Increasingly, multi-agent systems are being designed for a variety of complex, dynamic domains. E ective agent interactions in such domains raise some of most fundamental research...
In this paper, we study the problem of learning block classification models to estimate block functions. We distinguish general models, which are learned across multiple sites, an...
The authors present TWIG, a visually grounded wordlearning system that uses its existing knowledge of vocabulary, grammar, and action schemas to help it learn the meanings of new ...
Most studies in statistical or machine learning based authorship attribution focus on two or a few authors. This leads to an overestimation of the importance of the features extra...
This paper presents various strategies for improving the extraction performance of less prominent relations with the help of the rules learned for similar relations, for which lar...