ARTS: agent-oriented robust transactional system

Authors:
Mingzhong Wang;Amy Unruh;Kotagiri Ramamohanarao
Affiliations:
The University of Melbourne, Australia;The University of Melbourne, Australia;The University of Melbourne, Australia
Venue:
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Year:
2007

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Abstract

This paper presents the ARTS (Agent-oriented Robust Transactional System) model, which applies transaction concepts to provide agent developers with high-level support for agent system robustness and reliability. ARTS abstractly considers agents as executors of encapsulated task entities which comply with a set of execution constraints on both normative execution and compensation (repair) semantics. ARTS then defines the task interface in terms of predictable terminating states to support a contract-like interaction among agents. In conjunction with this encapsulation of task semantics, ARTS defines a model for specifying scoped compensation and exception-handling plans for a given task, and for systematically selecting and executing these plans --- triggered by subtask events --- so that the enclosing task semantics are enforced. These capabilities together define a model that reduces design complexity while increasing system robustness, by allowing an agent developer to compose recursively-defined, atomically-handled tasks.