Artificial Intelligence in Game Design

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Artificial Intelligence in Game Design

• Goal-Oriented Action Planning

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Planning

• Creating series of actions to meet some goal
• Planning actions to meet multiple needs
• Chosen so final state has highest utility
• Intermediate states after part of sequence should
  not be unacceptable
• Allows fast actions to be chosen in logical
  circumstances
• Planning actions with multiple steps before
  payoff
• Purchase ingredients
• Cook ingredients in oven
• Eat meal
• No effect on hunger until after last step

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Planning Example
Action Effect on Fun Effect on Energy
Paint -2 1
Go to Concert -9 3
Nap in Chair 1 -2
Sleep in Bed 5 -10
Need Current level After Paint After Go to Concert After Nap in Chair After Sleep in Bed
Fun 8 62 02 92 132
Energy 6 72 92 42 02
Total effect 100 85 81 107 169
Best action if single action allowed Will be very
tired afterward!
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Planning Example

• Better approach for two large needs
• Take fast action to relieve one
• Then take another action to relieve other
• Example
• Take nap before concert

Need Current level After Nap in Chair Then After Go to Concert
Fun 8 92 02
Energy 6 42 72
Total effect 100 107 49
Better than taking single action
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Planning and Search Trees

• Must try all possible combinations of actions
• Compute total discontentment for each path
• Choose path with lowest total

root
Sleep in Bed
Nap in Chair
Go to Concert
Paint
Paint Paint
Paint Go to Concert
Paint Nap in Chair
Paint Sleep in Bed
Nap in Chair Paint
Nap in Chair Go to Concert
Nap in Chair Nap in Chair
Nap in Chair Sleep in Bed
Go to Concert Paint
Go to Concert Go to Concert
Go to Concert Nap in Chair
Go to Concert Sleep in Bed
Sleep in Bed Paint
Sleep in Bed Go to Concert
Sleep in Bed Nap in Chair
Sleep in Bed Sleep in Bed
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Planning and Search Trees

• Expanding search tree is costly
• Given a possible actions
• Given n possible levels
• an possibilities to test
• Will only be able to test small number of action
  combinations (lookahead limit)
• Even two actions enough to seem intelligent
• Looks like character thinking ahead

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Planning and Search Trees

• Algorithm depth limited search
• Depth-first search to some fixed limit n
• an leafs in tree
• At each leaf, compute discontentment
• Just keep track of best path found so far
• Storage cost n

Best sequence of actions found so far
Current path being tested
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Pruning and Search Trees

• Can cut off search down obviously bad branches
• Branches with intermediate state unacceptably bad
• Can possibly save search time (but no guarantee)

Character dead No path can be better than best
found so far, so no further search
Character dead No path can be better than best
found so far, so no further search
Discontentment 172
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Planning and Preconditions

• Actions may have preconditions before they can be
  taken
• Multiple actions required to meet needs

Action
Preconditions
Postconditions
Action
Preconditions which character meets
Postconditions
meets
Action
Preconditions
Postconditions meet need
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Planning and Preconditions

• Example Microwaving food

Get food
Uncooked food in fridge
Have uncooked food
meets
Cook food in microwave
Have uncooked food Have microwave
Have cooked food
meets
Eat food
Have cooked food
Hunger - 5
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STRIPS Planning

• Simple planning for worlds with preconditions on
  actions
• Stanford research intelligent planning system
• Prohibitively expensive for complex problems
  (thousands of possible actions, prerequisites,
  etc.)
• Widely used in games where actions
  limited(Quake, Age of Empires, etc.)
• Plan of attack for single NPC
• Series of actions to meet large-scale
  goal(increase number of citizens farming)

Action
Preconditions
Postconditions
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STRIPS Planning

• Regressing searching Working backwards from
  goals to preconditionsgoallist unmet
  needswhile (unmet goals in goallist) choose
  goal G from goallist select action A with G in
  postconditions remove G from goallist add
  unmet preconditions of A to goallist

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STRIPS Planning Example

• Initial goal
• Choose attack action which meets initial goal
• Precondition added to list
• Choose load weapon to meet precondition
• Its precondition added
• That precondition already true, so plan complete

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Selecting Actions

• Different characters ? Different set of possible
  actions
• Algorithm selects from that characters actions
  which meet prerequisites

Get Key
Key in current room
Door unlocked
Open Door
Door unlocked
Be in room
Smash Door
Be in room
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Planning as Search

