Post on 21-Jun-2015
description
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
Controversy!
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Controversy!
• “...STRIPS-style goal oriented action planning has turned out
to be a dead end.”
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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
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)
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
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Alex, “This Year in Game AI”(Jan ’11)
What is Automated Planning?
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What is Automated Planning?
• “Strong” AI
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What is Automated Planning?
• “Strong” AI
• Finds action sequences - Plan
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What is Automated Planning?
• “Strong” AI
• Finds action sequences - Plan
• Over 40 years of research
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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
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How Does it Work?
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How Does it Work?
1. Description of actions possible
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How Does it Work?
1. Description of actions possible
2. Complete description of initial state of the world
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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
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Planning
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S0
Planning
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S0
S1 S2 S3
Planning
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S0
S1
Planning
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S0
S1
S4 S5 S6
Planning
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S0
S1
S6
Planning
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S0
S1
S6
And so on, until goal reached.
GOAP
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GOAP
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GOAP
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GOAP
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GOAP
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GOAP
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Issues with GOAP
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Issues with GOAP
• Issue 1 : Lack of directorial control.
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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.
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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
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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.
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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?
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Complexity Reduction
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Complexity Reduction
• If you can reduce complexity of the problem, it
becomes easier to solve...
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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.
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Landmark Analysis
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Landmark Analysis
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Initial State
Landmark Analysis
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Initial State
Landmark Analysis
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Initial State
Goal Found
Landmark Analysis
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Initial State
Goal Found
Landmark Analysis
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Initial State
Goal Found
Landmark 1
Landmark Analysis
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Initial State
Goal Found
Landmark 1
Landmark Analysis
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Initial State
Goal Found
Landmark 1
Landmark 2
Landmark Analysis
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Initial State
Goal Found
Landmark 1
Landmark 2
Landmark Analysis
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Initial State
Goal Found
Landmark 1
Landmark 2
Abstraction
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A
B
E
C D
F
Abstraction
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A
B
E
C D
F
Abstraction
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Horizon Management
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A
B
E
C D
F
Horizon Management
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Horizon Management
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E
Horizon Management
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B
E
Horizon Management
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B
E
C
Horizon Management
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B
E
C D
Horizon Management
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B
E
C D
F
B
E
C D
F
Heuristics
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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
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Heuristics
• Since GOAP came out, major advances in heuristics
• Most significant :
‣ Relaxed Plan Graph
‣ Landmark Heuristic
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Hierarchical Task Network
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Hierarchical Task Network
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Kill Enemy
Hierarchical Task Network
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Kill Enemy
Approach Enemy Face Enemy Shoot
Enemy
Hierarchical Task Network
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Kill Enemy
Approach Enemy Face Enemy Shoot
Enemy
Leave Cover Navigate
Hierarchical Task Network
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Kill Enemy
Approach Enemy Face Enemy Shoot
Enemy
Leave Cover Navigate ...and so on
Hierarchical Task Network
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Kill Enemy
Approach Enemy Face Enemy Shoot
Enemy
Leave Cover Navigate ...and so on
Until executable actions reached.
Optimality
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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
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Optimality
•Optimality is a big issue for academic vs industry
• Academics
‣ Aim is optimal - shortest, most efficient, least cost
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Optimality
•Optimality is a big issue for academic vs industry
• Academics
‣ Aim is optimal - shortest, most efficient, least cost
• Industry
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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
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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?
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Metrics
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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
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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?”
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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
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Execute Blind
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ExecutePlanStart
PlanEnd
Execute Blind
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Execute Blind
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Execute Blind
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Execute/Replan
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Execute/Replan
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ExecutePlanStart
Execute/Replan
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ExecutePlanStart
??? ?
Execute/Replan
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ExecutePlanStart
??? ?
Replan
Execute/Replan
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ExecutePlanStart
??? ?
Execute
Replan
Execute/Replan
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ExecutePlanStart
??? ?
Execute
Replan
GoalReached
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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Contact
• Email - luke@cis.strath.ac.uk
•Website - http://saig.cis.strath.ac.uk
• Twitter - @LukeD
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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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