Collaborating Authors

Snasci Reasoning


Some of the above list may not be traditionally thought of, or referred to, as types of reasoning. It is only when approaching the practical development of an Artificial General Intelligence is it observed that each of these are approaches or methodologies that constitute a form of reasoning independent of the others. Leveraging these reasoning types and approaches, Snasci will also be quite creative, capable of lying, story telling, humour and adapting to new situations without prior training. In business and scientific applications, Snasci's reasoning and comprehension capabilities will become invaluable. In addition, the ability to connect a wide range of sensors, novel inputs and outputs (including HPC rendering) means that any lab, research and development department, university, etc., can have a world class installation and knowledge base for a fraction of what it currently costs.

Second International Workshop on Nonmonotonic Reasoning

AI Magazine

The contributions to this workshop indicate substantial advances in the technical foundations of the field. They also show that it is time to evaluate the existing approaches to commonsense reasoning problems.

A Model-Theoretic View on Qualitative Constraint Reasoning

Journal of Artificial Intelligence Research

Qualitative reasoning formalisms are an active research topic in artificial intelligence. In this survey we present a model-theoretic perspective on qualitative constraint reasoning and explain some of the basic concepts and results in an accessible way. In particular, we discuss the significance of omega-categoricity for qualitative reasoning, of primitive positive interpretations for complexity analysis, and of Datalog as a unifying language for describing local consistency algorithms.

The Case-Based Reasoning Group

AITopics Original Links

Current research projects include projects to investigate the use of multiple case representation and indexing schemes in precedent-based CBR, the effect of high level reasoning goals on supporting CBR tasks and vice versa in a mixed paradigm blackboard-based architecture, the use of CBR for generation of retrieval strategies in the context of information retrieval, and the automatic selection of parameters for dynamic scheduling problems.