Download Introduction to Game AI

Introduction to Game AI
© CSSE CITS4242 Game Design and Multi-Media
Expectations and Reality
• AI is an enormous topic, so don't expect to come away from
this unit an expert.
• But you will have learned the skills necessary to create
entertaining and challenging AI for the majority of action
game genres.
• Furthermore, you will have a sound understanding of the key
areas of game AI, providing a solid base for any further
learning you undertake.
Academic AI
• Academic research is split into two camps: strong AI and weak AI.
– Strong AI concerns itself with trying to create systems that mimic
human thought processes.
– Weak AI (more popular nowadays) with applying AI technologies to
the solution of real-world problems.
• However, both of these fields tend to focus on solving a problem
optimally, with less emphasis on hardware or time limitations.
– For example, some AI researchers are perfectly happy to leave a
simulation running for hours, days, or even weeks on their 1000processor Beowolf cluster so long as it has a happy ending they can
write a paper about. This of course is an extreme case, but you get my
point.
Game AI
• Game AI programmers, on the other hand, have to work with
limited resources.
• The amount of processor cycles and memory available varies from
platform to platform. Compromises often have to be made in order
to get an acceptable level of performance.
• In addition, successful games — the ones making all the money —
do one thing very well: They entertain the player (not to be
suboptimal is of least concern).
– After all, most players will quickly become frustrated and despondent
with an AI that always gives them a whippin'. To be enjoyable, an AI
must put up a good fight but lose more often than win. It must make
the player feel clever, sly, cunning, and powerful. It must make the
player jump from his seat shouting, "Take that, you little @?%#$!"
Opponents and Allies - AI
• The Acronym AI is often used to refer to
–
–
–
–
Enemy AI: Monsters
Friendly AI: AI, Parties
NPCs (Non-Player Characters)
‘Bots
• AI being “uber” (too deadly, German Word: over)
• ‘Bots are “nerfed” (a nerf is a change t either the rules of the
game or the behaviour of the game objects that weakens or
reduces power of the object or rule in question)
The Illusion about Intelligence
• But what is this mysterious thing we call artificial intelligence?
• With regard to game AI, it is often firm of the opinion that if the
player believes the agent he's playing against is intelligent, then it is
intelligent. It's that simple. Our goal is to design agents that provide
the illusion of intelligence, nothing more.
• Because the illusion of intelligence is subjective, sometimes this
takes very little effort at all.
• The designers of the AI for Halo, for instance, discovered their
playtesters could be fooled into thinking the AI agents were more
intelligent simply by increasing the number of hit points required to
kill them.
Playtesting on the AI illusion
• For one test session they allowed the agents to die really
easily (low hit points)
– the result was that 36% of the testers thought the AI was too
easy and 8% thought the AI were very intelligent.
• For the next test session the agents were made harder to kill
(higher hit points).
– After just this small change 0% of the testers thought the AI was
too easy and 43% thought the AI was very intelligent!
• This is an astonishing result and clearly shows the importance
of playtesting throughout the game development cycle.
More tips on creating the AI illusion
• It has also been shown that a player's perception of the level of
intelligence of a game agent can be considerably enhanced by
providing the player with some visual and/or auditory clues as to
what the agent is "thinking" about.
• For example,
– if the player enters a room and startles an agent, it should act
startled.
– If your game is a "stealth-'em-up" like Thief and a game character
hears something suspicious, then it should start to look around and
maybe mumble a few words such as "What was that?" or "Is anyone
there?"
– Even something simple like making sure that an agent tracks the
movement of neighboring agents with its head can contribute
significantly to a player's perception of the AI.
What not to do to ruin your AI illusion
• You must be careful though when designing your AI not to let
the cloak of illusion slip, since once it does, the player's belief
in the game character will evaporate and the game becomes
much less fun to play.
• This will happen if the AI is
– seen to act stupidly (running into walls, getting stuck in corners,
not reacting to obvious stimuli) or
– is caught "cheating" (seeing through walls, requiring less gold to
build units than the human player, hearing a pin drop at 500
meters)
• You must take great pains to avoid all of these pitfalls
The non-cheating AI
Different Levels of Intelligence
• Unit AI: Individual Units need to behave sensibly and
autonomously for the player to perceive as a being with
intellect.
