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Technical Issues: Artificial Intelligence

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Title: Technical Issues: Artificial Intelligence


1
Technical IssuesArtificial Intelligence
2
Technical Issues Artificial Intelligence
  • Artificial intelligence can mean a variety of
    different things in different contexts.
  • By purist definitions, a game would possess
    artificial intelligence if a game player cannot
    distinguish between characters controlled by a
    human, or by the game itself.
  • In such a case, the game would be passing a
    limited version of what is called the Turing
    test.
  • In actual practice, however, a game that does not
    pass this test still has artificial intelligence,
    just not really good artificial intelligence.

3
Technical Issues Artificial Intelligence
Screen shot from Unreal Tournament 2003. If you
cannot tell if an opponent or teammate is a
human or a bot, then the bots artificialintellig
ence has passed the Turing test.
4
Technical Issues Artificial Intelligence
  • Game developers rarely use the Turing test
    definition of artificial intelligence.
  • In a game, artificial intelligence refers to the
    code used to control all non player characters
    and opponents within a game.
  • The reactions of the game may be totally random,
    or totally logical, but the control code is still
    referred to as the artificial intelligence of the
    game.

5
Technical Issues Artificial Intelligence
Even though the opponent control for Centipede
(left) and block droppercode for Tetris (right)
is simple and scripted, with a random
number generator producing some variation, both
are still considered to posses a certain amount
of artificial intelligence.
6
Goals of Game Artificial Intelligence
  • Players have different expectations of the
    artificial intelligence they find in different
    types of games.
  • Some games, like Centipede and Tetris, have very
    minimal intelligence requirements.
  • Other games, however, are heavily dependent on
    good artificial intelligence. If it is weak, the
    game would simply not be worth playing.
  • Despite this, there are several general goals for
    any game artificial intelligence. The importance
    of these goals depends greatly on the game.

7
Goals of Game Artificial Intelligence
Screen shot from CompuChess. Without strong
artificial intelligence, a game of chess might
not be worth playing, except for beginners.
8
Goals of Game AIChallenge the Player
  • Providing a reasonable challenge for the player
    must be the primary goal for artificial
    intelligence in any game.
  • There are several ways to provide challenge
  • A very sophisticated artificial intelligence.
  • Outnumbering the player.
  • Giving opponents abilities and advantages that
    the player does not have.
  • Assigning the players teammates or additional
    obligations that might hold them back.
  • Cheating. (As long as you dont get caught!)
  • Poor game design. (Do not do this!!!)

9
Goals of Game AIChallenge the Player
Screen shot from Doom II. It created challenges
for players by vastlyoutnumbering the player,
and providing opponents many advantages
(unlimited ammunition, seeing in the dark,
flying, and so on).
10
Goals of Game AIChallenge the Player
Screen shot from Warcraft III. Sometimes, the
difficulty in selecting and controlling units in
the heat of battle provides an unwanted and
frustrating challenge.
11
Goals of Game AIChallenge the Player
  • Creating a challenging and sophisticated
    artificial intelligence can be quite difficult.
  • In some games, outnumbering the player and
    providing additional abilities is not what the
    player wants or expects.
  • In such cases, the artificial intelligence must
    be very good.
  • Depending on the game genre and game
    characteristics, the player must be challenged in
    different ways.

12
Goals of Game AIChallenge the Player
Screen shot from NHL 2003. It would break player
expectations by giving opponents extra abilities
or by outnumbering the player. The
artificial intelligence must be better to
compensate for this.
13
Goals of Game AIChallenge the Player
Screen shot from Alpha Centauri. Since it is a
strategy game, the player expects a strong
opponent as the game is very thought intensive.
Since thegame is turn based, the game cannot
overwhelm the player by processing alone the
player can take their time and think.
14
Goals of Game AIBe Realistic
  • A game should provide artificial intelligence
    that is appropriate to its setting, story, and
    characters.
  • Characters that are supposed to be smart should
    not do dumb things.
  • Characters that are supposed to be dumb should
    not do smart things.
  • The more human and realistic a character is, the
    smarter it should behave.
  • Beast-like, alien, robotic, and undead characters
    can get away with more stupid actions, depending
    on the situation.

