"A.I."
...and Games

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, 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").
(A more realistic version was probably 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."

(Just like a Replay System!)

"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.
--- A.I. bot Alice2 in "conversation", 2013

The Ghoost In The Machine


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

This claim is controversial for a number of reasons, and is disputed by many observers. 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.

Progress?

Despite the many failings, there has been some progress
in the field of chat-bots: weird, upsetting, progress...


(End of Part One)

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.

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:

They're not making this up!


"WarGames" is (mostly) fiction,
of course, but it's well-informed fiction
inspired by real stuff!

For example, the U.S. "RAND Corporation"
really did do a lot of work on the "theory of games",
including its applications to the Game Of War.

Here's an example of their output.

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".

(End of Part Two)

Artificial Neural Networks


The idea of a learning-mechanism can be extended to
perhaps its most general form, if we could somehow
learn enough about neuroscience to...

SIMULATE A BRAIN

Our current attempts at this are embodied in the field of "Neural Networks". These were popular when I was at Uni (early 1990s), but fell out of favour because it was too hard
to make them big or complex enough to be useful.

What's a Neural Network?

It is a bunch of simple processing elements called "neurons", connected to each other via a (weighted) graph e.g.

Realistic ones, naturally, have to be much more complex.

Neurons


The "neurons" of an ANN are modelled on the roughly equivalent structures which exist in biological brains.

In Artificial Neural Networks (ANNs), we implement them
as simple combiner mechanisms which map multiple input signals into a single output signal.

They often do this by creating some kind of weighted sum
of their inputs, and then modulating that further with
a non-linear response function (e.g. a sigmoid).

Training


The clever thing about an ANN is that it can be "trained" in such a way as to encourage it to obtain some desired set of outputs from a given set of inputs.

e.g. You could give it a mixture of pictures of cats and dogs, and ask it to output "cat" or "dog" accordingly for each.

Initially, it will be rubbish at this but, with repeated training,
it will adjust the "magic numbers" inside the neurons until it gets better at the task. Eventually, given a fresh (untrained) image, it could (ideally) make a correct identification.

Back Propagation


The secret to training a neural network is to work out how to change the weightings inside the network in such a way as to make it better approximate the desired output (without, at the same time, "forgetting" whatever else it has already learned).

One way of doing this is via a "Back Propagation" algorithm.

The basic idea here is to take the difference between the actual and expected outputs, and apply that "delta" back through each layer of the network, making minor corrective adjustments to the weightings as you go...

A Big Subject


ANNs are a very large, and somewhat difficult, subject area
in their own right. I can't possibly go into all the details here, but there is a good online course by Geoffrey E. Hinton
(a pioneer in the field) that you could take (for free)!
Or a one hour summary video by him here.

ANNs are currently being used to recognise speech (oops!), images and handwriting, to perform automatic translation
of natural languages, and various other things that were previously considered to be tasks requiring real
"intelligence".

Recent Developments


When I first ran this course in 2013,
the new wave of Neural Nets was just beginning,
and it hadn't really registered on most people's radar...

Since then, it has taken off massively!

Neural Nets are winning in almost every
aspect of A.I. now, and can do amazing-looking
things like writing automatic captions for images,
or creating wonderful dog hallucinations...

Deep Dream



Deep Grocery Shopping



..or visualising strange poems



They Can Even Play Games!

This isn't (entirely) a custom "hack"


Don't Believe The Hype

...all very impressive... but...

The DeepMind thing is rubbish at games like
Space Invaders, which is mentioned in NONE
of the reporting that I've seen (and I've seen a lot).

So, how do I know its limitations?

BECAUSE I READ THE GODDAM PAPER!!!

The Smoking Gun...



Back Down To Earth


ANNs are very clever and fancy,
but I haven't got time to say more about them,
and will now have to focus on some of the more
fundamental and "traditional" stuff instead...

Route-Finding

One of the standard so-called "A.I." problems in games
is route-finding: i.e. getting from A to B...

...well, getting from A to B is usually quite easy.
Getting from A to Z, however, is generally much harder.

There is, of course, a "naive" route-finding algorithm
which works roughly as follows:

1. Point in the direction of 'Z'.
2. Keep going that way.
3. Hope you don't bump into anything.

Naive Route-Finding Fail

...Unfortunately, that one doesn't always work very well.

Even so, this basic strategy, augmented by a "wall hugging" rule for navigating around obstacles, sometimes works OK.

