Influence LandscapesFrom Spatial to Conceptual Representations              Luke Dicken and John Levine       Strathclyde A...
Automated Planning2
Automated Planning    • Given the following:2
Automated Planning    • Given the following:       ‣ Specification of possible actions and facts in the world.2
Automated Planning    • Given the following:       ‣ Specification of possible actions and facts in the world.       ‣ Comp...
Automated Planning    • Given the following:       ‣ Specification of possible actions and facts in the world.       ‣ Comp...
Automated Planning    • Given the following:       ‣ Specification of possible actions and facts in the world.       ‣ Comp...
Problems With Planning3
Problems With Planning    • Planning is inherently problematic.3
Problems With Planning    • Planning is inherently problematic.    • Core assumptions are lousy3
Problems With Planning    • Planning is inherently problematic.    • Core assumptions are lousy       ‣ World doesnt chang...
Problems With Planning    • Planning is inherently problematic.    • Core assumptions are lousy       ‣ World doesnt chang...
Problems With Planning    • Planning is inherently problematic.    • Core assumptions are lousy       ‣ World doesnt chang...
Problems With Planning    • Planning is inherently problematic.    • Core assumptions are lousy       ‣ World doesnt chang...
Problems With Planning    • Planning is inherently problematic.    • Core assumptions are lousy       ‣ World doesnt chang...
Searching vs Evaluating4
Searching vs Evaluating    • Planning involves tree search from initial state to goal4
Searching vs Evaluating    • Planning involves tree search from initial state to goal    • We know that search is expensiv...
Searching vs Evaluating    • Planning involves tree search from initial state to goal    • We know that search is expensiv...
Searching vs Evaluating    • Planning involves tree search from initial state to goal    • We know that search is expensiv...
Searching vs Evaluating    • Planning involves tree search from initial state to goal    • We know that search is expensiv...
Influence Maps5
Influence Maps    • Also known as “Artificial Potential Fields”5
Influence Maps    • Also known as “Artificial Potential Fields”    • “Influence” is how attractive or repellant a particular ...
Influence Maps    • Also known as “Artificial Potential Fields”    • “Influence” is how attractive or repellant a particular ...
Influence Maps    • Also known as “Artificial Potential Fields”    • “Influence” is how attractive or repellant a particular ...
Influence Maps    • Also known as “Artificial Potential Fields”    • “Influence” is how attractive or repellant a particular ...
Influence Maps6
PDDL vs SAS+7
PDDL vs SAS+    • PDDL is standard method of describing planning problems7
PDDL vs SAS+    • PDDL is standard method of describing planning problems    • Propositional representation, true/false st...
PDDL vs SAS+    • PDDL is standard method of describing planning problems    • Propositional representation, true/false st...
The DTG8
The DTG    • Each variable has a domain of possible values it can take.8
The DTG    • Each variable has a domain of possible values it can take.    • More than this, each value can only transitio...
The DTG    • Each variable has a domain of possible values it can take.    • More than this, each value can only transitio...
The DTG9
The DTG    • Consider a world in which a package can be moved between     certain places using a truck.9
The DTG    • Consider a world in which a package can be moved between     certain places using a truck.    • PDDL represen...
The DTG    • Consider a world in which a package can be moved between     certain places using a truck.    • PDDL represen...
The DTG    • Consider a world in which a package can be moved between     certain places using a truck.    • PDDL represen...
The DTG    • Consider a world in which a package can be moved between     certain places using a truck.    • PDDL represen...
The DTG    • Consider a world in which a package can be moved between     certain places using a truck.    • PDDL represen...
Example DTG10
Influence Landscapes11
Influence Landscapes     • A 2D Influence Map (particularly discretised) is really only a      graph with adjacent tiles con...
Influence Landscapes     • A 2D Influence Map (particularly discretised) is really only a      graph with adjacent tiles con...
Influence Landscapes     • A 2D Influence Map (particularly discretised) is really only a      graph with adjacent tiles con...
Propagating Influence12
Propagating Influence     • In its simplest form, influence is propagated from “hotspots”      across the graph. Effectively...
