These slides are from a presentation of a paper from AISB 2011. They lay out the concept of the Influence Landscape, a technique which uses Automated Planning tools to apply Influence Map-style representations to conceptual as well as spatial representations

1. Influence Landscapes
From Spatial to Conceptual Representations
Luke Dicken and John Levine
Strathclyde AI and Games Research Group

2. Automated Planning
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3. Automated Planning
• Given the following:
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4. Automated Planning
• Given the following:
‣ Specification of possible actions and facts in the world.
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5. Automated Planning
• Given the following:
‣ Specification of possible actions and facts in the world.
‣ Complete specification of initial state
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6. Automated Planning
• Given the following:
‣ Specification of possible actions and facts in the world.
‣ Complete specification of initial state
‣ Partial specification of goals that must be true
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7. Automated Planning
• Given the following:
‣ Specification of possible actions and facts in the world.
‣ Complete specification of initial state
‣ Partial specification of goals that must be true
• Compute a set of actions that will transform from initial
state to goal state.
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8. Problems With Planning
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9. Problems With Planning
• Planning is inherently problematic.
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10. Problems With Planning
• Planning is inherently problematic.
• Core assumptions are lousy
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11. Problems With Planning
• Planning is inherently problematic.
• Core assumptions are lousy
‣ World doesn't change unless the agent changes it.
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12. Problems With Planning
• Planning is inherently problematic.
• Core assumptions are lousy
‣ World doesn't change unless the agent changes it.
‣ Agent is the only active actor in the world.
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13. Problems With Planning
• Planning is inherently problematic.
• Core assumptions are lousy
‣ World doesn't change unless the agent changes it.
‣ Agent is the only active actor in the world.
‣ The outcomes of actions will be as expected.
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14. Problems With Planning
• Planning is inherently problematic.
• Core assumptions are lousy
‣ World doesn't change unless the agent changes it.
‣ Agent is the only active actor in the world.
‣ The outcomes of actions will be as expected.
• These assumptions generally don't hold in the context of
games
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15. Problems With Planning
• Planning is inherently problematic.
• Core assumptions are lousy
‣ World doesn't change unless the agent changes it.
‣ Agent is the only active actor in the world.
‣ The outcomes of actions will be as expected.
• These assumptions generally don't hold in the context of
games
‣ Which is why we like them as testbeds
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16. Searching vs Evaluating
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17. Searching vs Evaluating
• Planning involves tree search from initial state to goal
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18. Searching vs Evaluating
• Planning involves tree search from initial state to goal
• We know that search is expensive.
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19. Searching vs Evaluating
• Planning involves tree search from initial state to goal
• We know that search is expensive.
‣ PSPACE-Complete in the general case for planning.
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20. Searching vs Evaluating
• Planning involves tree search from initial state to goal
• We know that search is expensive.
‣ PSPACE-Complete in the general case for planning.
• Function evaluation trivial by comparison.
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21. Searching vs Evaluating
• Planning involves tree search from initial state to goal
• We know that search is expensive.
‣ PSPACE-Complete in the general case for planning.
• Function evaluation trivial by comparison.
‣ In Game AI, getting to "trivial" is key.
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22. Influence Maps
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23. Influence Maps
• Also known as “Artificial Potential Fields”
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24. Influence Maps
• Also known as “Artificial Potential Fields”
• “Influence” is how attractive or repellant a particular
location is.
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25. Influence Maps
• Also known as “Artificial Potential Fields”
• “Influence” is how attractive or repellant a particular
location is.
• Typically IMs are applied over a discrete (e.g. tiled)
environment.
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26. Influence Maps
• Also known as “Artificial Potential Fields”
• “Influence” is how attractive or repellant a particular
location is.
• Typically IMs are applied over a discrete (e.g. tiled)
environment.
• Bad things repulse the agent, good things attract, and the
agent can hill climb to reach the good while avoiding bad
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27. Influence Maps
• Also known as “Artificial Potential Fields”
• “Influence” is how attractive or repellant a particular
location is.
• Typically IMs are applied over a discrete (e.g. tiled)
environment.
• Bad things repulse the agent, good things attract, and the
agent can hill climb to reach the good while avoiding bad
• Always maps spatial coordinate => perceived value.
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28. Influence Maps
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29. PDDL vs SAS+
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30. PDDL vs SAS+
• PDDL is standard method of describing planning problems
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31. PDDL vs SAS+
• PDDL is standard method of describing planning problems
• Propositional representation, true/false statements, high
dimensional binary representation.
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32. PDDL vs SAS+
• PDDL is standard method of describing planning problems
• Propositional representation, true/false statements, high
dimensional binary representation.
• Alternative formalism SAS+, multivalued variables. Much
lower dimensionality, larger domain per dimension.
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33. The DTG
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34. The DTG
• Each variable has a domain of possible values it can take.
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35. The DTG
• Each variable has a domain of possible values it can take.
• More than this, each value can only transition to certain
other values.
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36. The DTG
• Each variable has a domain of possible values it can take.
• More than this, each value can only transition to certain
other values.
• Gives a concept of adjacency - a graph connecting values
together in sequence.
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37. The DTG
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38. The DTG
• Consider a world in which a package can be moved between
certain places using a truck.
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39. The DTG
• Consider a world in which a package can be moved between
certain places using a truck.
• PDDL represents this as a sequence of possible
propositions :
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40. The DTG
• Consider a world in which a package can be moved between
certain places using a truck.
• PDDL represents this as a sequence of possible
propositions :
‣ (at package1 location1) etc.
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41. The DTG
• Consider a world in which a package can be moved between
certain places using a truck.
• PDDL represents this as a sequence of possible
propositions :
‣ (at package1 location1) etc.
• Only one of these can be true at a time.
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42. The DTG
• Consider a world in which a package can be moved between
certain places using a truck.
• PDDL represents this as a sequence of possible
propositions :
‣ (at package1 location1) etc.
• Only one of these can be true at a time.
• SAS+ translates this into a single variable :
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43. The DTG
• Consider a world in which a package can be moved between
certain places using a truck.
• PDDL represents this as a sequence of possible
propositions :
‣ (at package1 location1) etc.
• Only one of these can be true at a time.
• SAS+ translates this into a single variable :
‣ loc_package1 ∈ {location1, ... locationN, inTruck}
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44. Example DTG
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45. Influence Landscapes
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46. Influence Landscapes
• A 2D Influence Map (particularly discretised) is really only a
graph with adjacent tiles connected.
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47. Influence Landscapes
• A 2D Influence Map (particularly discretised) is really only a
graph with adjacent tiles connected.
• Not all that dissimilar from the DTG concept we can derive
from any planning problem, additionally, it is far more
expressive.
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48. Influence Landscapes
• A 2D Influence Map (particularly discretised) is really only a
graph with adjacent tiles connected.
• Not all that dissimilar from the DTG concept we can derive
from any planning problem, additionally, it is far more
expressive.
• Still able to propagate influence in the same way as for
Influence Maps.
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49. Propagating Influence
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50. Propagating Influence
• In its simplest form, influence is propagated from “hotspots”
across the graph. Effectively a critical path analysis for a single
variable to transition from A to B.
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51. Propagating Influence
• In its simplest form, influence is propagated from “hotspots”
across the graph. Effectively a critical path analysis for a single
variable to transition from A to B.
• Not quite so straightforward
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52. Propagating Influence
• In its simplest form, influence is propagated from “hotspots”
across the graph. Effectively a critical path analysis for a single
variable to transition from A to B.
• Not quite so straightforward
‣ You only see a small piece of the picture with one
variable.
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53. Propagating Influence
• In its simplest form, influence is propagated from “hotspots”
across the graph. Effectively a critical path analysis for a single
variable to transition from A to B.
• Not quite so straightforward
‣ You only see a small piece of the picture with one
variable.
• Cross product of graphs gives a complete picture from
which we can propagate influence for a plan across the
whole space.
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54. Trivial Example
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55. Trivial Example
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56. Trivial Example
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57. Trivial Example
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58. Better Example
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59. Better Example
• From the paper : a battlefield with two tanks.
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60. Better Example
• From the paper : a battlefield with two tanks.
• Goal is to not die whilst killing the enemy.
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61. Better Example
• From the paper : a battlefield with two tanks.
• Goal is to not die whilst killing the enemy.
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62. Better Example
• From the paper : a battlefield with two tanks.
• Goal is to not die whilst killing the enemy.
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63. 15

