"A.I."
...and Games

(Part One)

Artificial Intelligence

A.I. is a huge, complex, fascinating, and philosophically profound area of intellectual inquiry, which may yet have a huge impact on the very future of our species
(and of its computerised descendants).

And I, for one, welcome our new A.I. Overlords!

Sadly, these lofty matters have almost no relation to the feeble and simplistic rubbish that we put into computer games. :-(

But let's take a look at some of the Good Stuff anyway... :-)

"Real" A.I.

The origins of serious A.I. go back to 1950, and a famous
paper called "Computing Machinery and Intelligence",
written by the English mathematician Alan Turing*.

You should read it.

It is fairly accessible (being aimed at mere philosophers),
but is rather brilliant, and still relevant.

It begins with the line:
"I propose to consider the question, Can machines think?"

(*Turing was portrayed by Benedict Cumberbatch in 2014's "The Imitation Game").

(But a better version is Derek Jacobi's performance in 1996's "Breaking The Code").

The Imitation Game

Turing framed the question in terms of whether a machine could successfully imitate the intellectual capacities of a human being, as judged by another human.

There's a lot "wrong" with this formulation, as Turing well knew (it's all in his paper), but it does have some advantages as a practical definition...

It avoids quibbling too much over mechanism and, despite constraining intelligence to the human mould, it seems like a legitimate (and sufficient, but not necessary) A.I. goal.

The Turing Test

Ancestor of "The Voight-Kampff Empathy Test"?

Turing Says...

What is a machine?

How about: "Anything human engineers can create!"

...except by making babies ("in the usual manner"),

which would be cheating!

...yes, cloning in the lab is cheating too!

Grrr. Damn.

New definition: "Our machine will be a digital computer."

(In 1950, these are just new, and require explanation!)

Digital Computers

So, what are these new-fangled "digital computers"?

They are general purpose machines which can follow any set of (precisely defined) instructions that you give to them, and
which execute those instructions reliably and slavishly.

They are like office clerks! (aka "human computers").

They have "memory" (e.g. notepads), and are capable of performing basic arithmetic, and making "if/then"
decisions based on the results thereof.

Discrete State-Machines

...is what digital computers really are.

"...given the initial state of the machine and the input signals
it is always possible to predict all future states."

(Turing knew about Replay Systems!)

"For instance, the number [of possible states] for the machine now working at Manchester is about 2^165000,
i.e. about 10^50000"

That's just 20 KiloBytes, in our modern terms!

A Sound Of Thunder

"The displacement of a single electron by a billionth of a centimetre at one moment might make the difference between a man being killed by an avalanche a year later, or escaping."

Yes, he knew about "The Butterfly Effect" too,

almost 20 years before Edward Lorenz coined the phrase.

Likewise "Chaos Theory" (named in 1975), although the foundational work goes back to Poincaré in the 1890s.

BTW: Ray Bradbury covered similar territory in his 1952 science-fiction short story "A Sound Of Thunder" (text)

Arguments Against A.I.

God, and our "immortal soul" *
"Heads in the Sand"
Maths, Gödel, and the limits of logic
Consciousness ("is magic"); solipsism
Machines can't do <X>
Lady Lovelace's Objection; originality
The Nervous System is not discrete
Informality of Behaviour ("rules")
ESP! ** -- Turing actually believed in this!

* including claims of "the Moslem view that women have no souls" -- this is an error on Turing's part, I believe!

** "the statistical evidence, at least for telepathy, is overwhelming" -- no it bloody isn't! Turing was wrong here.

What Turing Believed

"I believe that in about fifty years' time [i.e. in the year 2000]
it will be possible to programme computers, with a storage capacity of about 10^9, to make them play the imitation game so well that an average interrogator will not have more than 70 per cent chance of making the right identification after five minutes of questioning."

"A storage capacity of 10^9 [bits]" is about 120MB.

A Mac G4 Cube from 2000 had 128MB RAM (+20GB disk).

It couldn't fool you in a Turing Test though...

Loebner Prize

A real-world Turing Test competition.

I couldn't find the transcripts from the contest in 2000,
but here are some examples from the 2001 "winner":

JUDGE02[02:36:40] may I ask you some questions?
PROGRAM[02:36:40] Sure ask me anything.
JUDGE02[02:36:52] who composed Beethoven's piano concerto?
PROGRAM[02:36:52] Charlie Parker. :-)
JUDGE02[02:37:08] excellent. can you name another composer of music for me?
PROGRAM[02:37:08] Thanks for the compliment.
PROGRAM[02:37:08] Linus Torvalds. :-)
JUDGE02[02:37:21] have you heard of Caspar Hauser?
PROGRAM[02:37:21] No, tell me about him, her or it.
JUDGE02[02:37:40] later maybe. WHen did the 1820 settlers arrive?
PROGRAM[02:37:40] I understand.
PROGRAM[02:37:40] That is all water under the bridge. 
JUDGE02[02:37:50] I agree. Are you a computer?

But We Were Promised This!

(from 02:21)

:-(

"Prediction Is Difficult..."

...especially about the future.

