Symbolic reasoning is a well understood and effective approach to handling reasoning over formally represented knowledge; however, simple symbolic inference systems necessarily sl...
Matthew E. Taylor, Cynthia Matuszek, Pace Reagan S...
Temporal Networks play an important role in solving planning problems and they are also used, though not as frequently, when solving scheduling problems. In this paper we propose ...
Researchers have reported successful deployments of diagnosis decision support systems based on Bayesian networks. However, the methodology for evaluating the diagnosability for s...
Context-sensitive Multiple Task Learning, or csMTL, is presented as a method of inductive transfer that uses a single output neural network and additional contextual inputs for le...
We describe UCFTAC, a trading agent based on control based principles, which has participated with success in 2006 Supply Chain Management Trading Agent Competition. The UCFTAC ap...
Discovery of graphical models is NP-hard in general, which justifies using heuristics. We consider four commonly used heuristics. We summarize the underlying assumptions and anal...
We deal with the search process of the GraphPlan algorithm in this paper. We concentrate on a problem of finding supports for a sub-goal which arises during the search. We model t...
Macalester College offers a single undergraduate elective in artificial intelligence. This course is cross-listed between Computer Science and Cognitive and Neuroscience Studies,...
Much work has been done in the area of qualitative spatial reasoning over the past years, with application in various domains. However, existing models only capture particular asp...
This paper describes a hybrid model that combines machine learning with linguistic heuristics for integrating unknown word identification with Chinese word segmentation. The model...