Decentralized decision making under uncertainty has been shown to be intractable when each agent has different partial information about the domain. Thus, improving the applicabil...
Numerous logics have been developed for reasoning about inconsistency which differ in (i) the logic to which they apply, and (ii) the criteria used to draw inferences. In this pap...
As any other problem solving task that employs search, AI Planning needs heuristics to efficiently guide the problem-space exploration. Machine learning (ML) provides several tec...
Computing semantic relatedness of natural language texts requires access to vast amounts of common-sense and domain-specific world knowledge. We propose Explicit Semantic Analysi...
P-log is a probabilistic logic programming language, which combines both logic programming style knowledge representation and probabilistic reasoning. In earlier papers various ad...
In the context of operative disruption management, decision support systems have to evaluate the typically manifold options of responding to disturbances: The temporal shift of ac...
Statistical relational learning (SRL) algorithms learn statistical models from relational data, such as that stored in a relational database. We previously introduced view learnin...
Jesse Davis, Irene M. Ong, Jan Struyf, Elizabeth S...