An inference graph can have many \deriva-tion strategies", each a particular ordering of the steps involved in reducing a given query to a sequence of database retrievals. An \optimal strategy" for a given distribution of queries is a complete strategy whose \expected cost" is minimal, where the expected cost depends on the conditional probabilities that each requested retrieval succeeds , give...

This study examined the impact of an automated reading strategy trainer called the Interactive Strategy Trainer for Active Reading and Thinking (iSTART) for improving students’ reading comprehension of a science text. iSTART is an interactive trainer that utilizes animated agents to provide reading strategy instruction. The program contains both vicarious and interactive modules that provide ad...

Multi-agent multi-team systems are commonly seen in environments where hierarchical layers of goals are at play. For example, theater-wide combat scenarios where multiple levels of command and control are required for proper execution of goals from the general to the foot soldier. Similar structures can be seen in game environments, where agents work together as teams to compete with other team...

In the Semantic Web, RDF (Resource Description Framework) and RDF Schema are commonly used to describe metadata. There are a great many RDF data in current web, therefore, efficient storage and retrieval of large RDF data sets is required. So far, several RDF storage and query system are developed. According to the inference strategy they used, they can be classified into two categories, one ex...

Latent conditional models have become popular recently in both natural language processing and vision processing communities. However, establishing an effective and efficient inference method on latent conditional models remains a question. In this paper, we describe the latent-dynamic inference (LDI), which is able to produce the optimal label sequence on latent conditional models by using eff...

Stochastic variational inference allows for fast posterior inference in complex Bayesian models. However, the algorithm is prone to local optima which can make the quality of the posterior approximation sensitive to the choice of hyperparameters and initialization. We address this problem by replacing the natural gradient step of stochastic varitional inference with a trust-region update. We sh...

Stochastic variational inference is a promising method for fitting large-scale probabilistic models with hidden structures. Different from traditional stochastic learning, stochastic variational inference uses the natural gradient, which is particularly efficient for computing probabilistic distributions. One of the issues in stochastic variational inference is to set an appropriate learning ra...