Hypothesis Corroboration in Semantic Spaces with Swarming Agents

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. This paper presents the architecture and algorithms of our Ant CAFÉ system, which uses fine-grained swarming agents to extract and organize textual evidence that corroborates hypotheses about the state of the world. Multiple swarming processes operating in semantic spaces are required, including the clustering of paragraphs, identification of semantic relations in text, and assembly of evidence into structures that instantiate the hypothesis.

• Type: Conference
• Year: 2004
• Where: ANTSW
• Authors: Peter Weinstein, H. Van Dyke Parunak, Paul Chiusano, Sven Brueckner
• Keywords: ANTSW 2004, Artificial Intelligence, Fine-grained Swarming Agents, Multiple Swarming Processes, Security Issues Fragment