nifty feature that

you can

toggle inputs from excitatory to inhibitory by clicking

on the synapse. Unfortunately, "McCulloch" is misspelt in

two places, one being the bold and large-text page heading.

Perceptrons, and biologically realistic neurons

Lausanne's perceptron

builds on the artificial neuron,

extending it with the ability to set the

input-output pattern to train on by

clicking the output column of a truth table.

Training rate and number of iterations can

be selected, and you can either train in one

go, or single-step. As training proceeds, the

applet displays the "decision hyperplane" - the line

separating the two classes of inputs - evolving

in the input space.

There are many other applets in this collection,

including: multi-layer perceptrons; multi-layer perceptron for

character recognition; a radial basis function network;

and a Gaussian mixture network. Unusually - but

pleasingly - amongst the sites

I visited, some applets are more biologically realistic. One of these

is Sebastien Baehni's

retina

simulator, which models

the retina as a resistance-capacitance network,

allowing you to set the network's parameters, draw a simple

shape, and then see the result of passing that

input through the retinal layers. The applet code

can be downloaded, and

there are links to JavaDoc documentation.

Another applet, written by Florian Seydoux, models the

membrane potential of a single spiking neuron.

This has an elaborate Swing-based display,

which I did find slow to load. I couldn't

find any explanatory text, and it

wasn't immediately obvious how to start

a run (an unlabelled arrow button in

the top right-hand corner), but it's an

interesting model.

Hopfield networks

The applets I've looked at above are one-shot networks, but

Lausanne also have a

Hopfield model,

adapted by Olivier Michel

from an applet by Matt Hill. This allows you to

draw patterns on a small grid by clicking its squares and

then either store them, or use them

as test patterns to recall one already stored. A nice

feature is that you can recall a pattern, change a

few of its squares, and then use it as another test,

watching the network (if your changes weren't too great)

undo the damage.

This, and some of the other Hopfield applets around,

demonstrate the Hopfield

net acting as an autoassociator,

but ignore its

dynamics.

This would admittedly be difficult, since the net's

state has as many dimensions as the number of

neurons, but it would be interesting to

see someone try. The nearest I've found is an

applet written by Richard DeVaul at MIT to demonstrate

simulated annealing in spin glasses.

This generates a set of 100-particle spin glasses,

depicting each glass as a dot in two-dimensional space,

with distance from the centre of a circle representing energy.

As the

glasses cool, one sees a group of dots

moving from the energy maximum at the centre

to various points on the circle's circumference.

Other sites

There are many other neural-net applets on the Web, though the

Lausanne ones must be amongst the best-designed and best-explained.

One of the other collections resides at Wayne State University's

Computation and Neural Networks Laboratory. However,

although it appears in many resource lists, the

site is poorly presented, with spelling mistakes, poor

text layout, dead links, and some applets whose only documentation is

a research paper whose content doesn't match the

applet controls. It's a shame, because some applets,

such as the

ball-balancer,

must have needed a lot of work.

Having said that, they're useful resources, although

if using them in teaching, you might need to

write some explanations.

Applets take a lot of time and effort to write well,

so it's not surprising there aren't more around. All

credit to those who have put in the hours and made

their work available to the rest of us.

Links

java.sun.com/features/1998/05/birthday.html -

Java Technology: the early years, by Jon Byous.

"Chances are, everything you know about Java technology is only a few years old.

There's a good reason for that: On May 23, 1998 the technology officially celebrated its third birthday.

As part of the celebration, we interviewed several members of the Java

technology team who have been around since the early days, and we put together this retrospective to share with the readers of java.sun.com.

Join us on a stroll through history".

java.sun.com/products/jlf/ed2/book/ -

Java Look and Feel Guidelines, 2nd edition. Web version of Sun's

book on the good design of Java user interfaces, including those for applets.

diwww.epfl.ch/mantra/tutorial/english/index.html -

Neural Java: Neural Networks Tutorial with Java Applets,

École Polytechnique Fédéral de Lausanne. This contains the following applets mentioned above:

artificial neuron;

McCulloch-Pitts neuron;

perceptron learning;

retina simulation;

membrane potential of a single spiking neuron;

Hopfield model.

www.saliege.com/dynamique/projet/neurones/Hopfield.html -

Gérard Chevet's Hopfield applet (in French).

web.media.mit.edu/~rich/research/mathModel/optimize/Annealing.html -

The spin glass annealing applet, by Richard DeVaul, MIT.

www.itee.uq.edu.au/~cogs2010/cmc/chapters/Hopfield/ -

The Hopfield Network: Descent on an Energy Surface, by

Simon Dennis, University of Queensland.

A nice account of Hopfield networks, their dynamics, and basins

of attraction.

neuron.eng.wayne.edu/software.html -

Computation and Neural Networks Laboratory,

Wayne State University. This contains the

ball balancing

applet by Paul Watta and Mohamad Hassoun.

www.neuroinformatik.ruhr-uni-bochum.de/ini/VDM/research/gsn/DemoGNG/GNG.html -

DemoGNG applet for competitive learning, by Hartmut Loos and Bernd Fritzke,

Systems Biophysics

Institute for Neural Computation, Ruhr-Universität Bochum.

staff.aist.go.jp/utsugi-a/Lab/Links.html -

Applets for Neural Networks and Artificial Life.

This site often appears in resource lists, but I haven't

yet looked into it.

www.hav.com/nnhtml.htm -

The HTML Neural Net Consulter from hav.Software. Two

JavaScript net demonstrations.

www.statsoftinc.com/textbook/stneunet.html -

StatSoft's Neural Networks page. A collection of short entries covering much that's worth

knowing about problem solving with neural networks.

