Committment-Based Data-Aware Multi-Agent-Contexts Systems
This work addresses a gap in multi-agent systems for AI researchers by combining commitment-based communication with external data sources, but it appears incremental as it builds on existing DACMAS and Managed MCSs frameworks.
The paper tackles the problem of enabling multi-agent systems to interact with heterogeneous external information sources by proposing DACmMCMASs, a commitment-based first-order agent system that integrates with external contexts, and shows it retains formal properties of prior approaches.
Communication and interaction among agents have been the subject of extensive investigation since many years. Commitment-based communication, where communicating agents are seen as a debtor agent who is committed to a creditor agent to bring about something (possibly under some conditions) is now very well-established. The approach of DACMAS (Data-Aware Commitment-based MAS) lifts commitment-related approaches proposed in the literature from a propositional to a first-order setting via the adoption the DRL-Lite Description Logic. Notably, DACMASs provide, beyond commitments, simple forms of inter-agent event-based communication. Yet, the aspect is missing of making a MAS able to acquire knowledge from contexts which are not agents and which are external to the MAS. This topic is coped with in Managed MCSs (Managed Multi-Context Systems), where however exchanges are among knowledge bases and not agents. In this paper, we propose the new approach of DACmMCMASs (Data-Aware Commitment-based managed Multi- Context MAS), so as to obtain a commitment-based first-order agent system which is able to interact with heterogeneous external information sources. We show that DACmMCMASs retain the nice formal properties of the original approaches.