Bing Liu and Joxan Jaffar

AAAI Conferences

In this integration, a constraint variable or a constraint is regarded as a special fact, and rules can be written with constraints and information about constraints. Chaining of rules may trigger constraint propagation, and constraint propagation may cause fn-ing of rules. A prototype system (called CFR) based on this idea has been implemented.


Expressive Rule-Based Stream Reasoning

AAAI Conferences

Stream reasoning is the task of continuously deriving conclusions on streaming data. As a research theme, it is targeted by different communities which emphasize different aspects, e.g., throughput vs. expressiveness. This thesis aims to advance the theoretical foundations underlying diverse stream reasoning approaches and to convert obtained insights into a prototypical expressive rule-based reasoning system that is lacking to date.


Combining Uncertainty and Description Logic Rule-Based Reasoning in Situation-Aware Robots

AAAI Conferences

The paper addresses how a robot can maintain a state representation of all that it knows about the environment over time and space, given its observations and its domain knowledge. The advantage in combining domain knowledge and observations is that the robot can in this way project from the past into the future, and reason from observations to more general statements to help guide how it plans to act and interact. The difficulty lies in the fact that observations are typically uncertain and logical inference for completion against a knowledge base is computationally hard.


Stream Reasoning on Expressive Logics

arXiv.org Artificial Intelligence

Data streams occur widely in various real world applications. The research on streaming data mainly focuses on the data management, query evaluation and optimization on these data, however the work on reasoning procedures for streaming knowledge bases on both the assertional and terminological levels is very limited. Typically reasoning services on large knowledge bases are very expensive, and need to be applied continuously when the data is received as a stream. Hence new techniques for optimizing this continuous process is needed for developing efficient reasoners on streaming data. In this paper, we survey the related research on reasoning on expressive logics that can be applied to this setting, and point to further research directions in this area.