Some examples are geared towards StatSoft's Statistica neural networks software,

but the page is still a useful reference source which doesn't, as some texts do,

ignore the connection with statistics. Indeed, this page is just part of StatSoft's Electronic

Statistics Textbook, whose contents page at

www.statsoft.com/textbook/stathome.html

reveals it to be a compendium of information on everything from cluster analysis to quality

control charts.

ftp://ftp.sas.com/pub/neural/FAQ.html -

The comp.ai.neural-nets FAQ. Since the last modification date given is for 2002,

this may be an old version (there are many versions, of varying dates, scattered

around the Web), or perhaps it is no longer being maintained. Nevertheless, useful,

detailed answers to questions. E.g. under

"How many kinds of Kohonen networks exist?":

"Teuvo Kohonen is one of the most famous and prolific researchers in neurocomputing,

and he has invented a variety of networks. But many people refer to 'Kohonen networks'

without specifying which kind of Kohonen network, and this lack of precision can lead to confusion.

The phrase 'Kohonen network' most often refers to one of the following three types of networks: ...".

www.phys.uni.torun.pl/~duch/CI.html#Neural%20Networks -

Wlodzislaw Duch's links list for Computational intelligence: numerical methods

Neural networks and relevant math. An extremely thorough list: even

though it has the usual proportion of dead links, there's a wealth of good material and

a good starting point, covering financial applications, polyhedral computation,

Reactive Tabu Search, Chernoff faces, the Euroattractor conference, and

iterons, filtrons, and cellular automata.

The last modification date of 18.02.2005 at the bottom

of the page gives confidence it's still being cared for.

The Logical Languages Loglan and Lojban

Eskimos have 500 words for snow! In fact they don't, as

I'll explain later. But the hypothesis behind this common

urban myth is a serious one, and it's the same hypothesis

that gave rise to Loglan and Lojban, logical languages

packed with features to make you think more rationally.

These, and their relevance to AI, are what I am going to

talk about in this article.

The Sapir-Whorf hypothesis, or, how our language determines our thought

The 500-words myth arose from the Sapir-Whorf hypothesis,

named after linguist and anthropologist

Edward Sapir and his colleague and student Benjamin Whorf.

A strong statement of the hypothesis

says that the way we think about the world is determined by

how our language describes reality. Thus, so the popular accounts

go, because Russian has two words for "blue",

Russians will perceive two shades of blue where we see only one;

because the language of the Hopi Indians has no tenses,

the Hopi anticipated Einstein's relativistic view

of the Universe as an unchanging block extending through space-time.

A different version of the hypothesis permits language to both influence

and be influenced by thought - hence, the Eskimos might have

all those words for snow because they live amongst, and need to

work with, so many different varieties of the stuff. I'm

not going to comment further - the intellectual history is complicated, and

I haven't read much of what Whorf or Sapir actually wrote - but I've selected some links

for the end of this article,

including debunkings of the snow myth.

Sapir-Whorf has generated a dreadful lot of rubbish: beware of anything

that gives undue reverence to Hopi relativity, neurolinguistic programming,

or the cultural relativism of scientific truth.

James Cooke Brown and Loglan

After surviving the Oxford weather for the past three weeks,

I feel I have words for every kind of rain, hail, sleet and snow ever

precipitated. However, rather than testing myself for the influence

of weather words on thought, I've been doing

what James Cooke Brown would have wanted me to do - exposing my brain

to the language he invented. This is Loglan: a language intended to

be as rich as any natural language, but with a unique

logic-based structure.

Nowdays, Loglan interests people because of its basis in logic and

its relevance to computing. That's how I shall approach it and its

offshoot, Lojban. However, when James Cooke Brown

began, logic was secondary. He wanted to test the Sapir-Whorf

hypothesis by creating a language that would be different in

some extreme way from natural languages. Making it

extremely logical, rather than, say, extremely literary, just

happened to be most feasible. This is how, in his book

Loglan 1: A Logical Language,

he explains his project:

In 1955 there was no experimental linguistics. But the then very rapid

development of theoretical and mathematical linguistics seemed to be

transcending the earlier descriptive stage of that science, and hence

to offer clear if tentative guidelines for the construction of just

such a culture-free language as my strategy required. ...

But if the constructed language were to serve as a laboratory

instrument - something to "release", as it were, or at least

increase the probability of observing, Whorfian effects - it

would obviously not do to imitate any natural language or group of

languages too closely. What was wanted, apart from smallness, was

not a typical human language, but a severely atypical one. For if the

Whorfian effects of second-language learning turned out to be elusive -

and compared to those of primary language learning, which we had decided

were impossible to isolate, we could certainly expect them to be minor -

they would probably not be revealed in a single culture in a single try.

In fact, we could probably expect the complete pattern of any given second

language's Whorfian effects to emerge only against the background of many

primary cultures, and perhaps only then after many tries. Thus a language

constructed to release measurable Whorfian effects when learned as a

second language should offer fairly large structural contrasts with

all the first languages that might be involved. Since any natural

language might eventually be involved, what was required was a diminutive

but nevertheless genuine human language which was easily learned by adults

and which differed from all natural languages in some scientifically interesting

way. ...

The most promising way to create such a difference, it seemed to me, was to

exaggerate some natural function of human language, that is, to increase the

functional adequacy of some complex of linguistic structures in a way that

would have a strong independent likelihood of enhancing the measurable

performance of its learners on some specified set of tasks. Besides, in

its original formulation the Whorf hypothesis is a negative one: language

limits thought. One way of disclosing such phenomena is to take the suspected

limits off, more precisely, to push them outward in some direction in which

removing limits would have predictable effects. So it was settled. The diminutive

language should also be a functionally extreme one in some known or presumable

way: an extremely poetic one, say, or an extremely efficient one, or extremely logical.

Now there is very little scientific knowledge about the literary functions

of language, and while a lot is known about efficient codes, it is hard to

relate this property to Whorfian effects. Enhancing and clarifying the logical

structures of the diminutive language, however, seemed to answer all the

requirements of the project. There is a very considerable body of knowledge

about the formal properties of logical systems; and a hyperlogical linguistic

structure should have a clear and interesting Whorfian effect if it had any:

namely the facilitation of certain identifiable kinds of thought. Not only

that, but a language which only faintly promised such a mind-enhancing effect

would almost certainly prove attractive to a large body of potential learners,

namely students. Thus the idea of Loglan as a hyperlogical or thought-facilitating

language had a very natural birth.

