Directed hypergraphs and applications
Discrete Applied Mathematics - Special issue: combinatorial structures and algorithms
A Multistrategy Approach to Relational Knowledge Discovery inDatabases
Machine Learning - Special issue on multistrategy learning
Relational reinforcement learning
Machine Learning - Special issue on inducive logic programming
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
Scaling Up Inductive Logic Programming by Learning from Interpretations
Data Mining and Knowledge Discovery
INDED: A Distributed Knowledge-Based Learning System
IEEE Intelligent Systems
Relational Knowledge Discovery in Databases
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Part-of-Speech Tagging Using Progol
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Strongly Typed Inductive Concept Learning
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Improving the efficiency of inductive logic programming through the use of query packs
Journal of Artificial Intelligence Research
STRIPS: a new approach to the application of theorem proving to problem solving
IJCAI'71 Proceedings of the 2nd international joint conference on Artificial intelligence
Top-down induction of first-order logical decision trees
Artificial Intelligence
Scalability and efficiency in multi-relational data mining
ACM SIGKDD Explorations Newsletter
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Many inductive systems, including ILP systems, learn from a knowledge base that is structured around examples. In practical situations this example-centered representation can cause a lot of redundancy. For instance, when learning from episodes (e.g. from games), the knowledge base contains consecutive states of a world. Each state is usually described completely even though consecutive states may differ only slightly. Similar redundancies occur when the knowledge base stores examples that share common structures (e.g. when representing complex objects as machines or molecules). These two types of redundancies can place a heavy burden on memory resources. In this paper we propose a method for representing knowledge bases in a more efficient way. This is accomplished by building a graph that implicitly defines examples in terms of other structures. We evaluate our method in the context of learning a Go heuristic.