"I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted." --- Alan Turing, 1950

JUDGE:  I like Waiting for Godot. What is your favorite play?
ALICE2: I'm very patient. I'm not sure is I have a favorite play.  
        My favorite actress is Katie Holmes. Who is your favorite actress?
...
JUDGE:  What emotions are you now feeling?
ALICE2: I am unable to establish a network connection.

--- World's best A.I. bot Alice2 in "conversation", 2013

The Goost In The Machine

On 7th June 2014 (the 60th anniversary of Turing's death),
a chat-bot called "Eugene Goostman" was alleged
to have passed the Turing Test.

This claim is controversial for a number of reasons, and the promised technical write-up (with full transcripts) has yet
to appear. It's pretty clear that Eugene is not a radical
breakthrough of any kind, but I don't think we can
entirely dismiss the claim that it "passed" either.

I wrote a blog-post about the whole thing here.

Meanwhile, in Games...

...people were still writing "academic dissertations", in 2005, about such advanced and earth-shattering topics as: how to implement minor improvements to the "A.I." seen in 2D platform games such as 1986's "Bubble Bobble"!

It's all quite depressing, really.

Does this even count as A.I.? To me, that's very debatable.

It's certainly nowhere near the grand goal of
"Thinking Machines" --- and, yet, it does represent the simulation of a primitive type of (hypothetical) brain...

More Intelligent Than A Rock

In games, "A.I." seems to be understood as referring to
the simulation of entities which have any kind of apparent volition in their actions. i.e. if it has more apparent mental capacity than a bullet, or a lump of rock, it is "intelligent".

In fact, in some genres, the definition of "intelligence"
is stretched so far as to include "football players", and
the "soldiers" (or even the fish!) in "Call Of Duty" etc.

Simple Game Agents

In simple games, the so-called "A.I." belongs to the NPC
(Non Player Character) "agents", whose goals and activities
in the game are pretty trivial --- often taking a form such as:

"Keep walking alternately left-and-right along this platform, until the player character gets close, at which point you can then shoot at him ineptly until you are eventually killed."

Implementing this requires a tiny amount of state/memory (e.g. "which way am I going", "have I seen the player yet"),
and access to a "spatialManager" for range-checking.

Extensions

Trivial "intelligence" such as that described above can be augmented with a bit more complexity in the form of, say, simple tracking and/or avoidance behaviour, controlled jumping, use of line-of-sight and "cover" and maybe even basic tactics (e.g. weapon selection based on range).

There's nothing particular fancy or interesting about most of this though --- it's just standard "entity update" logic.

Line-of-sight and jumping involve a bit of geometry work,
but they are not exactly brain surgery!

Simple Brains

You can sometimes get quite convincing results in game A.I. by following through on the idea that you are simulating simple brains and their connected sensory systems...

You can give your characters "sense organs" by using range queries to approximate notions of limited visibility, hearing and (potentially) smell.

You can give them "moods" in the form of "state machines", and even the ability to weigh-up competing goals (e.g. the
aim of killing enemies vs the desire for self-preservation).

Wild Metal Country

In "WMC" (1999), we built a system where the AI creatures were configured with a set of miscellaneous goals and sub-goals, whose "strengths" would change over time, based on various pertinent factors (e.g. health level, distance from
home, proximity of "friends" etc), and which could be
weighed against each other in the manner of competing "emotional forces".

At any particular time, the dominant goal would be pursued, but with others being (re-)calculated "sub-consciously" (!),
and switched over to if they became strong enough.

Active Engagement

The kind of "agent" systems that we've just been describing can work quite well for some situations, but they are perhaps best suited to representing multi-agent contexts in which the environment is sufficiently diverse and dynamic that the magic of "emergent behaviour" can take over and make
things appear more "intelligent" than they really are.

One-on-one situations are much harder to pull off...

One-on-One Conversations

This, as the Loebner Prize illustrates,
is actually rather difficult to achieve!

But there is a famous (and deliberately parodic) example from 1966 which was sometimes convincing (to the naive)...

It was called "ELIZA"*, and you can talk to it here.

This was the first "chat-bot", and it usually pretended to be a psychiatrist, because this behaviour can be simulated despite

"knowing almost nothing of the real world" (!) ;-)

(* Try asking Siri about Eliza sometime.)

How Eliza Worked*

Eliza used a simple form of sentence "parsing", in which it would look for pre-defined keywords, and then perform
some corresponding transformation rules (including basic word inversions such as converting "I" to "you"), before essentially spitting the input back to the user
in a slightly modified form.

The principles behind this became the basis of the text "parsers" which were later used in interactive text-based games such as "Adventure / Colossal Cave", "Zork", and
"Hitchhikers Guide to The Galaxy"

(*The full original paper can be found here.)

Making ELIZA Better

In his full paper, Weizenbaum suggests that an ELIZA-like system could be made more interesting and convincing
as a conversationalist by allowing it to collect additional knowledge over time --- whether that is information about
the external world as reported to it by the users, or inferred data about the users themselves (e.g. their opinions, interests, family relations, etc.). Capturing such data is tricky though!