15
Goals of Game AIBe Realistic
Screen shot from Quake. The Zombies behave
pretty much as one would expect they lumber
towards you and take your shots until they get
close enough for an attack. Unless they are
blown to bits, they willget up and come back for
more, just like real zombies!
16
Goals of Game AIBe Realistic
  • There are some things, however, that are so dumb
    that nothing should do it.
  • For example, walking off of a cliff or not being
    able to navigate around a small obstacle.
  • In these situations it is obvious to the player
    what the artificial intelligence should have
    done.
  • Unfortunately, players seldom recognize how
    complex or difficult such obvious actions are to
    recognize and perform.
  • To avoid ridicule, the games artificial
    intelligence must have a mastery of what is
    obvious to human players.

17
Goals of Game AIBe Realistic
Screen shot from Quake. The ogre was notorious
for getting stuck in doorways in many levels
with its chainsaw, and not knowing how to get
unstuck.To players, this seemed ridiculous, even
for an ogre.
18
Goals of Game AIBe Realistic
Screen shot from Oni. In this situation, Konoko
is being chased by Muro. A TCF officer on
Konokos side has beaten his enemy in the
background,and stands over her body for several
minutes. Why isnt he helping me?
19
Goals of Game AIBe Realistic
  • One must be careful, however, to make artificial
    intelligence too realistic.
  • Games are often unreal situations set up because
    they are interesting, not because they are
    authentic.
  • For example, if an opponent realizes it has no
    chance of winning, it should run away
    indefinitely, which quickly ceases to be fun.
  • In building good artificial intelligence, one
    must keep in mind the true goal of the project
    building a fun, playable game.

20
Goals of Game AIBe Realistic
Screen shot from 007 Nightfire. Realistically,
the villain should just killJames Bond and be
done with it, instead of toying around with
him. That wouldnt make for a very good game
though!
21
Goals of Game AIBe Unpredictable
  • Humans are unpredictable. This is part of what
    makes them good opponents.
  • The same should be true of the artificial
    intelligence opponents in a game.
  • Players want the artificial intelligence to
    surprise them and use strategies and techniques
    that are unanticipated.
  • If the player can predict with some measure of
    certainty what the game will do, the fun in the
    game quickly disappears.

22
Goals of Game AIBe Unpredictable
Screen shot from Starcraft. Strategy games
benefit greatly fromunpredictability. It does
not take long for a seasoned player to
recognize the same strategy over and over again.
23
Goals of Game AIBe Unpredictable
  • Successful unpredictability can take many forms,
    depending on the game.
  • Usually, this involves adding some element of
    randomness to the games artificial intelligence.
  • Could be pure randomness.
  • Could be a form of selection in which there are
    several valid choices of action that are chosen
    from randomly. Weights can be applied to vary
    the amount of randomness.
  • In the end, the player will never know the action
    was random, and will tend to attribute it to some
    intelligence with a purpose.

24
Goals of Game AIBe Unpredictable
Screen shot from Unreal Tournament 2003.
Opponents can act inan unpredictable fashion
through randomly selecting a weapon to use,and
use tactics appropriate to that weapon.
25
Goals of Game AIBe Unpredictable
  • Keep in mind that unpredictability should enhance
    the challenge presented by the artificial
    intelligence in a game.
  • If things are so random that the game cannot put
    together a solid plan for defeating the player,
    you have gone too far.
  • Make sure that random choices are still realistic
    given the scenario.
  • If an opponent is about to win, and its
    artificial intelligence randomly selects a poor
    action, this will seem ridiculous.

