Improving the Forward Chaining Algorithm for Conceptual Graphs Rules

baget2004bInternational audienceSimple Conceptual Graphs (SGs) are used to represent entities and relations between these entities: they can be translated into positive, conjunctive, existential first-order logics, without function symbols. Sound and complete reasonings w.r.t. associated logic formulas are obtained through a kind of graph homomorphism called projection. Conceptual Graphs Rules (or CG rules) are a standard extension to SGs, keeping sound and complete reasonings w.r.t. associated logic formulas (they have the same form as tuple generating dependencies in database): these graphs represent knowledge of the form ''IF ... THEN''. We present here an optimization of the natural forward chaining algorithm for CG rules. Generating a graph of rules dependencies makes the following sequences of rule applications far more efficient, and the structure of this graph can be used to obtain new decidability results

ECAI 98 Brighton: 13th European Conference on Artificial Intelligence, Brighton, UK, August 23-28 1998, Proceedings

The 13th European Conference on Artificial Intelligence, held in Brighton, UK, in August 1998, attracted 456 submissions from 43 different countries, including 138 short papers presenting recent ongoing works by young researchers. The aim of the conference is to cover all aspects of AI, and to bring together basic and applied research. The International Programme Committee selected 158 papers, which are presented in this volume. The proceedings are organized into 16 sections: Belief Revision and Nonmonotonic Reasoning; Case-Based Reasoning and Knowledge-Based Systems; Cognitive Modelling; Computational Linguistics and Ontologies; Constraint-Based Reasoning; Diagnosis; Distributed AI and Multiagent Systems; Knowledge Representation; Logic Programming and Automated Reasoning; Machine Learning and Data Mining; Planning and Scheduling; Reasoning about Actions, Temporal and Spatial Reasoning; Reasoning under Uncertainty (Probabilistic and Fuzzy Set-Based Modelling); Robotics, Vision and Signal Understanding; Search and Meta-Heuristics for AI; and User Interfaces. With an acceptance rate of 34%, ECAI-98, like the previous ECAI conferences, belongs to the stream of high quality AI conferences. The proceedings also contain extended abstracts or papers accompanying the six invited talks