To anticipate and prevent acts of terrorism, Indications and Warnings analysts try to connect clues gleaned from massive quantities of complex data. Multi-agent approaches to support Indications and Warnings are appropriate because ownership and security issues fragment the data. Furthermore, the massive scale of the data suggests the need for large numbers of agents.

The Ant CAF? system uses fine-grained swarming agents to extract and organize textual evidence that corroborates hypotheses about the state of the world. Multiple swarming processes are required, including the clustering of paragraphs, identification of semantic relations in text, and assembly of evidence into structures that instantiate the hypothesis. These processes occur in semantic spaces defined using the WordNet ontology.

This paper describes an Ant CAF? prototype. It describes the system?s architecture, and provides detail on the innovative algorithm for evidence assembly. Initial experiments using artificially generated data confirm that a global property that we call "clarity" emerges from agent decisions made in a local, and therefore scalable, manner.