26
Goals of Game AIAssist Storytelling
  • The artificial intelligence in non player
    characters can be used for storytelling in many
    ways.
  • Setting mood.
  • Advancing the plot.
  • Providing foreshadowing.
  • And so on.
  • By telling the story in the game, as opposed to
    just cut scenes, the player becomes much more
    involved.

27
Goals of Game AIAssist Storytelling
Screen shot from Oni. Konoko has just been
declared a rogue agent, and civilians encountered
reflect this to help establish setting and mood.
28
Goals of Game AICreate a Living World
  • Creating a sterile game world filled with
    inanimate objects is not going to be a very
    authenticate reality for the player.
  • Instead, adding ambient life to a world can do a
    lot to enhance the experience.
  • Can be people going about doing their daily
    business.
  • Can be birds flying in the sky, or animals or
    other critters roaming around the ground.

29
Goals of Game AICreate a Living World
Screen shot from Grand Theft Auto III. This
series of games always had avery lively city,
full of motorists and pedestrians with
interestingbehaviours. Too bad for them!
30
Goals of Game AICreate a Living World
Screen shot from the Legend of Zelda The Ocarina
of Time. The world is full of lively artificial
intelligence agents, including the characters
wandering around, animals, and so on.
31
Goals of Game AICreate a Living World
These artificial intelligence agents can be
interacted with in a variety of ways. This is
what happens if you attack poultry too often
death by chickens!!!
32
Artificial Intelligence Tips
  • Before looking at some of the various techniques
    employed by artificial intelligence designers and
    coders, we first provide some general tips.
  • Some of these are common sense.
  • Others have come from years of experience, both
    good and bad.
  • Unless you have a good reason, it is advisable to
    follow these suggestions!

33
Artificial Intelligence TipsDo Your Homework
  • There is no one size fits all artificial
    intelligence system.
  • Different techniques are appropriate for
    different situations.
  • The right solution depends on many factors
  • The emphasis of AI in the game.
  • The scheduling and budget for AI development.
  • The development team make-up and experience.
  • It is important to determine the needs of your
    game and the capabilities of your team.

34
Artificial Intelligence TipsThe Sloped Playing
Field
  • It is generally not advisable to pin all your
    hopes on creating an artificial intelligence that
    can compete with human players relying solely on
    its synthetic intellect alone.
  • Generally, your artificial intelligence will
    require an edge to compete with a human player.
  • By outnumbering the player.
  • By having superior strength or abilities.
  • By having additional resources at its disposal.
  • By having perfect knowledge of the game world.
  • And so on.

35
Artificial Intelligence TipsThe Sloped Playing
Field
Screen shot from Doom II. It makes use of a
sloped playing field by vastlyoutnumbering the
player, and providing opponents many advantages
(unlimited ammunition, seeing in the dark,
flying, and so on).
36
Artificial Intelligence TipsUse the Environment
Wisely
  • Tune the design of levels to fit limitations of
    the artificial intelligence, instead of the other
    way around.
  • It is incredibly difficult to build artificial
    intelligence that can handle every level design
    in an effective and efficient manner.
  • Levels can be worked to fit with artificial
    intelligence limitations much easier and with few
    or no compromises.
  • Your non player characters and levels have to
    work together to make a game fun to play.

37
Artificial Intelligence TipsUse the Environment
Wisely
Screen shot from Damage Incorporated. Players
had to direct teammatesthrough a 3D environment.
Levels had to be redesigned many times
beforerelease because of artificial intelligence
problems in navigation.
38
Artificial Intelligence TipsUse the Environment
Wisely
  • Put smarts into the game environment itself, in
    addition to the characters.
  • Have objects in the world announce themselves to
    non player characters and provide them
    information and scripts on how to use them.
  • Embed navigation information in level design so
    that characters can traverse levels more easily.
  • By making a smarter environment, the artificial
    intelligence driving characters does not need to
    be as sophisticated.
  • Characters do not need to locate or identify
    objects or terrain they broadcast and tell them
    instead.

