Using language technology for text analysis and light-weight ontologies as a content-mediating level, we acquire indexing patterns from vast amounts of indexing data for Englishla...
Combining multiple classifiers via combining schemes or meta-learners has led to substantial improvements in many classification problems. One of the challenging tasks is to choos...
Common sense knowledge can be efficiently collected from non-experts over the web in a similar fashion to the Open Mind family of distributed knowledge capture projects. We descri...
Simulation-based training is increasingly being used within the military to practice and develop the skills of successful soldiers. For the skills associated with successful milit...
Andrew Gordon, Michael van Lent, Martin Van Velsen...
In recent years there has been a great deal of interest in "modular reinforcement learning" (MRL). Typically, problems are decomposed into concurrent subgoals, allowing ...
Sooraj Bhat, Charles Lee Isbell Jr., Michael Matea...
Despite the significant progress to extend Markov Decision Processes (MDP) to cooperative multi-agent systems, developing approaches that can deal with realistic problems remains ...
The relation between answer set programming (ASP) and propositional satisfiability (SAT) is at the center of many research papers, partly because of the tremendous performance boo...
This paper proposes a novel method to characterize the performance of autonomous agents in the Trading Agent Competition for Supply Chain Management (TAC-SCM). We create benchmark...
Constraint programming is a commonly used technology for solving complex combinatorial problems. However, users of this technology need significant expertise in order to model the...