Semantics for hierarchical task-network planning
Semantics for hierarchical task-network planning
Fast planning through planning graph analysis
Artificial Intelligence
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
DiscoveryLink: a system for integrated access to life sciences data sources
IBM Systems Journal - Deep computing for the life sciences
An agent- and ontology-based system for integrating public gene, protein, and disease databases
Journal of Biomedical Informatics
Journal of Artificial Intelligence Research
IJCAI'75 Proceedings of the 4th international joint conference on Artificial intelligence - Volume 1
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We present a novel method to create complex search services over public online biomedical databases using hierarchical task network planning techniques. In the proposed approach, user queries are regarded as planning tasks (goals), while basic query services provided by the databases correspond to planning operators (POs). Each individual source is then mapped to a set of POs that can be used to process primitive (simple) queries. Advanced search services can be created by defining decomposition methods (DMs). The latter can be regarded as "recipes" that describe how to decompose non-primitive (complex) queries into sets of simpler subqueries following a divide-and-conquer strategy. Query processing proceeds by recursively decomposing non-primitive queries into smaller queries, until primitive queries are reached that can be processed using planning operators. Custom web search services can be created from the generated planners to provide biomedical researchers with valuable tools to process frequent complex queries.