39
Artificial Intelligence TipsUse the Environment
Wisely
Screen shot from The Sims. The Sims makes use of
smart terrain thatbroadcasts what it offers.
For example, the refrigerator broadcasts that it
cansatisfy the hunger need and tells Sims how to
use it to do so when needed.
40
Artificial Intelligence TipsKeep it Simple
  • In the rush to beef up the artificial
    intelligence in a game, it is very easy for it to
    get out of control and too complex.
  • Ideally, the AI system should let the developers
    do complex things with a variety of simple parts.
  • By combining the simple parts appropriately, a
    variety of interesting and complex behaviours
    can be provided.
  • By keeping things simple, the system will be
    easier to understand, reuse, debug, and maintain.

41
Artificial Intelligence TipsPrecomputation is
Good
  • Good artificial intelligence can be
    computationally expensive at run-time.
  • Scarce resources are also needed for graphics,
    animation, physics, networking, and other
    subsystems though.
  • Precomputation should be used wherever possible
    to provide good AI cheaply.
  • Scripting of sequences of actions.
  • Navigation through game terrain.
  • Collisions with obstacles.

42
Artificial Intelligence TipsPrecomputation is
Good
Screen shot from Thief II. This game uses
navigation meshes to help characters navigate
terrain. By precomputing these in advance, and
usingthem in level design, character artificial
intelligence is simpler and cheaper.
43
Artificial Intelligence TipsTimeouts and
Fallbacks
  • Nothing looks worse than an artificial
    intelligence that repeatedly does the wrong thing
    over and over.
  • Players will not notice them make a wrong turn,
    but they will notice continuous collisions with
    an easy to navigate obstacle.
  • Every artificial intelligence system should check
    for success conditions within a reasonable amount
    of time.
  • If a timeout occurs, the system should give up
    and try something different.
  • At a minimum, it can fall back to interesting
    idle animations that express its confusion or
    frustration while a new plan is formulated in the
    background.

44
Artificial Intelligence TipsTimeouts and
Fallbacks
Screen shot from Grand Theft Auto. Police were
notoriously bad at moving around stopped
vehicles to arrest the player they could easily
get stuck or run back and forth. Timing out and
falling back would have been good.
45
Artificial Intelligence TipsAvoid Story
Interference
  • If the artificial intelligence in a game
    interferes with its story, it must be fixed.
  • The artificial intelligence system must be aware
    of game events that are important to telling the
    story.
  • This includes conversations, listening to dialog,
    watching a cut-scene, and solving game puzzles.
  • The AI system should know it should back off and
    not get in the way.

46
Artificial Intelligence TipsAvoid Story
Interference
Screen shot from Oni. Konoko was having a
conversation with the scientist inthe lab coat
when she was viciously interrupted.
Unfortunately, she missedthe rest of the story.
The guard was punished appropriately.
47
Artificial Intelligence TipsProvide Memories
  • The artificial intelligence characters in a game
    should remember what has happened to them and
    others during a game.
  • They can then change their behaviour and dialog
    accordingly.
  • This gives the player a sense that they are
    living beings with thoughts and feelings.
  • If the artificial intelligence in a game can
    learn and adapt from its memories of events, so
    much the better.

48
Artificial Intelligence TipsProvide Memories
Screen shot from Quake 3 Arena. Most game
characters and bots havememories. If you shoot
them and get on their bad side, they rememberit.
They will even keep grudges against each other
too!
49
Artificial Intelligence TipsVariety Through Data
  • A variety of character behaviours keeps games
    interesting and entertaining.
  • Providing code for each behaviour introduces
    programming, debugging, and testing headaches.
  • Instead, code should provide one or a small
    handful of behaviours that are greatly
    customizable through data.
  • Designers can then introduce a new behaviour by
    tuning these variables.
  • This includes awareness, speed, tactics, weapon
    preference, field and range of view, inventory,
    strength, abilities, chatter, and so on.
  • This data should be available to designers to
    assist in game balancing and adjustments.

