1 / 23

Evolving Social Commitment Based Protocols Using MAPC

This project aims to develop a robust methodology for creating multi-agent systems (MAS) by defining generic scenarios and goal sets. Utilizing evolutionary algorithms, we will instantiate conversation policies that facilitate agent interactions, whether cooperative or competitive. This research will delve into the creation and fulfillment of social commitments to enhance communication protocols. Real-life scenarios will provide context for the behaviors of agents, aiming to optimize interaction mechanisms, automate output filtering, and improve overall usability. The study will contribute to a deeper understanding of agent communications and social constructs.

carr
Download Presentation

Evolving Social Commitment Based Protocols Using MAPC

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Evolving Social Commitment Based Protocols Using MAPC Jason Heard Mount Royal University jheard@mtroyal.ca Rob Kremer University of Calgary kremer@cpsc.ucalgary.ca

  2. Outline • Long Term Goal • Background • Multi Agent System Communication • MAPC • Scenarios and Roles • Behaviour Representation • Actual Output

  3. Long Term Goal Produce a method of creating a multi agent system by specifying a generic situation (scenario) and a set of goals (postconditions). Evolutionary algorithms will instantiate behaviours (conversation policies) for each of the roles in the scenario.

  4. Background • Multi Agent Systems • Communication Protocols • Conversation Policies • Social Commitments

  5. Multi Agent Systems (MAS) • Multiple Agents: • Interacting with Each Other • Cooperative or Competitive • Possibly: • Multiple Programmers • Conflicting Goals • Need to Communicate

  6. query-if disconfirm confirm Communication Protocols • Describe How Communication Proceeds Alice Bob

  7. query-if Traditional Protocols, cont. Alice Bob not-understood ? refuse ?

  8. Conversation Policies • Describe Rules for Conversations • Map Events (Such as Messages) to Creation and Fulfillment of Social Commitments • Much More Flexible

  9. Social Commitments Behaviours Debtor -> Bob Creditor -> Alice Action -> Send reply message to Alice’s query-if message. describe Social Commitments property Externally Verifiable

  10. Conversation Policy Example 1 Message • Example: • A message with the query-if performative commits the receiver to reply. Social Commitment ( query-if :receiver Bob :sender Alice :reply-with “unique-982347” :language English :ontology “Life” :content “closed(store)”) Debtor -> Bob Creditor -> Alice Action -> Send reply message to Alice’s query-if message.

  11. Conversation Policy Example 2 Message • Example: • A message with the reply performative fulfills a commitment to reply. Social Commitment ( disconfirm :receiver Alice :sender Bob :reply-with “unique-857341” :language English :ontology “Life” :content “closed(store)” ) Debtor -> Bob Creditor -> Alice Action -> Send reply message to Alice’s query-if message.

  12. Protocols Described by Policies • Protocols Can Be Described As Set of Policies • Protocol Proceeds As Agent Attempts to Resolve All Commitments • Commitment Creation Indicates a Required Action • Commitment Fulfillment Allows Agents to Delete Commitments • Still Require Extensive Work to Accomplish New Communication Tasks

  13. MAPC • Input • Scenario • Postconditions (Currently Fitness Functions) • Output • Conversations Policies for each Role

  14. Scenarios and Roles • Scenario  Generic Situation • Set of Global Variables, Global Actions and Roles • Scenario Instance  Specific Situation • Role  Generic Agent in Scenario • Set of Variables and Actions • Role Instance  Specific Agent • Within a Specific Scenario Instance

  15. Scenario Table

  16. Behaviour Representation • For Each Role • Start-up Function • To Start the Protocol • Idle Function • To Allow Non-Reactive Behaviours • Conversation Policies • Evolved Protocol • Uses Lisp Syntax

  17. Selector Game

  18. Selector Game Output (Selector) (agent nop ; no start-up function nop ; no do-idle function (policy petition act2 (add-commitment msg.receiver msg.receiver msg.performative act5 (select msg.sender)) ) )

  19. Selector Game Output (Choice) (agent (seq ; start-up function (send-message reply act7 this-agent 17) (if-else (get-data "isAcceptable") (seq (seq (send-message petition act10 this-agent 17) (if-else (get-data "isAcceptable") (seq (send-message petition act2 all-agents 39) (nop) ) (seq (send-message petition act2 all-agents 39) (if-else true nop nop)

  20. Selector Game Output (Choice) ) ) ) nop nop ) ) nop ; no do-idle function nop ; no policies )

  21. Selector Game Observations • Expected Query Protocol • Ended Up with Advertisement Protocol

  22. Future Work • Optimize Genetic System • Allows More Complex Scenarios • Automate Filtering Output • More Useful Output • Improve Goal Description • Improve Ease of Use

  23. Questions?

More Related