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Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
Influence Landscapes - From Spatial to Conceptual Representations
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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 …

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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  • Transcript

    • 1. Influence LandscapesFrom Spatial to Conceptual Representations Luke Dicken and John Levine Strathclyde AI and Games Research Group
    • 2. Automated Planning2
    • 3. Automated Planning • Given the following:2
    • 4. Automated Planning • Given the following: ‣ Specification of possible actions and facts in the world.2
    • 5. Automated Planning • Given the following: ‣ Specification of possible actions and facts in the world. ‣ Complete specification of initial state2
    • 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. 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. Problems With Planning3
    • 9. Problems With Planning • Planning is inherently problematic.3
    • 10. Problems With Planning • Planning is inherently problematic. • Core assumptions are lousy3
    • 11. Problems With Planning • Planning is inherently problematic. • Core assumptions are lousy ‣ World doesnt change unless the agent changes it.3
    • 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. 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. 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. 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. Searching vs Evaluating4
    • 17. Searching vs Evaluating • Planning involves tree search from initial state to goal4
    • 18. Searching vs Evaluating • Planning involves tree search from initial state to goal • We know that search is expensive.4
    • 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. 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. 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. Influence Maps5
    • 23. Influence Maps • Also known as “Artificial Potential Fields”5
    • 24. Influence Maps • Also known as “Artificial Potential Fields” • “Influence” is how attractive or repellant a particular location is.5
    • 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. 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. 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. Influence Maps6
    • 29. PDDL vs SAS+7
    • 30. PDDL vs SAS+ • PDDL is standard method of describing planning problems7
    • 31. PDDL vs SAS+ • PDDL is standard method of describing planning problems • Propositional representation, true/false statements, high dimensional binary representation.7
    • 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. The DTG8
    • 34. The DTG • Each variable has a domain of possible values it can take.8
    • 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. 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. The DTG9
    • 38. The DTG • Consider a world in which a package can be moved between certain places using a truck.9
    • 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. 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. 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. 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. 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. Example DTG10
    • 45. Influence Landscapes11
    • 46. Influence Landscapes • A 2D Influence Map (particularly discretised) is really only a graph with adjacent tiles connected.11
    • 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. 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. Propagating Influence12
    • 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. 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. 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. 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. Trivial Example13
    • 55. Trivial Example13
    • 56. Trivial Example13
    • 57. Trivial Example13
    • 58. Better Example14
    • 59. Better Example • From the paper : a battlefield with two tanks.14
    • 60. Better Example • From the paper : a battlefield with two tanks. • Goal is to not die whilst killing the enemy.14
    • 61. Better Example • From the paper : a battlefield with two tanks. • Goal is to not die whilst killing the enemy.14
    • 62. Better Example • From the paper : a battlefield with two tanks. • Goal is to not die whilst killing the enemy.14
    • 63. 15
    • 64. 15
    • 65. 15
    • 66. 16
    • 67. Weaknesses17
    • 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. 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. 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. Main Contribution18
    • 72. Main Contribution • Biggest contribution is showing that reactive systems can use the same basic "language" as deliberative systems such as Planning18
    • 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. 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. 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. I2A19
    • 77. I2A • Influence Landscapes form one part of a new execution architecture - the Integrated Influence Architecture (I2A)19
    • 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. 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. Shameless Plugs20
    • 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. 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. 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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