A Composite Self-organisation Mechanism in an Agent Network
Reference: Ye, D., Zhang, M., Bai, Q. (2011). WISE 2011, LNCS 6997, Springer. Source file: WISE2011-2.pdf. URL
Summary
The authors propose a decentralized self-organization mechanism that lets agents in a collaborative task-allocation network dynamically adapt their weighted relations (peer-to-peer and subordinate-superior) to improve overall profit. The mechanism combines two components: a Dezert-Smarandache-theory-based trust model that fuses direct experience and neighbor opinions to select candidate partners, and a multi-agent Q-learning algorithm that learns which relation-adaptation actions (enhance/weaken the relation type) pay off.
Unlike prior approaches that assume crisp binary relations, this model uses weighted relation strengths in [0,1], allowing gradual rather than sudden social change. Agents aim to minimize communication, computation, and management cost while maximizing subtask benefit. Reward matrices defined over action pairs couple the two learners so that joint optimal adaptations emerge.
Key Ideas
- Weighted relations replace crisp relations for realism.
- DSmT trust model fuses self and witness evidence.
- Multi-agent Q-learning for joint relation adaptation.
- Profit = benefit − (communication + computation + management costs).
- Decentralized, resilient to single-node failures.
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Conceptual Contribution
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