– Autonomous Agents
• Reactive (chase, evade, attach etc)
• Goal-driven (defend from dragon, attach dragon, get food …)
• Group AI: communication between units (‘bots) in a group
– Team behaviour
• Master AI: control multiple groups, analyse the terrain and
make high-level decisions (more useful for RTS games).
Multi-tiered AI
Every 5-15
second
Every
Second
2-5 times per
Second
AI for Computer Games
• Script-Based AI
• Finite State Machines
• Searching and Finding Path
• Behaviour Trees
• Fuzzy Logic
• Genetic Algorithms
• Neural Networks
• Others
Script-Based AI
• The opponent acts according to a predefined script. A script is
nothing more than a series of simple actions:
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
Find a suitable base location.
Establish a base.
Build building A.
Build building B.
…
Train units.
Train three footmen.
Train two archers.
…
Attack Player P.
Repeat from Step 6.
Examples: scripting with NeoAxis
• The Logic Editor also supports a form of scripting, including a
special “Wait” command.
• Coroutines allow scripting directly in languages like C# that
support “yield” in enumerators. (As in previous lectures.)
• Alternatively a separate scripting language like Lua can be
used.
– Reference LuaInterface.dll in the solution browser
– Register C# methods as Lua functions.
– Load and run scripts at runtime with Lua calling C# and C#
calling Lua.
Finite State Machines
• In FSMs, the AI ‘bots can be in one of several states.
• There is also a set of rules to govern how to move from one
state to another.
• Comparing to script-based AI, FSMs are much more robust.
Scripted AI will stop working as intended if the situation
occurred isn’t in the script.
– Stationary (guards, spies)
– Move
• Follow paths
• Free Roaming
• Chase
Behaviour Trees
• Behaviour Trees are a recent and increasingly popular way of
describing behaviour.
• They naturally generalize scripts and hierachical FSMs plus
they fit well with goal directed behaviour like path finding.
• They originated in software engineering research about ten
years ago, at the Software Quality Institute at Griffith
University.
• They are now used in many mainstream games such as Halo 2
and 3.
Practical Path Finding
Points of visibility Navigation Graph
(left: Coarse, right: Fine)
Fuzzy Logic
• Fuzzy logic uses approximate reasoning to sort data into
vaguely defined set.
• In Fuzzy logic, a question of “are you hungry”, can be
answered as 30% to the “hungry” set and 70% to the “not
hungry” set. More interesting decisions can be made based on
this:
– If peckish, have a salad
– If somewhat hungry, have some spaghetti
– If starving, have the roast beef
• In comparison, Boolean logic, can only be answered with
“Yes”, or “No”.
Genetic Algorithm
• GA builds on the concept of evolution, concepts such as Natural
Selection and survival of the fittest, inheritance and mutation are
used.
– Start with a number of random solutions (each solution is an
individual)
– For each iteration of the algorithm (generation)
• Pick a number of good solutions (parents)
• Breed them together using cross-over operation (take genes from
parents)
• Form new solutions (children)
• GA can be used to solve problems with large search space or learn
the strategy used by a player and figure out how to beat them.
Artificial Neural Networks
• Similar to GA, ANN has its roots in biology. It tries to mimic
the way interconnected neurons in a brain operates.
• ANN can extact meaning from complex and imprecise data.
They can be used to identify patterns and trends that are too
complex to be noticed by either humans or other AI methods.
• They learn from examples, but cannot be programmed to
perform specific task. A trained network can be used to draw
good conclusions from similar cases.
– Good news is: they are fast at runtime
– Bad news is: they are hard to train, tweak and change.
Others
• Natural Language Processing and Natural Language
Generation
• Speech recognition and processing
• Still not widely adopted by the Gaming Community due to its
sheer complexity and the constraints placed by real-time
games
References
• "Programming Game AI by Example", by Mat Buckland
ISBN 1-55622-078-2 (Two copies in the UWA library.)
• “Programming an RTS Game with Direct3D”, by Carl Granberg,
Thomson, ISBN 1-58450-498-6 (Chapter 14)
• Advanced 3D Game Programming All in One”, by Kenneth C
Finney, Thomson, ISBN 1-900-733-3, Part II (Chap 5-9)
• AI-Junkie: www.ai-junkie.com