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The Alive Project Bruce M. Blumberg, MIT

The Alive Project Bruce M. Blumberg, MIT. Sidney D’Mello Control of Autonomous Agents. Overview:. Agent: Autonomous Dog (Silas T. Dog) Environment : 3D artificial world and people. Sensors : Synthetic vision. Goals : Satisfy internal motivations. Obey the user. Actions :

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The Alive Project Bruce M. Blumberg, MIT

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  1. The Alive ProjectBruce M. Blumberg, MIT Sidney D’Mello Control of Autonomous Agents

  2. Overview: • Agent: Autonomous Dog (Silas T. Dog) • Environment: 3D artificial world and people. • Sensors: Synthetic vision. • Goals: Satisfy internal motivations. Obey the user. • Actions: Respond to gestures of the user. Other basic actions (satisfy drives, etc..)

  3. Goals: • Autonomy: Pursues its own agenda. Senses the environment. Acts on the environment. • Directability: Action selection may be independent of the behavior system. Suggest how the action should be performed.

  4. Multiple Levels of Control • Motivational Level: Change the dog’s current motivation. • Task Level: Supply a high level directive. • Direct Level: Direct control of the motor system.

  5. Behavior Model Design goals: • Optimal action selection model. • Model the effect of external stimuli and internal motivation. • Multiple behaviors may suggest actions and execution preferences. • Maintain a winner takes all architecture. • Support for motivational and task level directions at run time.

  6. Behavior Architecture • Three central parts: • Behavior • Motor Skills • Geometry • Two layers of abstraction: • Controller • Degree’s of Freedom

  7. Geometry • Provides the shapes and transforms. • Issued by the motor system. • Manipulated over time for animation.

  8. Motor System • Components: Controller, Motor Skills, Degree of Freedom • Commands: left, sit, forward, back,…. • Importance: Translates behaviors into commands. Abstraction barrier (forward --- walking, swimming). Provides a generic set of commands (eat, sleep). Provides resource management.

  9. Degrees of Freedom • “Knobs” to modify the underlying geometry. • Used to wag the tail, move a joint,… • Importance: Provides a locking mechanism. Provides an abstraction mechanism.

  10. Motor Skills • Examples: lower head, turning, walking,…. • Can be turned on or off. • Coordinates DOF’s to perform smooth motion. • Spring loaded. • Reduces bookkeeping.

  11. Controller • Abstraction barrier between behavior system and motor skills. • Maps commands to turn on or turn of motor skills. • Example: forward command --- walk, move motor skill. • Command Types: Primary command Secondary command. Meta command.

  12. Hierarchical network of goal-directed entities. Examples: move-to-tree search-for-food knock-on-door Behavior System

  13. Behavior Diagram

  14. Releasing Mechanisms • Filters sensory input. • Identify relevant objects and/or events. • Typically output a continuous value. • May be shared among multiple behavior. • Example: weak stimulus/strong motivation strong stimulus/weak motivation

  15. Internal Variables • Used to model the internal state. • Expressed as continuous values. • Modified by behaviors. • May be shared by multiple behaviors.

  16. Behavior Groups • Groups of mutually inhibiting behaviors. • Loose hierarchicalstructure: • Upper Level: Driven by motivation (engage-in-feeding). • Lower Level: Driven by sensory input (pounce, chew).

  17. Inhibition and Level of Interest • Avalanche Effect. • Insures that only one behavior will be non-zero. • Controls the persistence of behaviors. • Manages the level of interest of behaviors. • Provides a mechanism for the winner takes all arbitration.

  18. Issuing Commands: • Winning behavior and its children: Primary commands. • Losing behaviors: Secondary and Meta commands. • Winning behavior can overrule a secondary command. • Winning behavior can ignore a meta command.

  19. Integration of Directability • Motivational control: Access to internal variables. • Behavior control: Access to Releasing Mechanism. Possible to activate behaviors. • Sensory control: imaginary sensory input. • Motor Control: Shut of the behavior system. Issue secondary or meta commands.

  20. Implementation Specifics • Responds to a dozen user gestures. • 40 Behaviors. • 11 Behavior Groups. • 40 Releasing Mechanisms. • 8 Internal Variables. • 70 Motor Commands.

  21. Conclusions • Action selection algorithm suits needs. • Autonomy and directability are not mutually exclusive. • Blend of autonomy and direction provide more control. • Control architecture can be easily incorporated onto different agents.

  22. References: • Multi-Level Direction of Autonomous Creatures for Real-Time Virtual Environments Bruce M. Blumberg and Tinsley A. Galyean • Expressive Autonomous Cinematography for Interactive Virtual Environments Bill Tomlinson, Bruce Blumberg, Delphine Nain

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