50
Artificial Intelligence TipsVariety Through Data
Screen shot from Unreal Tournament. It provides
a wide variety of bot behaviours based on the
settings of a few parameters.
51
Artificial Intelligence TipsSolve the Right
Problem
  • Solving a different problem from the one thought
    to be at hand could result in a superior
    solution.
  • Always ask yourself
  • What is the real problem we are trying to solve?
  • Can I solve a different problem that is easier or
    cheaper and get a result that is equivalent or
    superior?
  • This process can be difficult but can also be
    very rewarding in the end.
  • It takes time and practice to do this effectively.

52
Artificial Intelligence TipsSolve the Right
Problem
Screen shot from The Sims. To solve one problem
(How can Sims locate and identify game objects
to satisfy their needs?), Will Wright actually
solved the real problem (How do I get the Sims
to behave intelligently?).
53
Techniques for Artificial Intelligence in Games
  • There are many different techniques used for
    providing artificial intelligencein games.
  • Most techniques come from mainstream work in
    artificial intelligence.
  • These techniques typically include
  • Search techniques
  • Rule based systems (finite state machines,
    decision trees, and production rule systems)
  • Game theory and game trees

54
Techniques for Artificial Intelligence in Games
  • Techniques used also include
  • Artificial life and flocking techniques
  • Planning techniques
  • Fuzzy logic
  • Scripting
  • There are many other mainstream approaches that
    are not used in practice very much yet, but that
    is changing.
  • This includes neural networks, genetic
    algorithms, belief networks, and so on.

55
Search Techniques
  • Search techniques are used to serve a variety of
    functions in game artificial intelligence.
  • Navigation and pathfinding.
  • Constructing action plans (more later).
  • And so on.
  • These techniques are suitable for both
  • Non player character control.
  • Control of player characters not being directly
    controlled by the player themselves.

56
Search Techniques
Screen shot from Starcraft. Starcraft requires
navigation and pathfinding for directing both
player and non player characters around game
levels.
57
Search Techniques Navigation without Obstacles
  • Without obstacles, navigation is a simple matter
    of determining which direction you need to go in
    to reach your objective.
  • Some simple algebra will do the trick.
  • If your movement directions are limited, pick the
    closest to the calculated result.

y
Start (x1,y1)
Goal (x2,y2)
x
Direction (x2-x1,y2-y1)
58
Search Techniques Navigation without Obstacles
Screen shot from Double Dragon. Navigation in
this case can be quite simple,as there are no
obstacles for non player characters to avoid.
They just needto home in on the player and
attack. (In later levels, there are
obstacles,but it is obvious that the AI did
little or no pathfinding around them!)
59
Search Techniques Navigation without Obstacles
  • The same approach can also be used for aiming the
    shots of projectile weapons.
  • With this however, you would also want to
    compensate for
  • Motion of the target (velocity and acceleration)
  • Motion of the shooter (velocity and acceleration)
  • Motion of the projectile (acceleration, maximum
    velocity, friction, wind resistance, gravity, and
    so on).
  • Depending on the skill of the shooter, you can
    tune how much compensation is actually done, and
    add random deviation to the chosen direction to
    avoid a perfect shot every time.

60
Search TechniquesNavigation with Obstacles
  • This problem is significantly more complex than
    the case without obstacles.
  • We now need our artificial intelligence to route
    characters around obstacles.
  • The path selected must meet several criteria
  • It must be as short as possible, or the least
    costly, depending on the situation.
  • It must look realistic and make sense to select
    the path chosen.
  • It must be selected in a reasonable amount of
    time without consuming too many scarce game
    resources.

61
Search TechniquesNavigation with Obstacles
  • A leading approach to doing this is known as the
    A or heuristic search.
  • There are several components to an A search
  • A map (or graph) that A uses to find a path
    between two positions. Maps are composed of
    nodes that are connected if a direct path between
    the nodes exists.
  • A cost associated with each direct connection
    between map nodes.
  • A heuristic used to estimate the cost between two
    map nodes that are not directly connected. It
    must be an admissible heuristic that does not
    overestimate the cost.

