Semantic Approach to Engineering Agent-Based Systems

When it comes to developing complex, distributed software-based systems,
the agent-based approach was advocated to be a well suited one.
Although the flexibility of agent interactions has many advantages
when it comes to engineering a complex system, the downside is that it leads
to certain unpredictability of the run-time system: as agents are autonomous,
the patterns and the effects of their interactions are uncertain.
Literature sketches two major directions for search for a solution:
social-level characterization of agent systems and ontological approaches
to inter-agent coordination.

We work on a solution to advance into both these directions.
With respect to data, we use the semantic (RDF-based) data model for both
the description (i.e. agents' knowledge, goals) and the prescription
(programming agents' behaviors, i.e. a semantic agent programming language).
With respect to architecture, we use role-based behavior prescriptions,
and externalization of those prescription, i.e. agents access them from
organizational repositories when needed.