Propagating Influence     • In its simplest form, influence is propagated from “hotspots”      across the graph. Effectively...
Propagating Influence     • In its simplest form, influence is propagated from “hotspots”      across the graph. Effectively...
Propagating Influence     • In its simplest form, influence is propagated from “hotspots”      across the graph. Effectively...
Trivial Example13
Trivial Example13
Trivial Example13
Trivial Example13
Better Example14
Better Example     • From the paper : a battlefield with two tanks.14
Better Example     • From the paper : a battlefield with two tanks.     • Goal is to not die whilst killing the enemy.14
Better Example     • From the paper : a battlefield with two tanks.     • Goal is to not die whilst killing the enemy.14
Better Example     • From the paper : a battlefield with two tanks.     • Goal is to not die whilst killing the enemy.14
15
15
15
16
Weaknesses17
Weaknesses     • Still largely theoretical - it looks good on paper right now,      and it works in prototype.Very differe...
Weaknesses     • Still largely theoretical - it looks good on paper right now,      and it works in prototype.Very differe...
Weaknesses     • Still largely theoretical - it looks good on paper right now,      and it works in prototype.Very differe...
Main Contribution18
Main Contribution     • Biggest contribution is showing that reactive systems can use      the same basic "language" as de...
Main Contribution     • Biggest contribution is showing that reactive systems can use      the same basic "language" as de...
Main Contribution     • Biggest contribution is showing that reactive systems can use      the same basic "language" as de...
Main Contribution     • Biggest contribution is showing that reactive systems can use      the same basic "language" as de...
I2A19
I2A     • Influence Landscapes form one part of a new execution      architecture - the Integrated Influence Architecture (I...
I2A     • Influence Landscapes form one part of a new execution      architecture - the Integrated Influence Architecture (I...
I2A     • Influence Landscapes form one part of a new execution      architecture - the Integrated Influence Architecture (I...
Shameless Plugs20
Shameless Plugs     • Learn more about Automated Planning, the uses for      Influence Landscapes and the Integrated Influen...
Shameless Plugs     • Learn more about Automated Planning, the uses for      Influence Landscapes and the Integrated Influen...
Shameless Plugs     • Learn more about Automated Planning, the uses for      Influence Landscapes and the Integrated Influen...
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Influence Landscapes - From Spatial to Conceptual Representations

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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

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  • Influence Landscapes - From Spatial to Conceptual Representations

    1. 1. Influence LandscapesFrom Spatial to Conceptual Representations Luke Dicken and John Levine Strathclyde AI and Games Research Group
    2. 2. Automated Planning2
    3. 3. Automated Planning • Given the following:2
    4. 4. Automated Planning • Given the following: ‣ Specification of possible actions and facts in the world.2
    5. 5. Automated Planning • Given the following: ‣ Specification of possible actions and facts in the world. ‣ Complete specification of initial state2
    6. 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 true2
    7. 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.2
    8. 8. Problems With Planning3
    9. 9. Problems With Planning • Planning is inherently problematic.3
    10. 10. Problems With Planning • Planning is inherently problematic. • Core assumptions are lousy3
    11. 11. Problems With Planning • Planning is inherently problematic. • Core assumptions are lousy ‣ World doesnt change unless the agent changes it.3
    12. 12. Problems With Planning • Planning is inherently problematic. • Core assumptions are lousy ‣ World doesnt change unless the agent changes it. ‣ Agent is the only active actor in the world.3
    13. 13. Problems With Planning • Planning is inherently problematic. • Core assumptions are lousy ‣ World doesnt change unless the agent changes it. ‣ Agent is the only active actor in the world. ‣ The outcomes of actions will be as expected.3
    14. 14. Problems With Planning • Planning is inherently problematic. • Core assumptions are lousy ‣ World doesnt 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 dont hold in the context of games3
    15. 15. Problems With Planning • Planning is inherently problematic. • Core assumptions are lousy ‣ World doesnt 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 dont hold in the context of games ‣ Which is why we like them as testbeds3
    16. 16. Searching vs Evaluating4
    17. 17. Searching vs Evaluating • Planning involves tree search from initial state to goal4
    18. 18. Searching vs Evaluating • Planning involves tree search from initial state to goal • We know that search is expensive.4
    19. 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.4
