In this paper, we investigate the semantic meaning of the messages passed in the Hugin architecture for prob-abilistic inference. By utilizing this information, one can avoid passing up to half of the messages that could have had to be passed in the Hugin architecture.
The multiply sectioned Bayesian network (MSBN) model successfully extends the traditional Bayesian network (BN) model for the support of probabilistic inference in distributed multi-agent systems. However, existing MSBN inference methods do not allow agents to reason about their own problem sub-domains right after the initialization process. Extensive… (More)
Programmable robots have become a very popular tool to introduce children to technology. However most curricula that emphasize on actual programming typically target kids 10 years and older. This summer, the University of New Hampshire organized an Elementary Program Introducing Computing (EPIC) camp. Children 7-9 years old, with no prior programming… (More)
A physical computing classroom is a popular setting to teach elementary students programming through the use of realistic physical hardware. However, various learning activities and their associated instructional media may cause distraction and disengagement in students' learning experiences. We propose a method to improve the design of learning activities… (More)
We present preliminary experiences in designing a Computer Science Principles undergraduate course for all majors that is based on physical computing with the Arduino microprocessor platform. The course goal is to introduce students to fundamental computing concepts in the context of developing concrete products. This physical computing approach is… (More)
The multiply sectioned Bayesian network (MSBN) is a well-studied model for probability reasoning in a multi-agent setting. Exact inference, however, becomes difficult as the problem domain grows larger and more complex. We address this issue by integrating approximation techniques with the MSBN Linked Junction Tree Forest (LJF) framework. In particular, we… (More)
Cooperative agents often need to reason about the states of a large and complex uncertain domain that evolves over time. Since exact calculation is usually impractical, we aim at providing a modeling tool that supports approximate online monitoring in such settings. Our proposed framework, the Multi-Agent Dynamic Bayesian Networks(MA-DBNs), models the… (More)
An increasing number of applications require cooperative agents to reason about the state of an distributed uncertainty domain. However, inference process of such system could become overly slow for practical applications, and there has been significant interest in developing faster approximation techniques. In this paper, we focus on the existing MSBN… (More)