the next generation of game planners
DESCRIPTION
Tutorial given during the 2011 Paris Game AI Conference on Automated Planning applied to GamesTRANSCRIPT
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The Next Generation of Game Planners
The "Everything You (N)Ever Wanted to Know" Tour
Luke DickenStrathclyde AI and Games Research Group
University of Strathclyde
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Controversy!
2
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Controversy!
• “...STRIPS-style goal oriented action planning has turned out
to be a dead end.”
2
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Controversy!
• “...STRIPS-style goal oriented action planning has turned out
to be a dead end.”
• “Academia has long discarded such planners in favor of
hierarchical ones...”
2
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Controversy!
• “...STRIPS-style goal oriented action planning has turned out
to be a dead end.”
• “Academia has long discarded such planners in favor of
hierarchical ones...”
2
Alex, “This Year in Game AI”(Jan ’11)
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Controversy!
• “...STRIPS-style goal oriented action planning has turned out
to be a dead end.”
• “Academia has long discarded such planners in favor of
hierarchical ones...”
• This session will drill into what Automated Planning is and
why (some) parts of it are still relevant for Game AI
2
Alex, “This Year in Game AI”(Jan ’11)
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What is Automated Planning?
3
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What is Automated Planning?
• “Strong” AI
3
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What is Automated Planning?
• “Strong” AI
• Finds action sequences - Plan
3
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What is Automated Planning?
• “Strong” AI
• Finds action sequences - Plan
• Over 40 years of research
3
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What is Automated Planning?
• “Strong” AI
• Finds action sequences - Plan
• Over 40 years of research
• Planning Domain Description Language (PDDL) - 1998
3
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How Does it Work?
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How Does it Work?
1. Description of actions possible
4
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How Does it Work?
1. Description of actions possible
2. Complete description of initial state of the world
4
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How Does it Work?
1. Description of actions possible
2. Complete description of initial state of the world
3. Definition of goals that need to be achieved
4
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Planning
5
S0
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Planning
5
S0
S1 S2 S3
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Planning
5
S0
S1
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Planning
5
S0
S1
S4 S5 S6
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Planning
5
S0
S1
S6
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Planning
5
S0
S1
S6
And so on, until goal reached.
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GOAP
6
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GOAP
6
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GOAP
6
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GOAP
6
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GOAP
6
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GOAP
6
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Issues with GOAP
7
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Issues with GOAP
• Issue 1 : Lack of directorial control.
7
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Issues with GOAP
• Issue 1 : Lack of directorial control.
‣When NPCs get smart enough to realise standing next
to exploding barrels is hazardous, cinematic experience is
diminished.
7
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Issues with GOAP
• Issue 1 : Lack of directorial control.
‣When NPCs get smart enough to realise standing next
to exploding barrels is hazardous, cinematic experience is
diminished.
• Issue 2 : Computational Complexity
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Issues with GOAP
• Issue 1 : Lack of directorial control.
‣When NPCs get smart enough to realise standing next
to exploding barrels is hazardous, cinematic experience is
diminished.
• Issue 2 : Computational Complexity
‣ GOAP is derived directly from STRIPS. NP-Hard search
problems in the general case.
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Issues with GOAP
8
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Issues with GOAP
• Issue 1 - either a “strong” AI approach is suitable to your
design or it isn’t. Places it often will be include sandbox
environments and companion AI.
8
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Issues with GOAP
• Issue 1 - either a “strong” AI approach is suitable to your
design or it isn’t. Places it often will be include sandbox
environments and companion AI.
• Issue 2 is what will be the focus of the rest of the session -
how have planning systems improved since STRIPS/GOAP?
8
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Complexity Reduction
9
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Complexity Reduction
• If you can reduce complexity of the problem, it
becomes easier to solve...
9
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Complexity Reduction
• If you can reduce complexity of the problem, it
becomes easier to solve...
• Either less depth to the problem or less breadth.
