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FORKS: Automated Loading Dock Multi Agent

FORKS: Automated Loading Dock Multi Agent. By Reddy. Contents. Environment Sensing Capabilities Actions Goals Action Selection Architecture. Environment. Loading-dock domain. Sensing Capabilities. Agents can sense only a part of the environment.

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FORKS: Automated Loading Dock Multi Agent

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  1. FORKS: Automated Loading Dock Multi Agent By Reddy

  2. Contents • Environment • Sensing Capabilities • Actions • Goals • Action Selection Architecture

  3. Environment Loading-dock domain

  4. Sensing Capabilities • Agents can sense only a part of the environment. • Simplifies geometrical trajectory planning of the agents. • Represents a real world scenario of loading-dock problem.

  5. Forklifts Primitive Actions • Moving from one location to another. • Turning around. • Getting goods from shelves. • Storing goods in shelves. • Communicating with other agents.

  6. Goals • Load and unload incoming trucks.

  7. Action Selection Architecture • Three Control layers • Flow of control • Upward activation request. • Downward commitment posting.

  8. Behavior-Base Layer (BBL) • Situation-action rules. • Actions that are appropriate for time-critical situations. • A procedure can be called by either BBL or LPL layer. • Processing time of the procedures should be fast.

  9. Local Planning Layer(LPL) • Goal directed behavior. • Planner generates plans for the current goals. • Scheduler determines the sequence of actions. • Hierarchical and uses a plan library.

  10. Cooperative Planning Layer (CPL) • Plan and cooperate with other agents. • Each agent can choose its own strategy for negotiation. • Produces a list of <role-name, message> pairs.

  11. References • Computational Logic and Multi-Agent Systems: a Roadmap by Fariba Sadri, Francesca Toni. • An Agent Specification Language by Michael Rosinus, Jorg P. Muller, Markus pischel. • An architecture for dynamically interacting agents by J. P. Muller and M. Pischel. • Software Agents: An Overview by Hyacinth S. Nwana.

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