How Loglan was constructed

Brown wrote an article for the June 1960 issue of Scientific American

which explains very clearly how he constructed Loglan's

vocabulary and grammar. Most artificial languages intended for

international communication, such as

Esperanto, have vocabularies made up of natural-language

roots. One of the earliest, Volapük, had a high

proportion of Germanic roots: its name is from

the German "Weltsprache" or "world speech", mangled

to fit Volapük's sound structure. More often though, the

creators of such languages have based them on Latin and

Greek roots, because of their internationality.

To avoid such cultural bias, Brown started with

the most frequently-spoken natural languages, which he deemed to be

English, Mandarin, Hindi, Russian, Spanish, Japanese, French, and

German. To make a Loglan word for some concept, he took words from each

of these languages, and blended them in various ways, selecting

some sounds and rejecting others. He scored each blend

by adding up the proportion of sounds from each language, weighted by

the language's importance: English weighted higher than Mandarin,

and Mandarin higher than Hindi, because of the number of speakers.

The total score for each blend measured its learnability by speakers

of all eight languages, weighted by each language's importance.

As an example, Loglan for "blue" is blanu. This contains all the

sounds from "blue"; all those from "lan", Mandarin for "blue"; and

all those from the German "blau", if the dipthong "au" is split

into its constituent vowels. But it contains only 2/3 of French "bleu",

1/2 of Hindi "nila", 1/2 of Spanish "azul", 2/7 of

Russian "galuboi", and

none of the Japanese "ao" or "kon". To score "blanu", each fraction

was multiplied by the weighting for its language.

Brown did the same for other trial words for "blue", such

as blula and lablu. blanu

scored highest.

Some other Loglan words which display their English ancestry

are: prano ("run");

briki ("brick"); cefli ("chef"); and

vegri ("green"). For what it's worth, I

find these and other words pretty easy to recall.

The great Loglan-Lojban schism

Before moving on to grammar, I want to say a bit about

Lojban, a younger offshoot of Loglan. Lojban users are more active

on the Web and in discussion groups than Loglan users, and

Lojban's Web site is bigger, with many more teaching

materials. Because of this, I spent most of this

article's preparation time on Lojban,

not Loglan.

Lojban arose because of an intellectual property dispute.

between Brown and the Lojbanists-to-be. Summarising the

accounts I've read, it seems that

the Lojbanists wanted to change Loglan to fix problems, and

also to publish wordlists and other materials; Brown, on the

other hand, regarded Loglan as an unfinished research project,

and so wanted final control over the language, as well as

royalties on the published materials. The dispute resulted in

a court case over the name "Loglan", which Brown trademarked.

Bob leChavalier, chief engineer of Lojban, founded

The Logical Language Group, and Lojban went on to emerge as a

complete language. In fact, it was already in use in 1987.

In October 1987, leChavalier

married co-Lojbanist Nora Tansky. The marriage vows were spoken in

primitive Lojban.

The good news for anyone wanting to use Lojban in

computing is that it has a large Web site at

www.lojban.org/, with language

manuals and other reference material, as well as texts

written in or translated into Lojban. Be warned - Lojban

uses words from its own vocabulary for grammatical terms such

as "pronoun" and "sentence", even when talking about them

in English. This is more precise, since its grammar is

so different from that of the natural

languages, but it does mean you'll need to learn

the terminology before being able to make much sense of the texts.

The grammar of Loglan - names

I'll now talk about grammar. As I mentioned above, I'm going to

describe Lojban, not Loglan, because more has been published about

it. The grammatical structure of

Lojban and Loglan is similar; they do have different vocabularies,

because the Lojbanists rebuilt the words from scratch, using

more accurate estimates of the numbers of speakers of world

languages.

Before describing how Lojban handles predicates, I need some

objects for them to relate. So like any language teacher trying to

convey first impressions of a language

while avoiding the complexities of noun declension, I'll start

with proper names.

All parts of speech in Lojban have a specified pattern of consonants and

vowels, making it possible for human and computer to parse a sentence even

when they don't know what its words mean.

The basic predicate words, for example, must follow the pattern CVCCV

or CCVCV, as shown by dunda,

meaning "give", and klama, meaning "go".

To accomodate the huge variety of names for people, cities, and everything

else, Lojban restricts

them less than it does other words. However,

changes are often still necessary when Lojbanising a name.

Moreover, Lojban doesn't allow sounds foreign to its repertoire: thus it

lacks both English "th" sounds, because most people around

the world can't say them. Some names, incidentally, puzzled me

when I started, because they had an a where I would

expect o to be the nearest Lojban vowel.

I think this is because

the names were translated by Americans, for whose pronounciation

a was closer. This would explain why the

Loglan translation of the childrens' "See Spot run!" - yes, there

really is one - is Vizka La Spat.

Let's make some names then. My name, Jocelyn, becomes

DJOselin.. This has two unusual features: the capitals, and the full stop.

The capitals indicate that the stress falls on the

first syllable, contrary to Lojban's default stress rule. Lojban is designed to

be audio-visually isomorphic, meaning that there's a one-to-one

correspondence between the written and spoken forms of a word; thus,

written Lojban always indicates stress, rather than leaving it to the imagination as in

English. The full stop is not a sentence terminator: Lojban uses it to indicate

a slight spoken pause, thus preventing the name running into the next

word and causing ambiguity. Names which start with a vowel need

a pause at the beginning as well as the end: Oxford becomes

.oksfyrd..

The grammar of Loglan - predicates

Now we have some objects, let's relate them, starting with

the Lojban translation of "Jocelyn goes to Oxford":

la DJOselin. klama la .oksfyrd.

The little word la looks like a French or Spanish "the", but

means something more like "the object that I know by the following name".

klama means "go". More precisely, klama is a predicate. Looking

in the dictionary, I found it was defined as:

goes: klama: x1 comes/| to destination x2 from origin x3 via route

x4 using means/vehicle x5 [also travels, journeys, moves, leaves to ... from ...;

x1 is a traveller; (x4 as a set includes points at least sufficient to constrain the route relevantly)]

Like a comment in any well-documented program, the

definition tells us the meanings of the arguments. The first

is the actor doing the going, and the second is their destination.