62
Search TechniquesNavigation with Obstacles
  • Each map node has three values associated with
    it
  • g The cost to get from the start node to this
    node.
  • h The estimated cost from this node to the goal
    node.
  • f The sum of g and h representing our current
    best guess of the cost of the path going through
    this node.
  • A maintains two lists of nodes an Open list,
    of nodes currently being explored, and a Closed
    list, of nodes that have been completely explored.

63
Search TechniquesNavigation with Obstacles
  • The A algorithm
  • Let S be the starting point.
  • Compute g, h, and f for S.
  • Add S to the empty Open list.
  • Let B be the node from the Open list with the
    lowest f value.
  • If B is the goal G, quit with success.
  • If the Open list is empty, quit with failure.
  • Let C be a node connected to B.
  • Compute g, h, and f for C.
  • If C is on the Open or Closed list, check if the
    new path is better with a lower f value, and
    update if necessary.
  • Otherwise, add C to the Open list.
  • Repeat 5. for all connections, then add B to the
    Closed list.
  • Repeat from 4.

64
Search TechniquesNavigation with Obstacles
  • There are many extensions to A
  • Terrain based A that has variable costs based on
    different types of terrain.
  • Hierarchical A to optimize paths for aesthetics.
  • Tactical A to select paths based on certain
    strategies (avoiding enemy detection, to gain
    superior firing positions, and so on).
  • Dynamic A that compensates for obstacle and goal
    movement throughout the map.
  • There are also many other search algorithms
    besides A that are also effective.

65
Search TechniquesNavigation with Obstacles
66
Search TechniquesNavigation with Obstacles
  • A can be expensive to compute, so we need to
    make some optimizations.
  • The best way is to simplify the search space as
    much as possible.
  • Use hierarchical pathfinding as much as possible,
    as it simplifies the search space.
  • Make sure a good heuristic cost is used.
  • Cautiously overestimate the heuristic cost, but
    you must be careful with this.
  • Preallocate as many search resources as possible.
  • Sometimes this is not the best approach

67
Search TechniquesNavigation with Obstacles
  • An alternative approach is to use precomputed
    navigation meshes.
  • An invisible mesh is used to represent all of the
    valid environment locations that can be occupied
    by game characters.
  • This mesh can be traversed in an incredibly
    efficient fashion using a variety of algorithms
    to control character movement.
  • Objects of use to characters can be placed along
    the mesh and searched for in an efficient fashion.

68
Search TechniquesNavigation with Obstacles
  • Navigation meshes can be used to facilitate what
    would seem to be very complex and intelligent
    behaviour.
  • Special points can be placed throughout the mesh,
    along with special links, indicating other
    actions can be successfully executed from those
    locations.
  • This can be used to support jumping, crouching,
    crawling, diving, climbing, and a variety of
    other seemingly intricate behaviour with little
    or no additional effort.
  • It makes characters seem smarter, but the smarts
    are part of the levels, not the artificial
    intelligence.

69
Search TechniquesNavigation with Obstacles
Screen shot from the Unreal Tournament 2003
editor. Paths and path nodesare added to ensure
that bots can navigate the environment. If they
areabsent, the bots will not be able to move
around and will get lost.
70
Rule Based SystemsFinite State Machines
  • Finite state machines are simple and effective
    approach to game artificial intelligence.
  • States represent the different game situations a
    character can face.
  • On the occurrence of certain events, or with the
    passage of time, the character will take certain
    actions and will change to a new state. The new
    state can either be a different state or the same
    one.
  • By encoding the appropriate events and actions in
    this fashion, state machines provide considerable
    power with little complexity.

