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  • Pippi___MAS_Instantiation

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Pippi: Practical Protocol Instantiation

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Publication date13/05/2022
Host publicationAAMAS '22: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Number of pages281
ISBN (Electronic)9781450392136
<mark>Original language</mark>English
EventInternational Conference on Autonomous Agents and Multiagent Systems 2022 - Online, Aukland, New Zealand
Duration: 9/05/202213/05/2022
https://aamas2022-conference.auckland.ac.nz/

Conference

ConferenceInternational Conference on Autonomous Agents and Multiagent Systems 2022
Abbreviated titleAAMAS 2022
Country/TerritoryNew Zealand
CityAukland
Period9/05/2213/05/22
Internet address

Conference

ConferenceInternational Conference on Autonomous Agents and Multiagent Systems 2022
Abbreviated titleAAMAS 2022
Country/TerritoryNew Zealand
CityAukland
Period9/05/2213/05/22
Internet address

Abstract

A protocol specifies interactions between roles, which together constitute a multiagent system (MAS). Enacting a protocol presupposes that agents are bound to the its roles. Existing protocol-based approaches, however, do not adequately treat the practical aspects of how roles bindings come about.

Pippi addresses this problem of MAS instantiation. It proposes the notion of a metaprotocol, enacting which instantiates a MAS suitable for enacting a given protocol. Pippi demonstrates the subtleties involved in instantiating MAS arising from protocol composition, correlation, and decentralization. To address these subtleties and further support practical application patterns, we introduce an enhanced protocol language, with support for parameter types (including role and protocol typed parameters, for metaprotocols), interface flexibility, and binding constraints. We discuss the realization of our approach through an extended agent architecture, including the novel concept of a MAS adapter for contact management. We evaluate Pippi's expressiveness by demonstrating common patterns for agent discovery.