Altogether, klama has five arguments, and we can fill them in

as follows:

la DJOselin. klama la .oksfyrd. la kardif. la bristyl. la ferstgreituestyrn.

are Cardiff and Bristol. ferstgreituestyrn. is my attempt

at Lojbanising the train company First Great Western, which

runs trains on that line. I haven't shown you how to omit

arguments yet, so I needed something to fill the "via" place.

In fact, trailing arguments can be omitted just by not writing them. So

la DJOselin. klama la .oksfyrd.

means "Jocelyn goes to Oxford".

Omitting non-trailing arguments calls for more grammar. One way

is to use zo'e, "something not important". So if I want

not to say where I came from, I can write:

la DJOselin. klama la .oksfyrd. zo'e la bristyl. la ferstgreituestyrn.

and I can avoid naming the means of travel too, either by omitting it

or by replacing it with the "something not important":

la DJOselin. klama la .oksfyrd. zo'e la bristyl.

la DJOselin. klama la .oksfyrd. zo'e la bristyl. zo'e

The apostrophe in zo'e indicates that the two vowels are

pronounced separately rather than running together as a dipthong.

According to Nick Nicholas and Robin Turner in their

Lojban For Beginners,

most people don't write more than one zo'e.

Lojban doesn't prohibit it, because such a sentence would still mean

something, but it does provide other grammatical possibilities.

These include the argument-position indicators fa, fe, fi, fo,

and fu, which act like

keyworded arguments in some programming languages. Thus,

la DJOselin. klama la .oksfyrd. fu la ferstgreituestyrn.

- "Jocelyn goes to Oxford by First Great Western", where

fu marks the phrase following as the fifth place.

These argument-position markers can be used in any order,

so I could have written

fu la ferstgreituestyrn. klama fe la .oksfyrd. fa la DJOselin.

fa and

its fellows would be very useful in Prolog when passing

partially parameterised predicates to maplist

and other higher-order predicates.

The grammar of Loglan - more predicates

Perhaps my subsconscious is still running on the 500 types of snow, but

the first word my cursor fell on in the English-Lojban dictionary

was "umbrella". This is the definition:

umbrella:

santa:

x1 is an |/parasol shielding x2 from x3, made of material x4, supported by x5.

So although "umbrella" is a noun, when you open it into

the Lojban world, it unfolds into a predicate.

In fact, Lojban does not distinguish between nouns, adjectives, adverbs

and verbs. They're all predicates. Here are some other

weather-related words:

cold: lenku: x1 is |/cool by standard x2.

fall: farlu: x1 falls/drops to x2 from x3 in gravity well/frame of reference x4 [note: things can | in spin, thrust, or tide as

well as gravity; (agentive "drop" = one of two lujvo: falcru and falri'a)].

hail: bratu: x1 is |/sleet/freezing rain/solid precipitation of material/composition including x2 [this is the substance,

not the act or manner of its falling, which is carvi].

rain: carvi: x1 rains/showers/[precipitates] to x2 from x3; x1 is precipitation [not limited to '|'].

wet: cilmo: x1 is moist/|/damp with liquid x2.

winter: dunra: x1 is |/wintertime [cold season] of year x2 at location x3.

The entry for "hail" is so right - sleet, freezing rain, and solid precipitation

is exactly what Oxford has been suffering.

The grammar of Loglan - describing objects

Loglan uses predicates to describe actions. It also uses predicates

to describe objects, with the help of the word

le. Here's "Jocelyn goes to Oxford by train":

la DJOselin. klama la .oksfyrd. fu le trene

where the definition of trene is

train (vehicle): trene (ren re'e): x1 is a | [vehicle] of cars/units x2 (mass)

for rails/system/railroad x3, propelled by x4 [a railed vehicle or | of vehicles;

also subway (= tu'unre'e), metro, trolley, tramway (= lajre'e), roller coaster;

monorail (= dadre'e); cable car, sky car, ski lift (= cildadre'e)]

What's happening in this sentence is that le converts a predicate

into some object I'm thinking about that the predicate describes.

Imagine two robots speaking Prolog to

one another. One says to the other:

  ?- find_referent_of_name( 'Jocelyn', A1 ), 

     find_referent_of_name( 'Oxford', A2 ), 

     find_referent_of_noun( train, A5 ),

     mark_as_unknown( A3 ),

     mark_as_unknown( A4 ),

     update_your_world_model_with_event( goes( A1, A2, A3, A4, A5 ) ).

That, roughly, is the effect the speaker wants to have on the listener.

Whereas la told the listener to identify the object referred to by

a proper name, le tells them to identify the object referred to by

a description.

The grammar of Loglan - sets of objects and typical objects

I now want to tell some jokes:

Q: Did you know a man is knocked down on Britain's roads

every eight minutes?

A: No.

Q: Yes there is, and he's getting pretty fed up about it.

Johnny's mother: Johnny told me he got 100 in his tests yesterday!

Johnny's mother: He did - 50 in spelling and 50 in arithmetic.

Teacher: Suzie, please spell "cattle".

Suzie: C-A-T-T-T-L-E.

Teacher: Leave out one of the Ts.

Suzie: Which one?

Q: Name five things that contain milk.

A: Butter, cheese, ice cream, and two cows.

All these rely on ambiguity for their effect. If any Lojbanists are reading

this, I'd like to issue them as a challenge, because I suspect

they'd be impossible to translate without losing the humour. Lojban is

probably too precise.

The first joke, for instance, appears to be confusing two meanings of "a":

"the typical",

and "a particular individual". The point is that

in English, "a" can indicate typicality.

When I say "a Macintosh is an elegant machine", I am talking

not about a specific machine - that one on my desk, or in the dealer's window -

but about the typical Mac. In the same way, I might say "a typical child in

England starts school at the age of five". Lojban provides a particle for such references,

le'e.

In general, Lojban enforces great precision in distinguishing between

particular individuals, the typical individual, sets,

members of sets, and masses such as sand

or water.

Some general points

Rather than continuing with the grammar in detail, I'll

leave the task to one of the excellent teaching texts.

Instead, I want to utter a few generalities about what

Lojban can do.