71
Rule Based SystemsFinite State Machines
Opponent sighted in range Choose weapon, aim, and
fire
Not dead yet? Keep shooting
Running
Shooting
Opponent dead Go look for more
Out of ammunition Commence search
Hit jump point Start jumping
Done Go backto runsome more
Searching
Jumping
Simplified Finite State Machine for a First
Person Shooter AI
72
Rule Based SystemsFinite State Machines
  • There are also several extensions to the finite
    state machine approach
  • Hierarchical finite state machines allow states
    to be decomposed into their own separate finite
    state machines capable of implementing the parent
    state.
  • Nondeterministic finite state machines allow
    multiple transitions on the same input event to
    different states. This can be used to add
    unpredictability to actions taken in a clean and
    consistent fashion. Weights can be added to tune
    the probability that the various transitions will
    be selected.
  • Many more useful extensions exist.

73
Rule Based SystemsFinite State Machines
Been shot at Return fire
Running
Shooting
Been shot at Evasive maneuvers
Been shot at Start jumping frenzy
Dodging
Jumping
Nondeterministic Finite State Machine for a First
Person Shooter AI
74
Rule Based SystemsDecision Trees
  • Decision trees are another approach to artificial
    intelligence.
  • They are essentially complex if-then
    conditionals organized as a tree.
  • Decisions based on a set of inputs are made by
    starting at the trees root and, at each node,
    selecting the next child node to visit based on
    the values of the inputs.
  • The leafs of the tree contain the actions to take
    if tree traversal reaches the leaf.
  • A variety of algorithms like ID3 and C4.5 can be
    used to construct such trees.

75
Rule Based SystemsDecision Trees
Am I being shot at?
Yes
No
Is it a gun or grenade?
Is an enemy in range?
Gun
Grenade
Yes
No
Aim and shoot!
Can one be found?
Strafe to dodge
Jump to dodge
Yes
No
Go after it
Get moreammunition
A Simplified Partial Decision Tree for a First
Person Shooter.
76
Rule Based SystemsProduction Systems
  • Production systems are essentially a database of
    rules.
  • Each rule consists of an arbitrarily complex
    conditional statement, and a set of actions to
    execute if the conditional is satisfied.
  • Production systems are basically lists of
    if-then statements, with various conflict
    resolution mechanisms available in case more than
    one rule is satisfied at the same time.

77
Rule Based SystemsProduction Systems
  • Some example rules for a production system for a
    First Person Shooter
  • Enemy sighted
  • Aim and shoot at enemy
  • Out of ammunition
  • Launch search for more ammunition
  • Being shot at
  • Execute evasive actions

78
Game Theory and Game Trees
  • Some games can be thought of and treated as
    search problems quite effectively.
  • The different possible game states are
    represented in a tree-like fashion. Different
    moves in the game trigger transitions from a
    parent state to its children states below.
  • This tree is searched starting from its root (the
    current game state) to locate a game outcome
    resulting in a victory or the highest possible
    score for the player.
  • The set of tree nodes traversed indicates the set
    of moves required to result in this situation.

79
Game Theory and Game Trees
Current Position
A1
A2
A3
3
2
4
Ply 1
A11
A12
A13
A21
A22
A23
A31
A32
A33
Ply 2
3
12
8
2
4
6
14
5
2
A sample game tree for a game. After white
explores two plies of moves ahead, its best move
would be A1, to which yellow would respond with
A11 for a valueof 3. If white made a different
move, it would end up with a value of 2.
80
Game Theory and Game Trees
  • This approach can be very time consuming to
    explore and evaluate the various plies of
    possible moves.
  • It is usually impossible with most games to fully
    explore all of the possibilities.
  • Often, different approaches need to be used to
    improve overall efficiency.
  • Take advantage of opponent think time to explore
    additional plies.
  • Prune the tree whenever possible to reduce the
    number of possible moves to evaluate.
  • Apply game-specific heuristics to be selective
    during the process of exploration. For example,
    in chess, spend extra time exploring
    queen-capturing moves.

