Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
The Next Generation of Game Planners
1. The Next Generation of
Game Planners
The "Everything You (N)Ever Wanted to Know" Tour
Luke Dicken
Strathclyde AI and Games Research Group
University of Strathclyde
3. Controversy!
• “...STRIPS-style goal oriented action planning has turned out
to be a dead end.”
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4. 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
5. 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...”
Alex, “This Year in Game AI”
(Jan ’11)
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6. 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...”
Alex, “This Year in Game AI”
(Jan ’11)
• 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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9. What is Automated
Planning?
• “Strong” AI
• Finds action sequences - Plan
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10. What is Automated
Planning?
• “Strong” AI
• Finds action sequences - Plan
• Over 40 years of research
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11. 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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13. How Does it Work?
1. Description of actions possible
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14. How Does it Work?
1. Description of actions possible
2. Complete description of initial state of the world
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15. 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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30. 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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31. 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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32. 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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34. 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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35. 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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37. Complexity Reduction
• If you can reduce complexity of the problem, it
becomes easier to solve...
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38. 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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71. Optimality
• Optimality is a big issue for academic vs industry
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72. Optimality
• Optimality is a big issue for academic vs industry
• Academics
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73. Optimality
• Optimality is a big issue for academic vs industry
• Academics
‣ Aim is optimal - shortest, most efficient, least cost
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74. Optimality
• Optimality is a big issue for academic vs industry
• Academics
‣ Aim is optimal - shortest, most efficient, least cost
• Industry
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75. 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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76. 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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83. Plan Execution
• Planning is not the same as doing something
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84. Plan Execution
• Planning is not the same as doing something
• Big question is: “what happens next?”
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85. 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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108. Summary
• GOAP is not the extent of planning
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109. 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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110. 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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111. 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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113. 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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