Lojban has equivalents for tenses and

spatial adverbs and prepositions - and,

being regular, treats both the same way, since location

in time is the same kind of concept as location in space.

It has pronouns - and distinguishes between the

inclusive "we" which includes the listener, and the exclusive "we" which

doesn't.

And it has words for "and" and "or" - and

distinguishes between "inclusive or" and "exclusive or".

Lojban also has material

implication, the logical "if". But, as the following extract from

What is Lojban? shows, it takes pains to distinguish this

from the "if" which means causation:

Lojban connectives cannot be used to correctly translate English

"If you water it, it will grow", because material implication is too weak

and the special causal connectives, which connect assertions, are

too strong. What can be done instead?

The English sentence "If you water it, it will grow" looks superficially like

a Lojban na.a connection (material implication), but it

actually has causal connotations not present in na.a. Therefore, a

proper translation must involve the notion of cause. Neither the Lojban coordinating

causal conjunction nor the two correlative subordinating causal conjunctions

(one of which subordinates the cause and the other the effect) will serve,

since these require that either the cause, or the effect, or both be asserted.

Instead, the correct translation of the English involves 'cause' as a predicate,

and might be paraphrased "The event of your watering it is a cause of the

event of its future growing."

(roda zo'u lenu do jacysabji da cu rinka lenu da ba banro)

I have known authors of expert-system knowledge bases who used logical "if"

when they meant causation: that quote should be required reading for such people.

Lojban has imperatives - "come here!", "turn the light off", "please close

the door". It has indicators for emotional attitude, such as surprise, interest,

amusement, and love. (However,

as linguist Geoffrey Sampson says

in his review of John Cowan's book

The Complete Lojban Language,

this part of

the language embodies some questionable analyses of human emotion.)

And if you want to be deliberately imprecise, you can: Lojban allows you to

create metaphors by combining predicates but without specifying the

relationship between them. This is what I would be doing when writing

"knowledge base" or "expert system" in English, were it not that we've

evolved generally agreed-upon interpretations for these phrases.

You can even write poetry in Lojban, at least in the weak sense that

word order is sufficiently malleable for you to rearrange words until

you find an arrangement that best suits your desired rhyme, alliteration,

or scansion. Ambiguities such as the line "And all the air a solemn

stillness holds" from Gray's Elegy - where either

the air or the stillness could be

doing the holding - probably can't be translated; but then, avoiding such

ambiguities is exactly why Lojban is worth study by anybody concerned with

linguistic analysis.

The many implications of "no"

Before moving on to Lojban and AI, I want to look at one final

topic, negation.

When I started reading about Lojban, I wondered how

much there could be to say. Surely

building negation into Lojban was just be a matter of

creating a word for "not" and adding

brackets to delimit its scope?

In fact, the matter is a lot richer, and

should interest those working with subjects such

as speech-act theory and discourse analysis.

In point of fact,

Lojban does have brackets. The words

ke and ke'e are used to bracket combinations

of predicates, distinguishing for example

"a new (freight train)" from "a (new freight) train".

However, as Sampson says

in his review:

In theory, standard predicate-logic notation could itself be made speakable,

by assigning pronunciations to signs such as brackets and comma. But -

leaving aside the

fact that any standard logical system ignores many humanly-important considerations which

Lojban does express, such as a speaker's emotional attitude to the

propositions he states -

such a language would be unusable. It would be grossly cumbersome, and would do

nothing to cater to speakers' needs to foreground or suppress particular elements,

or structure information into different perspectives. These things are facilitated

in English by mechanisms alien to logical notation, such as the passive construction. Lojban

generalizes devices such as the passive, and the contrast between forethought and afterthought

sequencing ("if p then q" versus "q, if p"), to provide even more flexibility than is typical

of natural languages.

Several examples are explored in

Chapter 15

of the Lojban reference grammar: "'No' Problems: On Lojban Negation".

The most extreme is "metalinguistic negation", where

something is wrong with the statement being negated.

In this case, it's meaningless to talk about its

truth value; the negation can make no sense until

the speaker has corrected their error.

A notorious example is the sentence

"I have not stopped beating my wife", when I never

started in the first place. Others are "5 is not blue" - colour

doesn't apply to numbers - and "the current King of France is not bald",

which is meaningless because there is no current King of

France.

To avoid such pitfalls, Lojban allows you, if asked

"have you stopped beating your wife", to reply

na'i. Your questioner will understand this

not as in English -

"no (and therefore, since I

haven't stopped, I'm still beating)" - but as "sorry, your

question was meaningless".

Another type of negation, explained earlier

in the same chapter, is "scalar negation". This

is exemplified by the sentence "the chair

is not brown". This does negate one fact -

that the chair is brown - but we also understand it

as providing positive information, namely that

there is still a chair, and that it does

still have a colour, and that this colour is

something other than brown.

Lojban has another word for "no" when you

want to give this kind of answer. The problem

then becomes that it could cause ambiguities.

For example, if I say

"I am not going to Oxford from Cardiff", does

this mean I'm not going anywhere, or that I

am going to Oxford but not from Cardiff, or that I

am going from Cardiff but to somewhere other than Oxford?

The point is that I may want to tell you that there

is still some relationship between

me and Oxford, or me and Cardiff, or me and movement;

it's just not the one stated. Lojban provides a way

to do so.

Loglan and man-machine interaction

What use could Loglan or Lojban be to computing and AI?

Many science-fiction readers will

have first heard of Loglan in Robert Heinlein's novel

The Moon

is a Harsh Mistress, in which the protagonist converses in

Loglan with Mike, the intelligent computer.

In fact, James Cooke Brown proposed Loglan for man-machine

interaction in Loglan

1: A Logical Language.

He did admit few would learn it solely for this purpose,

but hoped it might become used if ever enough

people had already learnt it for other reasons.

I have found one

program for analysing Lojban, written,

not surprisingly, in Prolog. This is

Nick Nicholas's

semantic

analyser, written in

NU-Prolog.

