Artificial Intelligence in Game Design Goal-Oriented Action Planning.
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Artificial Intelligence in Game Design Goal-Oriented Action Planning
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Transcript of Artificial Intelligence in Game Design Goal-Oriented Action Planning.
Artificial Intelligence in Game Design
Goal-Oriented Action Planning
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
Planning ExampleAction 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!
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
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 Conc
ert
Paint
Nap in
Chair
Paint
Sleep in
Bed
Go to Conc
ertPaint
Go to Conc
ertGo to Conc
ert
Go to Conc
ertNap in
Chair
Go to Conc
ertSleep
in Bed
Nap in
ChairPaint
Nap in
ChairGo to Conc
ert
Nap in
ChairNap in
Chair
Nap in
ChairSleep
in Bed
Sleep in
BedPaint
Sleep in
BedGo to Conc
ert
Sleep in
BedNap in
Chair
Sleep in
BedSleep
in Bed
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
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
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)
Discontentment = 172
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
Planning and Preconditions
• Actions may have preconditions before they can be taken
• Multiple actions required to meet needsActionPreconditions Postconditions
ActionPreconditions which character meets
Postconditions
ActionPreconditions Postconditionsmeet need
meets
Planning and Preconditions
• Example: Microwaving food
Get foodUncooked food in fridge
Have uncooked food
Cook food in microwave
Have uncooked foodHave microwave
Have cooked food
meets
Eat foodHave cooked food
Hunger - 5
meets
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”)
ActionPreconditions Postconditions
STRIPS Planning
• Regressing searching: Working backwards from goals to preconditions
goallist = {unmet needs}while (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 }
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
Selecting Actions
• Different characters Different set of possible actions• Algorithm selects from that character’s actions which
meet prerequisites
Open DoorDoor unlocked Be in roomGet KeyKey in current
room
Smash Door Be in room
Door unlocked
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
Open DoorDoor unlocked
Get KeyKey in current room
Door unlockedNo available action has this as postcondition
Pick Lock Door unlockedBacktrack and try this action
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
ActionPrecondition Goal
Precondition
Precondition
Action
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
Open DoorDoor unlocked
Get KeyKey in current room
Door unlockedTry first since estimated path cost = 4
Pick Lock Door unlockedTry next since estimated path cost = 6
Planning as Tree Building
• Actions with multiple preconditions represented as trees – Actions with preconditions Nodes with preconditions as branches– Actions with no preconditions Leafs
Build Cannons
Build Cannon Factory
Produce Cannons in Factory
Order troops to collect 1000 wood
Mine iron
Have 1000 wood
Have 1000 iron
Build Iron Mine
Order troops to mine 1000 iron
Order troops to collect 100
wood
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 Cannons
Build Cannon Factory
Produce Cannons in Factory
Order troops to collect 1000 wood
Mine ironBuild Iron Mine
Order troops to mine 1000 iron
Order troops to collect 100
wood
preceeds
preceeds
Can be done in parallel
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 TaskTaskPreconditions Postconditions
Main plan
ActionPreconditions PostconditionsTask Action
SubplanAction Action
Hierarchical Planning
Gather weapons Attack city
Move to city
Have weapons At city
General plan to capture a 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.
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 riverGather wood
Build boats At city
Cross river in boats
Move to city
Planning for Hierarchical Teams
• Goals of team should direct goals of individuals• Individuals take actions to support overall actions of team
Planning for Hierarchical Teams
• Individual units meet goal based on individual abilities– No micromanaging from overall system
Search all rooms
Open DoorPick LockLook in Room
Look in Room with X-ray Vision
Smash Door
Look in Room
Overall team goal
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
Planning for Hierarchical Teams
• Individual goal modified by goals of team– Example: Leader orders squad to charge, and not retreat
Current action
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
Planning for Hierarchical Teams
• If individual goals strong, team goals may be overridden!– Example: Leader orders squad to patrol– This unit currently under ambush
Current action
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