81
Game Theory and Game Trees
Screen shot from CompuChess. Games like Chess
and Go frequently make use of game trees. They
can be applicable elsewhere too imagine a
fighting game that has planned moves and counter
moves several plies deep!
82
Artificial Life and Flocking Techniques
  • Artificial life refers to systems of multiple
    artificial intelligence agents that attempt to
    apply some properties of living systems to
    themselves in a virtual world.
  • For example, the agents can be given a variety of
    basic needs that they must fulfill. Fulfilling
    these needs typically requires other needs to be
    met, and often leads to other needs as well.
  • Games such as SimCity and the Sims use these
    techniques to provide very interesting and
    entertaining gameplay.

83
Artificial Life and Flocking Techniques
Screen shot from The Sims. The Sims makes use of
artificial life techniques extensively. For
example, Sims feel hungry after a while and will
do what they can to satisfy that need, which can
lead to complex behaviours.
84
Artificial Life and Flocking Techniques
  • Flocking is a subset of artificial life to
    regulate and direct group behaviour.
  • Such systems employ the following general rules
    of flocking behaviour
  • Separation steer to crowding local flockmates.
  • Alignment steer toward the average heading of
    local flockmates.
  • Cohesion steer to move toward the average
    position of local flockmates.
  • Avoidance steer to avoid running into local
    obstacles or other terrain features.
  • Survival steer to meet basic needs as necessary
    or to avoid danger if a predator seen.

85
Artificial Life and Flocking Techniques
Screen shots from several flocking Programs with
different behaviours.
86
Planning Techniques
  • Planning techniques are an extension of search
    methods that emphasize the subproblem of finding
    the best sequence of actions to achieve a
    particular result.
  • Start with an initial game world state.
  • Also start with a precise definition of the
    consequences of each possible action.
  • Planning then moves forward from the start, or
    backwards from the desired result to discover the
    sequence of actions required.
  • Such techniques have met with some success.

87
Planning Techniques
Screen shot from Warcraft III. Game artificial
intelligence must formulate complex plans in
building units and structures, due to their
dependencies. (Other techniques could have been
used for the same result.)
88
Fuzzy Logic
  • Fuzzy logic uses real-valued numbers to represent
    degrees of truth or set membership as opposed to
    the Boolean (true or false) values of traditional
    logic.
  • Fuzzy logic techniques allow for more expressive
    reasoning and more richness and subtlety than
    traditional logic.
  • They more accurately capture situations in which
    things are not either black or white.
  • More often than not, things are shades of grey.

89
Fuzzy Logic
Screen shot from Grand Theft Auto. Fuzzy logic
is good for modeling trafficbehaviour with
multiple autonomous drivers in a game such as
this to control braking, steering, and
acceleration to avoid accidents.
90
Scripting
  • Game artificial intelligence does not need to
    spontaneously think up every behaviour performed
    in a game.
  • A combination of dynamic and scripted behaviours
    often works best.
  • If the game designer can make assumptions as to
    what is happening in the game, scripted behaviour
    can be used quite effectively.
  • For example, if a player is forced to enter a
    room from a certain direction, scripting can be
    used to provide an interesting challenge to the
    player.

91
Scripting
  • When setting up scripts, the level designer must
    be involved.
  • This way scripts can be written so that
    characters can take advantages of level features,
    such as hiding places, ambush locations, and game
    items.
  • The game must also be able to detect when a
    scripted plan is not working out.
  • It must switch to another script, or attempt some
    kind of unscripted action.
  • Avoid predictability and ensure reactivity.
  • Select scripts randomly, depending on the
    situation.
  • Keep scripts short, and use them as building
    blocks.
  • Try to build scripts that only provide hints of
    actions.

92
Scripting
Screen shot from Half-Life. This game was widely
praised for the strength of its artificial
intelligence. In fact, what was perceived as
intelligence was doneusing scripted paths that
game characters would follow in certain
situations.
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