Autonomous Agents and Multi-Agent Systems
Larks: Dynamic Matchmaking Among Heterogeneous Software Agents in Cyberspace
Autonomous Agents and Multi-Agent Systems
Graph-based technologies for intelligence analysis
Communications of the ACM - Homeland security
Graph-based Relational Learning with Application to Security
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
IAAI'07 Proceedings of the 19th national conference on Innovative applications of artificial intelligence - Volume 2
Process technology to facilitate the conduct of science
SPW'05 Proceedings of the 2005 international conference on Unifying the Software Process Spectrum
Modeling and Query Patterns for Process Retrieval in OWL
ISWC '09 Proceedings of the 8th International Semantic Web Conference
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As AI developers increasingly look to workflow technologies to perform complex integrations of individual software components, there is a growing need for the workflow systems to have expressive descriptions of those components. They must know more than just the types of a component's inputs and outputs; instead, they need detailed characterizations that allow them to make fine-grained distinctions between candidate components and between candidate workflows. This paper describes ProCat , an implemented ontology-based catalog for components, conceptualized as processes , that captures and communicates this detailed information. ProCat is built on a layered representation that allows reasoning about processes at varying levels of abstraction, from qualitative constraints reflecting preconditions and effects, to quantitative predictions about output data and performance. ProCat employs Semantic Web technologies RDF, OWL, and SPARQL, and builds on Semantic Web services research. We describe ProCat's approach to representing and answering queries about processes, discuss some early experiments evaluating the quantitative predictions, and report on our experience using ProCat in a system producing workflows for intelligence analysis.