    20. 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.4
    21. 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.4
    22. 22. Influence Maps5
    23. 23. Influence Maps • Also known as “Artificial Potential Fields”5
    24. 24. Influence Maps • Also known as “Artificial Potential Fields” • “Influence” is how attractive or repellant a particular location is.5
    25. 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.5
    26. 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 bad5
    27. 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.5
    28. 28. Influence Maps6
    29. 29. PDDL vs SAS+7
    30. 30. PDDL vs SAS+ • PDDL is standard method of describing planning problems7
    31. 31. PDDL vs SAS+ • PDDL is standard method of describing planning problems • Propositional representation, true/false statements, high dimensional binary representation.7
    32. 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.7
    33. 33. The DTG8
    34. 34. The DTG • Each variable has a domain of possible values it can take.8
    35. 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.8
    36. 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.8
    37. 37. The DTG9
    38. 38. The DTG • Consider a world in which a package can be moved between certain places using a truck.9
    39. 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 :9
    40. 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.9
    41. 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.9
    42. 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 :9
    43. 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}9
    44. 44. Example DTG10
    45. 45. Influence Landscapes11
    46. 46. Influence Landscapes • A 2D Influence Map (particularly discretised) is really only a graph with adjacent tiles connected.11
    47. 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.11
    48. 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.11
    49. 49. Propagating Influence12
    50. 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.12
    51. 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 straightforward12
    52. 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.12
    53. 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.12
    54. 54. Trivial Example13
    55. 55. Trivial Example13
    56. 56. Trivial Example13
    57. 57. Trivial Example13
    58. 58. Better Example14
    59. 59. Better Example • From the paper : a battlefield with two tanks.14
    60. 60. Better Example • From the paper : a battlefield with two tanks. • Goal is to not die whilst killing the enemy.14
    61. 61. Better Example • From the paper : a battlefield with two tanks. • Goal is to not die whilst killing the enemy.14
    62. 62. Better Example • From the paper : a battlefield with two tanks. • Goal is to not die whilst killing the enemy.14
    63. 63. 15
    64. 64. 15
    65. 65. 15
    66. 66. 16
    67. 67. Weaknesses17
    68. 68. Weaknesses • Still largely theoretical - it looks good on paper right now, and it works in prototype.Very different from a real implementation.17
    69. 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.17
    70. 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.17
    71. 71. Main Contribution18
    72. 72. Main Contribution • Biggest contribution is showing that reactive systems can use the same basic "language" as deliberative systems such as Planning18
    73. 73. Main Contribution • Biggest contribution is showing that reactive systems can use the same basic "language" as deliberative systems such as Planning • Planning isnt appropriate for Game AI in general, but purely reactive approaches arent always great either.18
    74. 74. Main Contribution • Biggest contribution is showing that reactive systems can use the same basic "language" as deliberative systems such as Planning • Planning isnt appropriate for Game AI in general, but purely reactive approaches arent always great either. ‣ Both have (dis)advantages.18
    75. 75. Main Contribution • Biggest contribution is showing that reactive systems can use the same basic "language" as deliberative systems such as Planning • Planning isnt appropriate for Game AI in general, but purely reactive approaches arent always great either. ‣ Both have (dis)advantages. ‣ Hybrid approach seems reasonable.18
    76. 76. I2A19
    77. 77. I2A • Influence Landscapes form one part of a new execution architecture - the Integrated Influence Architecture (I2A)19
    78. 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 ILs19
    79. 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.19
    80. 80. Shameless Plugs20
    81. 81. Shameless Plugs • Learn more about Automated Planning, the uses for Influence Landscapes and the Integrated Influence Architecture at the Paris Game AI Conference 201120
    82. 82. Shameless Plugs • Learn more about Automated Planning, the uses for Influence Landscapes and the Integrated Influence Architecture at the Paris Game AI Conference 201120
    83. 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.com20

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