• Can take form of search tree
• Depth-limited search
• Must backtrack if current plan has unmeetable
  preconditions
• Adds appearance of intelligence
• Can be expensive if happens too often

No available action has this as postcondition
Key in current room
Get Key
Door unlocked
Open Door
Door unlocked
Backtrack and try this action
Pick Lock
Door unlocked
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Planning as A Search

• Can use A to choose next action to add to tree
• Cost of path found so far
• Actions assigned costs
• Cost of rest of plan from current state
  sum of actions from
  current state to goal
• Heuristic estimate of rest of path
• Number of unmet preconditions
• Possibly weighted by some expected cost measure

Action
Action
Precondition
Goal
Precondition
Precondition
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Planning as A Search

• Example
• Open Door action has cost 1
• Get Key action has cost 2
• Pick Lock action has cost 5
• Key in current room precondition estimated cost
  1

Try first since estimated path cost 4
Key in current room
Get Key
Door unlocked
Open Door
Door unlocked
Try next since estimated path cost 6
Pick Lock
Door unlocked
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Planning as Tree Building

• Actions with multiple preconditions represented
  as trees
• Actions with preconditions ? Nodes with
  preconditions as branches
• Actions with no preconditions ? Leafs

Have 1000 wood
Build Cannon Factory
Order troops to collect 1000 wood
Have 1000 iron
Build Cannons
Order troops to collect 100 wood
Mine iron
Build Iron Mine
Produce Cannons in Factory
Order troops to mine 1000 iron
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Planning and Scheduling

• Key question How to schedule the actions that
  result from plan
• Some branches may have to be accomplished before
  others
• Some branches can be done in parallel

Build Cannon Factory
Order troops to collect 1000 wood
Can be done in parallel
Build Cannons
preceeds
Order troops to collect 100 wood
Mine iron
Build Iron Mine
Produce Cannons in Factory
preceeds
Order troops to mine 1000 iron
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Hierarchical Planning

• Overall plan created at different levels of
  abstraction
• Low-level subplans created to do higher level
  tasks
• High-level tasks have preconditions and
  postconditions
• High-level tasks have methods for expanding into
  subplans
• Steps of subplan accomplishes postconditions
  given preconditions

Task
Task
Task
Preconditions
Postconditions
Main plan
Action
Task
Action
Preconditions
Postconditions
Subplan
Action
Action
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Hierarchical Planning
General plan to capture a city
Gather weapons
Attack city
Move to city
Have weapons
At city
Gather wood
Build bows and arrows
Move to bridge
Cross bridge
Move to city
At city
Subplan generated for this particular city, level
of technology, etc.
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Dynamic Replanning

• Conditions used to create plan may no longer
  apply in course of executing plan
• Bridge NPC destroyed by player while gathering
  weapons
• Can create a different subplan to accomplish
  specific task without replanning entire plan
• Move to city subplan accomplished by building
  boats

Move to city
Have weapons
At city
Move to river
Gather wood
Build boats
At city
Cross river in boats
Move to city
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Planning for Hierarchical Teams

• Goals of team should direct goals of individuals
• Individuals take actions to support overall
  actions of team

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Planning for Hierarchical Teams

• Individual units meet goal based on individual
  abilities
• No micromanaging from overall system

Open Door
Pick Lock
Look in Room
Search all rooms
Look in Room with X-ray Vision
Overall team goal
Smash Door
Look in Room
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Planning for Hierarchical Teams

• Individual units can balance team goals with
  individual goals to give appearance of
  independent thought
• Each individual/subteam has relevance measure for
  different goals
• Meets goal with highest current relevance

Goal Charge TakeCover Retreat Patrol
Relevance 0.6 0.8 0.3 0.0
Current action
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Planning for Hierarchical Teams

• Individual goal modified by goals of team
• Example Leader orders squad to charge, and not
  retreat

Goal Charge TakeCover Retreat Patrol
Relevance 0.6 0.8 0.3 0.0
Modifier 0.5 0.0 -0.5 0.0
Total 1.1 0.8 -0.2 0.0
Current action
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Planning for Hierarchical Teams

• If individual goals strong, team goals may be
  overridden!
• Example Leader orders squad to patrol
• This unit currently under ambush

Goal Charge TakeCover Retreat Patrol
Relevance 0.4 0.8 0.6 0.0
Modifier 0.0 0.0 0.0 0.5
Total 0.4 0.8 0.6 0.5
Current action