In a paper on the program,

Nicholas gives the following example of parsing

mi prami lo prenu ku poi ke'a citka lo cakla: "I love the person who eats a

chocolate"

  q(suho(1), _FJKWJ, prenu(_FJKWJ, _FJKDO, _FJKDP, _FJKDQ, _FJKDR),

  q(suho(1), _FJKGU, cakla(_FJKGU, _FJKLJ, _FJKLK, _FJKLL, _FJKLM), _FJKIQ,

  citka(_FJKWJ, _FJKGU, _FJKGV, _FJKGW, _FJKGX)), prami(mi, _FJKWJ, _FJJZA,

        _FJJZB, _FJJZC))

which can be displayed more legibly as:

  \exists X:

    person(X), {first restriction on X}

    (\exists Y:

      chocolate(Y); (eats(X,Y))); {second restriction on X}

    (loves(me,X)).

Loglan and machine translation

Brown proposes other applications of Loglan, including

machine translation.

The idea is an old and familiar one,

namely that we translate one language to another by

extracting the meaning of the source, representing it in a completely

language-independent form, and then translating this semantic representation

back into the target. The intermediate meaning representation is the

interlingua or interlanguage, also known as the pivot language.

Many formalisms have been put forward for interlinguas. One of the

first was Schank's Conceptual Dependency,

which analyses natural language statements into

primitives such as "exchange possession of", "move location of",

"move bodypart", and "pass food into body through orifice".

Although not invented with translation in mind, some researchers

did try using it in small systems. A modern example is

UNL

or Universal Networking Language, developed under the auspices

of the United Nations University in Tokyo. The UNL site has several

presentations demonstrating small UNL translations of natural language. It

also has a lot of dead links and no publications after 2002, suggesting

that the project has been abandoned.

Interlinguas are not widely used.

Most machine translators

prefer the "transfer" method. Unlike an interlingua, this

requires a separate translation program for each source-target

language pair: a transfer translator for all EU languages, for

example, would need an English-German translator, and

a Polish-Dutch, and a Portuguese-Czech, and many others.

The advantage of transfer is that it doesn't try to

extract and represent the meaning of its input. Instead, it carries

out a relatively superficial syntactic analysis and

transformation: the kind of almost word-for-word conversion one might do in transforming

the Dutch

"er kwam een klop op de deur" into "there came a knock on the door".

You can find out more from the introduction to Maria Flanagan's dissertation

Extended Transfer

Architecture for German Machine Translation.

Although transfer works moderately well for

languages of similar structure, it might not for

languages as different as some of those in the

Pacific Rim countries. An interlingua would

be more suitable.

This is Nick Nicholas's view in

his paper

Lojban

as a Machine Translation Interlanguage in the Pacific,

Loglan, RDF, and ontologies

An ontology specifies the

vocabulary to be shared between programs

engaged in some common task. I revealed

a small, and rather military-oriented, part

of OpenCyc's ontology in my AI

Alphabet; there are now many others in existence.

Given that

Lojban has a vocabulary

honed over the years for describing almost everything in

everyday life in logical terms, even including

Aesop's fable of The Fox

and the Crow, has it been used

as an ontology?

In point of fact, there seems to be very little. I did find two

postings on Loglan and RDF, linked at

the end. Both were by Steve Pomeroy, one proposing that

to understand how to implement negation in RDF, it was worth looking

at Lojban, and one giving a Lojban

translation of some of the RDF

used to describe the

FOAF, or Friend of a Friend, project.

With what I now know of them, I think Loglan and Lojban

are definitely worth considering as

ready-made logical formalisms for any AI task involving

linguistic analysis.

As Nick Nicholas says

in his Lojban page:

The attraction Lojban holds for me is that it is one formal

model of language which actually puts its money where its

mouth is, and tries to take on an entire linguistic system

rather than toy sentences and a postage stamp's worth of vocabulary.

The language's detailed and painstakingly compiled language definition

materials are available, the 'write-up' has been completed, and

the reference grammar has been published.

Or, to quote Sampson again:

In general, Lojban constitutes a strikingly thorough working-out of its

creators' goals, and its design is responsive to a rich,

subtle understanding of linguistics and philosophical logic.

I shall propose one final test: once Asterix

has been translated into Lojban, we shall know it has arrived as a

real language.

Links and other references

Loglan by James Cooke Brown, in Scientific American, volume 202, June 1960.

A very clear explanation of why and how Loglan was constructed.

www.loglan.org/ -

The Loglan Institute, with links to teaching materials and

other resources. James Cooke Brown's book on

Loglan, Loglan 1: A Logical Language, is at

www.loglan.org/Loglan1/index.html,

from where his views on Loglan for AI and machine translation are linked. The

translation of "See Spot run" is at

www.loglan.org/Texts/vizka-la-spat.html.

www.lojban.org/ - The Lojban site.

Navigation around the site is fairly easy: good places

to start are the Help page,

the FAQ, and

the introductory book What is Lojban?

at www.lojban.org/en/publications/level0.html.

This book contains the translation of Aesop's The Fox and

The Crow.

The chapter on negation I referred to

from the reference grammar is at

www.lojban.org/publications/reference_grammar/chapter15.html.

members.fortunecity.com/jeroenkuiper/Linguistics/Lojban/Dictionary/aindex.html -

A draft English-Lojban dictionary. I think this is somewhat out of date, but it was

OK for illustrating the principles, and

faster to use than the interface to the current

dictionary project at www.lojban.org/jbovlaste/.

home.bluemarble.net/~langmin/miniatures/lojban.htm -

Do You Speak Logic? by William Z. Shetter. A nice little essay.

His other features on linguistics and language are also fun to read.

www-106.ibm.com/developerworks/rational/library/2740.html -

Using UML to understand Lojban

by Jérôme Desquilbet of IBM.

An unusual, but very readable, application of UML.

For non-UMLers, it's also a good demonstration of

UML itself.

ptolemy.tlg.uci.edu/~opoudjis/lojbanbrochure/lessons/less2changeplaces.html -

Lojban for Beginners by Robin Turner and Nick Nicholas.