I think it would also help to give the system an apparent "agenda" of its own e.g. curiosity about certain topics,
or an objective of some kind...

From Conversations to Games

A Nice Game Of Chess?

"We may hope that machines will eventually compete with men in all purely intellectual fields. But which are the best ones to start with? Even this is a difficult decision.

Many people think that a very abstract activity,
like the playing of chess, would be best."

--- Alan Turing, 1950

But let's start with something a little simpler than Chess...

Tic-Tac-Toe

The core concepts are basically the same as Chess:

Two players, alternate turns, "perfect information"
(i.e. both players can see the full board), and clear win/lose/draw outcomes under fixed rules
of an essentially "simple" character.

What's our strategy for playing/implementing this?

Strategy: "Use Brute Force"

(Sad, but true)

Just consider every possible move, and pick the "best" one.

But "best" depends on the potential counter-moves of the opponent.. and on our potential replies to those.

So just look as far ahead as we can through the "tree" of possible moves, within the constraints of our memory
(and time).

This is probably a crude approximation of what people do,
but it's far more mechanistic, and isn't "intuition" driven.

A Game Tree

If we merge boards which are symmetrically equivalent,
the tree for Tic-Tac-Toe is almost "practical" to store...

Minimax

We can then go to the "leaf" nodes of our game-tree and
give them a score depending on whether they represent
a win/draw/lose situation for us.

Next, work from the bottom nodes up, and assume that
each player will pick the move which gives them the most favourable score: so, I pick the highest available score,
my opponent picks the lowest (from my perspective).

This represents the idea of two (equally capable) players,
each doing their best. These scores trickle up the tree until
it is completely filled-in, and they will tell us what to do...

A Minimax Tree

Hopefully, you can see how the scores "bubble up",
and inform the selection of moves at each level...

Tic-Tac-Toe Is Easy

The range of possibilities in Tic-Tac-Toe is actually small enough that the game can be formally analysed and "solved"

--- meaning that we can create a perfect strategy which,
if applied correctly, ensures that we cannot lose.

Two such players will, of course, simply "draw" all the time.

(If the opponent makes a mistake though, you can win.)

Chess

The size of the game-tree in Chess is huge, so much so
that even modern machines cannot explore it fully.
(BTW: The tree for "Go" is even larger).

This means that the computer can't generally see to the end of the game, so it has to stop short: it is therefore forced to give a "static evaluation" score to those incomplete "leaf" positions, usually based on adding-up points for the remaining pieces.

Also, there are too many valid moves to consider,
so the search tree must be "pruned" in various ways.

(e.g. Alpha-Beta pruning).

In Practice

These techniques of minimax and tree-pruning were successfully used to create basic Chess playing programs as early as the 1950s. Turing came up with one in 1948, but it
was too big to implement on the "Manchester Machine",
so he executed it himself "by hand", taking 20 minutes or
more to compute each move. (Also, it wasn't very good!)

A working, but simplified, Chess program was developed
for the Manchester Machine by Dietrich Prinz in 1951.

Claude Shannon also did some beautiful work at the same time, including writing the first paper on computer chess.

Ultimate Victory

From 1950 onward, the basic approach was simply to
"throw more computing power at the problem".

By using faster machines with larger memories,
and by improving some parts of the playing "strategy",
great practical (but little theoretical) progress was made...

...reaching the point where, in 1997, IBM's "Deep Blue"
computer defeated human World Champion
Garry Kasparov.

...who promptly accused it of "cheating".

HAL Playing Chess "in 2001"

Other Competitive "Games"

The ideas behind computer chess can be, and have been, extended to other domains:

Learning

(Watch the first 30 seconds of this clip).

What is "Learning"?

I think it's actually quite difficult to define but, for our purposes, I would say that it has something to do with the ability to react differently to a repeated situation or stimulus, as a consequence of prior experience, analysis... or "thought".

Compare this with the popular definition of "insanity":

"Doing the same thing over and over,
and expecting a different result."

--- generally attributed to Albert Einstein

(incorrectly, of course)

How Do We Do This?

Well, according to my (imperfect) definition, the learning process has to be embodied in some kind of long-term persistent, but mutable (i.e. changeable) state.

It requires memory.

And the ability to change.

For example, a "learning" Chess program would be able to react to previous results and somehow adjust its strategy.

Turing suggested that it could do this by e.g. tweaking the piece values that it uses in the "static evaluation" function.

Reward and Punishment

Turing also claimed (in his 1950 paper), that generalised learning could be implemented via a form of "training",
where the machine would be given feedback on its
behaviour, and would adapt accordingly.

This is actually quite tricky, because it isn't generally clear
what change should be made in response to simple feedback.

If you are "punished" for losing a game, that in itself
doesn't tell you what to change in future!

Nevertheless...

Despite the potential limitations, it seems plausible that a program (perhaps one designed for a specialised domain), could be made to implement some primitive form of "learning" behaviour over time.

This has, in fact, already been done (arguably)
in the form of so-called "genetic algorithms".

TO BE CONTINUED....

"A.I."...and Games (Part One)