9
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Landmark Analysis
10
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Landmark Analysis
10
Initial State
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Landmark Analysis
10
Initial State
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Landmark Analysis
10
Initial State
Goal Found
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Landmark Analysis
10
Initial State
Goal Found
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Landmark Analysis
10
Initial State
Goal Found
Landmark 1
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Landmark Analysis
10
Initial State
Goal Found
Landmark 1
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Landmark Analysis
10
Initial State
Goal Found
Landmark 1
Landmark 2
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Landmark Analysis
10
Initial State
Goal Found
Landmark 1
Landmark 2
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Landmark Analysis
10
Initial State
Goal Found
Landmark 1
Landmark 2
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Abstraction
11
A
B
E
C D
F
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Abstraction
11
A
B
E
C D
F
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Abstraction
11
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Horizon Management
12
A
B
E
C D
F
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Horizon Management
12
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Horizon Management
12
E
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Horizon Management
12
B
E
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Horizon Management
12
B
E
C
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Horizon Management
12
B
E
C D
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Horizon Management
12
B
E
C D
F
B
E
C D
F
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Heuristics
13
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Heuristics
• Since GOAP came out, major advances in heuristics
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Heuristics
• Since GOAP came out, major advances in heuristics
• Most significant :
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Heuristics
• Since GOAP came out, major advances in heuristics
• Most significant :
‣ Relaxed Plan Graph
13
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Heuristics
• Since GOAP came out, major advances in heuristics
• Most significant :
‣ Relaxed Plan Graph
‣ Landmark Heuristic
13
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Hierarchical Task Network
14
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Hierarchical Task Network
14
Kill Enemy
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Hierarchical Task Network
14
Kill Enemy
Approach Enemy Face Enemy Shoot
Enemy
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Hierarchical Task Network
14
Kill Enemy
Approach Enemy Face Enemy Shoot
Enemy
Leave Cover Navigate
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Hierarchical Task Network
14
Kill Enemy
Approach Enemy Face Enemy Shoot
Enemy
Leave Cover Navigate ...and so on
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Hierarchical Task Network
14
Kill Enemy
Approach Enemy Face Enemy Shoot
Enemy
Leave Cover Navigate ...and so on
Until executable actions reached.
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Optimality
15
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Optimality
•Optimality is a big issue for academic vs industry
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Optimality
•Optimality is a big issue for academic vs industry
• Academics
15
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Optimality
•Optimality is a big issue for academic vs industry
• Academics
‣ Aim is optimal - shortest, most efficient, least cost
15
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Optimality
•Optimality is a big issue for academic vs industry
• Academics
‣ Aim is optimal - shortest, most efficient, least cost
• Industry
15
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Optimality
•Optimality is a big issue for academic vs industry
• Academics
‣ Aim is optimal - shortest, most efficient, least cost
• Industry
‣ Aim is entertaining - believable, beatable, pseudo-smart
15
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Optimality
•Optimality is a big issue for academic vs industry
• Academics
‣ Aim is optimal - shortest, most efficient, least cost
• Industry
‣ Aim is entertaining - believable, beatable, pseudo-smart
• How can we bridge this disconnect?
15
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Metrics
16
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Metrics
• Plan Metrics allow you to define optimal on your
terms.
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Metrics
• Plan Metrics allow you to define optimal on your
terms.
• Not a total solution, adds extra compute time.
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But what happens after planning?
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Plan Execution
18
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Plan Execution
• Planning is not the same as doing something
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Plan Execution
• Planning is not the same as doing something
• Big question is: “what happens next?”
18
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Plan Execution
• Planning is not the same as doing something
• Big question is: “what happens next?”
‣ Especially considering that the traditional assumptions of
planning make doing things with plans “challenging”!
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Execute Blind
19
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Execute Blind
19
ExecutePlanStart
PlanEnd
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Execute Blind
19
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Execute Blind
19
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Execute Blind
19
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Execute/Replan
20
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Execute/Replan
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ExecutePlanStart
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Execute/Replan
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ExecutePlanStart
??? ?
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Execute/Replan
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ExecutePlanStart
??? ?
Replan
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Execute/Replan
20
ExecutePlanStart
??? ?
Execute
Replan
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Execute/Replan
20
ExecutePlanStart
??? ?
Execute
Replan
GoalReached
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Execution Monitoring
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Execution Monitoring
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Integrated Influence
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Integrated Influence
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Integrated Influence
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Integrated Influence
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Integrated Influence
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Integrated Influence
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Integrated Influence
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Integrated Influence
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Summary
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Summary
• GOAP is not the extent of planning
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Summary
• GOAP is not the extent of planning
•We’ve come a long way in the 40 years since
STRIPS was invented.
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Summary
• GOAP is not the extent of planning
•We’ve come a long way in the 40 years since
STRIPS was invented.
• Planning is still mostly focused on the “big”
problems.
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Summary
• GOAP is not the extent of planning
•We’ve come a long way in the 40 years since
STRIPS was invented.
• Planning is still mostly focused on the “big”
problems.
• There is work in planning of relevance.
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References• Landmarks
‣ “On the Extraction, Ordering and Usage of Landmarks in Planning” Porteous et al, ECP ’01
• Abstraction
‣ “Applying Clustering Techniques to Reduce Complexity in Automated Planning Domains” Dicken &
Levine, IDEAL ’10
• Relaxed Plan Graph
‣ “The FF Planning System: Fast plan Generation Through Heuristic Search” Hoffman, JAIR Vol. 14
• Landmark Heuristic
‣ “The LAMA Planner Using Landmark Counting in Heuristic Search” Richter & Westphal, IPC ’08
• HTNs
‣ “SHOP2 : An HTN Planning System” Nau et al, JAIR Vol. 20
• Execute/Replan
‣ “FF-Replan: A baseline for probabilistic planning” Yoon et al, ICAPS ’07
• Execution Monitoring
‣ “T-REX: A Deliberative System for AUV Control” McGann et al, PPERWS ’07
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