But one can easily construct (or discover) cases where it
fails though, and that's when we need something better...

Planning a route, in the general case, is a bit like a single-player game of chess: i.e. you just have to plan a bit ahead
at each move in order to approach (or reach)
a desirable end-goal.

A Route Tree

At each point on your journey, you have
a range of options for where to go next.

...So you could build a "Route Tree", with a branch for
each option, and then search through it just like any other
"Game Tree", giving scores to the leaf positions (based on
how near they are to the final destination) and then
bubbling those back up to the starting position.

Unfortunately, even if you restrict yourself to a fairly simple set of fixed directions (e.g. 8 compass points), most routes
are long enough that such a tree quickly becomes unwieldy.

Multiple Paths


Also, on a realistic map, there are multiple paths going through any particular point, but we're only generally interested in the shortest of these paths.

We can avoid exploring the redundant long-paths, by "growing" our solution along an expanding contour of
shortest-paths-so-far.

This approach is called Dijkstra's Algorithm...

Dijkstra's Algorithm

Watch how it "routes" around the obstacle...
After the target node has been found, it is easy to backtrack through the preceding "short paths" to find the whole route.

Grids vs Graphs

We've just seen Dijkstra's algorithm processing a "grid" type map, but real ones are often represented as "graphs" instead.

A graph representation can be more economical,
because it only stores important "landmarks".
In games, this is called a "Navmesh".

Navigation Meshes

A Navmesh is a simplified representation of a map, which is designed to accelerate or simplify route-finding. They're usually pre-computed from the detailed map geometry.

A* Search

(Pronounced "A-Star Search")

Dijkstra's algorithm is nice but, if you look at how it behaves on the grid-based example, you'll see that it covers a lot of nodes which a human would say were obviously "off the beaten track" and very unlikely to be part of the route.

This is because it explores All The Nodes.

A more efficient approach would be to preferentially
explore the "most promising nodes".

Most Promising?

But if we somehow magically knew which nodes were
actually the "most promising", we'd just follow those ones directly to begin with, and the problem would be solved!

The point is that our idea of "most promising" can only be a guess (aka a "heuristic") and, as such, it might be wrong.

But, if it's a reasonable guess, it could still be useful as a guide... in particular, it would encourage us to pursue some paths more eagerly than others.

A simple, but effective, reasonable heuristic for a search is
the "straight line distance" to the destination.

A* Is Born!

The A* Algorithm works by employing a guiding heuristic of this kind, and it results in a rather efficient search.

Note how it starts to take the "straight line" path (guided by the heuristic), but then explores the other paths when it has to.

A* In Action


Sensory Search

...in fact, A* is sometimes a little bit too good.

It has "perfect knowledge" of the map (more than we might expect an in-game character to have, perhaps?)

A more "realistic" in-game search might impose some restrictions on this e.g. by only "knowing" about obstacles within some sensory range of the character.

BTW: All of these techniques also allow for different "terrain types" etc. This is done simply by modifying the "transit cost" of each node so that it is higher for rough terrain.

Wrapping It Up...

Turing In Context

It has been said that the question of "Can machines think"
is as meaningless as asking "Can submarines swim?"

...or, perhaps, "Can aeroplanes fly?""

The point being that machines can, increasingly, solve the kinds of problems that we solve by "thinking", but they may
do so using other means, more suited to how they operate.

But my own opinion is that, in principle, a machine could be made to "think", in our sense of the word. And I also think
that, one day (unless we destroy ourselves first), they will.

Turing's View


"This is only a foretaste of what is to come, and only the shadow of what is going to be. We have to have some experience with the machine before we really know its capabilities.

It may take years before we settle down to the new possibilities, but I do not see why it should not enter any
of the fields normally covered by the human intellect
and eventually compete on equal terms."

--- Alan Turing, 1949

A Strange Game



Special Appearance by Iceland


Dark Star

(co-written by and co-starring the late great Dan O'Bannon
--- the writer of "Alien" and "Total Recall")

PS: Some Words Of Caution


Cognitive Scientist Steven Pinker says:
"Human-level AI is 15-25 years away (& always has been)",
in his twitter summary of this amusing paper.

Rodney "Roomba" Brooks says we shouldn't worry.

Nevertheless, the 2019 Loebner Prize winner was
much better than the older ones!

(But 2014's Trollbot is still my favourite).

Copy of "A.I." and Games

By Pat Kerr

Copy of "A.I." and Games

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