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An autonomous agent for negotiation with multiple communication channels using parametrized deep Q-network.

Siqi Chen1, Ran Su1

  • 1College of Intelligence and Computing, Tianjin University, Tianjin 300072, China.

Mathematical Biosciences and Engineering : MBE
|July 8, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces MCAN, a negotiation agent that uses multiple communication channels, including language, to improve automated negotiation. MCAN significantly outperforms other agents and human players in achieving better negotiation outcomes.

Keywords:
cooperative gamesdeep learninghuman-agent interactionmulti-agent systemsreinforcement learning

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Area of Science:

  • Artificial Intelligence
  • Computational Economics
  • Human-Computer Interaction

Background:

  • Current agent-based negotiation primarily uses offer exchange, limiting communication bandwidth.
  • Real-world negotiations involve linguistic channels for expressing intentions and discussing plans.
  • Traditional negotiation agents have restricted information bandwidth tied to action spaces.

Purpose of the Study:

  • To develop a negotiation agent capable of utilizing multiple communication channels, including linguistic interactions.
  • To model multi-channel negotiation as a Markov decision problem with a hybrid action space.
  • To enhance automated negotiation by integrating communication skills with bidding strategies.

Main Methods:

  • Developed MCAN (multiple channel automated negotiation) agent.
  • Modeled negotiation as a Markov decision problem with a hybrid action space.
  • Employed a novel deep reinforcement learning technique using parametrized deep Q-networks (P-DQNs) for hybrid discrete-continuous action spaces.

Main Results:

  • MCAN agent demonstrated superior performance compared to other agents and human players in terms of averaged utility.
  • Experimental results confirmed the effectiveness of P-DQNs in enhancing MCAN's negotiation strategy.
  • A user study indicated high human perception and acceptance of the MCAN agent's interactions.

Conclusions:

  • MCAN effectively integrates linguistic communication and bidding strategies for advanced automated negotiation.
  • The use of P-DQNs enables efficient strategies in hybrid action spaces for multi-channel negotiation.
  • This research advances agent-based negotiation by incorporating richer communication modalities.