64. 15

65. 15

66. 16

67. Weaknesses
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68. Weaknesses
• Still largely theoretical - it looks good on paper right now,
and it works in prototype.Very different from a real
implementation.
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69. Weaknesses
• Still largely theoretical - it looks good on paper right now,
and it works in prototype.Very different from a real
implementation.
• Still likely to be naive - a lot of the information a deliberative
planner would extract is being ignored for speed.
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70. Weaknesses
• Still largely theoretical - it looks good on paper right now,
and it works in prototype.Very different from a real
implementation.
• Still likely to be naive - a lot of the information a deliberative
planner would extract is being ignored for speed.
• Key will be proving exactly how efficient this approach is, and
how successful it is for creating intelligent (or believable)
agents.
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71. Main Contribution
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72. Main Contribution
• Biggest contribution is showing that reactive systems can use
the same basic "language" as deliberative systems such as
Planning
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73. Main Contribution
• Biggest contribution is showing that reactive systems can use
the same basic "language" as deliberative systems such as
Planning
• Planning isn't appropriate for Game AI in general, but purely
reactive approaches aren't always great either.
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74. Main Contribution
• Biggest contribution is showing that reactive systems can use
the same basic "language" as deliberative systems such as
Planning
• Planning isn't appropriate for Game AI in general, but purely
reactive approaches aren't always great either.
‣ Both have (dis)advantages.
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75. Main Contribution
• Biggest contribution is showing that reactive systems can use
the same basic "language" as deliberative systems such as
Planning
• Planning isn't appropriate for Game AI in general, but purely
reactive approaches aren't always great either.
‣ Both have (dis)advantages.
‣ Hybrid approach seems reasonable.
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76. I2A
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77. I2A
• Influence Landscapes form one part of a new execution
architecture - the Integrated Influence Architecture (I2A)
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78. I2A
• Influence Landscapes form one part of a new execution
architecture - the Integrated Influence Architecture (I2A)
• Concept is that Planning and Environmental stimuli provide
sources of Influence across ILs
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79. I2A
• Influence Landscapes form one part of a new execution
architecture - the Integrated Influence Architecture (I2A)
• Concept is that Planning and Environmental stimuli provide
sources of Influence across ILs
• Allows informed reaction biased by long term objective
satisfaction.
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80. Shameless Plugs
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81. Shameless Plugs
• Learn more about Automated Planning, the uses for
Influence Landscapes and the Integrated Influence
Architecture at the Paris Game AI Conference 2011
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82. Shameless Plugs
• Learn more about Automated Planning, the uses for
Influence Landscapes and the Integrated Influence
Architecture at the Paris Game AI Conference 2011
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83. Shameless Plugs
• Learn more about Automated Planning, the uses for
Influence Landscapes and the Integrated Influence
Architecture at the Paris Game AI Conference 2011
http://gameaiconf.com
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