Really nice introduction, which I used in constructing some of my examples.

www.grsampson.net/Vloj.html -

The review by Geoffrey Sampson at the University of Sussex,

of John Cowan's book The Complete Lojban Language.

www2.cmp.uea.ac.uk/~jrk/conlang.html -

Richard Kennaway's annotated resource list for artificial languages.

www.zompist.com/kit.html -

Mark Rosenfelder's Language Construction Kit pages. A fun way

to learn some linguistics while reading about how to create languages

with the same characteristics that natural ones have.

www.zompist.com/heinlein.html -

Mark Rosenfelder's review of The Moon is a Harsh Mistress.

en.wikipedia.org/wiki/Sapir-Whorf_Hypothesis -

A good Wiki entry on the Sapir-Whorf hypothesis. There are links to

entries for Loglan and Lojban.

venus.va.com.au/suggestion/sapir.html

and www.aber.ac.uk/media/Documents/short/whorf.html -

Notes on the Sapir-Whorf Hypothesis by Lawrence Campbell and Daniel Chandler respectively.

www.sfs.nphil.uni-tuebingen.de/linguist/issues/5/5-1239.html -

Postings from LINGUIST List 5.1239, 6 November 1994, on the Eskimo words

for "snow". Includes a count of the snow words in the Eskimo language Yup'ik by Anthony Woodbury,

University of Texas at Austin.

www.derose.net/steve/guides/snowwords/ -

"Eskimo" words for snow,

by Steven Derose. Reviews Geoffrey Pullum's book

The Great Eskimo Vocabulary Hoax, and cites an interesting-sounding paper by Laura

Martin:

Eskimo Words for Snow: A case study in the genesis and decay of an

anthropological example in American Anthropologist, volume 88, 1986.

www.bohemica.com/?m=catalog&s=258&a=138 -

Amusing list of Czech words for which English has no equivalent, from Dominik Lukeč,

Bohemica.com.

www.lojban.org/files/software/analyser -

Nick Nicholas's semantic analyser for Lojban, written in NU-Prolog; there's a paper on the program

at www.lojban.org/files/papers/lojban_parser_paper.

Nicholas's home page

is at www.tlg.uci.edu/~opoudjis/; apart from his Lojban links and

translations at

www.tlg.uci.edu/~opoudjis/Play/lojban.html,

there's some useful material on rendering Greek in Unicode, and -

oh gosh - Hamlet

translated into Klingon.

www.cs.mu.oz.au/~lee/src/nuprolog/ -

The NU-Prolog home page.

gollem.science.uva.nl/SWI-Prolog/mailinglist/archive/old/1298.html -

A posting by Richard O'Keefe explaining how when annotations,

used in Nicholas's analyser, affect the order in which goals are called.

sow.lcs.mit.edu/2004/proceedings/Speer.pdf -

Meeting the Computer Halfway: Language Processing in the Artificial Language

Lojban by Rob Speer and Catherine Havasi, MIT. Short paper describing

a Lojban question-answering program written in Python, probably at a very early

stage in the project.

swpat.ffii.org/xatra/lojban/index.en.html -

EU Patents in Logical Language!, on the Foundation for a Free Information Infrastructure site.

The FFII lobbies against European software patents - see, for example,

their webshop.ffii.org/ page, which demonstrates

that many typical components of a typical Web shopping site are patented. They

propose patents be published in a language such as Lojban, as a starting point for unambiguous

translation into the EU languages. (They also say that development

of software tools for logical languages would be harmed if

that software could be patented.)

www.cs.cf.ac.uk/Dave/AI2/node69.html -

Notes on Conceptual Dependency, from Cardiff

School of Computer Science.

www.fitug.de/debate/9811/msg00297.html -

Posting to the UNL list briefly comparing UNL and Lojban.

www-clips.imag.fr/geta/User/wang-ju.tsai/unllinks.html -

The UNL Center page. Most of the presentations and PDFs

contain samples of UNL.

www.cs.tcd.ie/courses/csll/flanagmr0001.ps -

Extended Transfer Architecture for German Machine Translation by

Marian Flanagan, Trinity College Dublin.

This describes a Prolog implementation

of the so-called "shake-and-bake" transfer method. It also

explains the different translation methods, and looks

at how some of today's systems work.

citeseer.ist.psu.edu/27803.html -

Lojban as a Machine Translation Interlanguage in the Pacific by

Nick Nicholas.

www.rickharrison.com/language/mtil.html -

On the unsuitability of "logical languages"

for use as interlinguas in machine translation, by Rick Morneau.

norman.walsh.name/2004/04/02/notinrdf -

Not in RDF: "There isn't really a simple "not" operator in RDF. Nevertheless, it's useful,

particularly for establishing default values. So what can we do?". An essay by

Norman Walsh on implementing "not" in RDF, taking into account the difference

between open-world and closed-world views of negation. The replies on this

page include Steve Pomeroy's suggestion to look at how

Lojban describes negation.

www.w3.org/2000/10/swap/doc/cwm -

Cwm main page. Cwm is a forward-chaining reasoner for processing RDF,

used in Walsh's essay.

www.w3.org/DesignIssues/Notation3.html -

W3C's Notation 3 page. Notation 3, or N3, is a superset of RDF, and

easier for humans to read and write. Walsh uses it with cvm in his essay.

www.w3.org/TR/rdf-primer/ -

W3C's RDF primer. A good introduction.

staticfree.info/blog/lang/lojban2rdf.comments -

The other posting by Pomeroy, a Lojban

translation of some RDF used to describe the

FOAF - Friend of a Friend - project.

freshmeat.net/projects/jbofihe/ -

Main page for jbofihe, a parser for checking the syntax of Lojban text.

It also translates Lojban into pseudo-English - Pomeroy

uses it to translate his Lojban translation of RDF.

www.ainewsletter.com/newsletters/aix_0501.htm#o -

My AI Alphabet entry for OpenCyc, with a brief excerpt from

the OpenCyc ontology.

www.aaai.org/AITopics/html/ontol.html -

AAAI Ontologies page.

www-ksl.stanford.edu/kst/what-is-an-ontology.html -

What is an Ontology? by Tom Gruber, Stanford.

www.asterix-international.de/asterix/collection2.shtml -

The languages of Asterix.

Leibniz's Calculus of Reason

... Whence it is manifest that if we could find characters or signs appropriate for expressing all our thoughts as definitely and as exactly as arithmetic expresses numbers or geometric analysis expresses lines, we could in all subjects in so far as they are amenable to reasoning accomplish what is done in Arithmetic and Geometry.

For all inquiries which depend on reasoning would be performed by the transposition of characters and by a kind of calculus, which would immediately facilitate the discovery of beautiful results. For we should not have to break our heads as much as is necessary today, and yet we should be sure of accomplishing everything the given facts allow.

Moreover, we should be able to convince the world what we should have found or concluded, since it would be easy to verify the calculation either by doing it over or by trying tests similar to that of casting out nines in arithmetic. And if someone would doubt my results, I should say to him: "Let us calculate, Sir," and thus by taking to pen and ink, we should soon settle the question.

I still add: in so far as the reasoning allows on the given facts. For although certain experiments are always necessary to serve as a basis for reasoning, nevertheless, once these experiments are given, we should derive from them everything which anyone at all could possibly derive; and we should even discover what experiments remain to be done for the clarification of all further doubts. That would be an admirable help, even in political science and medicine, to steady and perfect reasoning concerning given symptoms and circumstances. For even while there will not be enough given circumstances to form an infallible judgment, we shall always be able to determine what is most probable on the data given. And that is all that reason can do.

Now the characters which express all our thoughts will constitute a new language which can be written and spoken; this language will be very difficult to construct, but very easy to learn. It will be quickly accepted by everybody on account of its great utility and its surprising facility, and it will serve wonderfully in communication among various peoples, which will help get it accepted. Those who will write in this language will not make mistakes provided they avoid the errors of calculation, barbarisms, solecisms, and other errors of grammar and construction. In addition, this language will possess the wonderful property of silencing ignorant people. For people will be unable to speak or write about anything except what they understand, or if they try to do so, one of two things will happen: either the vanity of what they advance will be apparent to everybody, or they will learn by writing or speaking. As indeed those who calculate learn by writing and those who speak sometimes meet with a success they did not imagine, the tongue running ahead of the mind. This will happen especially with our language on account of its exactness. So much so, that there will be no equivocations or amphibolies, and everything which will be said intelligibly in that language will be said with propriety. This language will be the greatest instrument of reason.

I dare say that this is the highest effort of the human mind, and when the project will be accomplished it will simply be up to men to be happy since they will have an instrument which will exalt reason no less than the Telescope perfects our vision. It is one of my ambitions to accomplish this project if God gives me enough time. I owe it to nobody but myself, and I had the first thought about it when I was 18 years old, as I have a little later evidenced in a published treatise (De Arte Combinatoria, 1666). And as I am confident that there is no discovery which approaches this one, I believe there is nothing so capable of immortalizing the name of the inventor. But I have much stronger reasons for thinking so, since the religion I follow closely assures me that the love of God consists in an ardent desire to procure the general welfare, and reason teaches me that there is nothing which contributes more to the general welfare of mankind than the perfection of reason.

Links

www.rbjones.com/rbjpub/philos/classics/leibniz/meth_math.htm -

The Method of Mathematics

by Gottfried Wilhelm Leibniz,

Preface to the General Science.

From RBJones.com, "The home of metaphysical positivism".

The Resurrection of Etaoin Shrdlu

One program that would have been easier to write

if we typed at computers in Lojban is

Shrdlu, Terry Winograd's natural-language

understander. Every AI textbook carries

a reproduction of Shrdlu's sparse virtual

world, a schematic robot gripper poised above

a collection of wire-frame pyramids and cubes

standing on a tabletop. Under this will invariably

be found

a sample of Shrdlu dialogue -

Person: PICK UP A BIG RED BLOCK.

Computer: OK. (does it)

Person: GRASP THE PYRAMID.

Computer: I DON'T UNDERSTAND WHICH PYRAMID YOU MEAN.

Person: FIND A BLOCK WHICH IS TALLER THAN THE ONE YOU ARE HOLDING AND PUT IT INTO THE BOX.

Computer: BY "IT", I ASSUME YOU MEAN THE BLOCK WHICH IS TALLER THAN THE ONE I AM HOLDING.

Computer: OK. (does it)

Shrdlu became famous as the example of

pronoun disambiguation and procedural

semantics. To

quote

Winograd:

The system answers questions,

executes commands, and accepts information in an interactive

English dialog... The system contains a parser, a recognition

grammar of English, programs for semantic analysis, and a

general problem solving system... It can remember and

discuss its plans and actions as well as carrying them out...

Knowledge in the system is represented in the form of procedures,

rather than tables of rules or lists of patterns.

Now the Semaphore Corporation have put together a collection of links to Shrdlu's creators and their reminiscences and source code. For this and other information, including Winograd's account of how Shrdlu gained its name from Linotype by way of Mad magazine, follow my links.

Logic, Rules and Spreadsheets

An early beta version of an Excel add-in that supports rules and logic is available for those interested in exploring the integration of inferencing and spreadsheet calculation and providing feedback on initial directions of the product. If you are interested, send an e-mail to Dennis.

Links

www.semaphorecorp.com/misc/shrdlu.html -

SHRDLU resurrection, from

Semaphore Corporation.

hci.stanford.edu/~winograd/shrdlu/ -

Terry Winograd's Shrdlu page, with

dialogue and an original screen display

showing Shrdlu at home in its blocks world.

hci.stanford.edu/~winograd/shrdlu/name.html -

Winograd on How SHRDLU got its name.

www.poplog.org/docs/popdocs/pop11/teach/msdemo -

TEACH MSDEMO, by Richard Bignell and Aaron Sloman.

The teaching notes for

MSBlocks, a mini-Shrdlu written in the Poplog

programming language. One of many AI tutorials

distributed with Poplog.

www.j-paine.org/students/practicals/popbeast/popbeast.html -

AI and PopBeast. Students' practical notes I once wrote describing

my Prolog-based AI which lived in a virtual world and interfaced

simple language-understanding to a planner. Another

mini-Shrdlu,

demonstrating planning, referent disambiguation,

and propositional representations.

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