Using interactive concept learning for knowledge-base validation and verification
Validation, verification and test of knowledge-based systems
Recovering software specifications with inductive logic programming
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
First-order jk-clausal theories are PAC-learnable
Artificial Intelligence
Interactive theory revision: an inductive logic programming approach
Interactive theory revision: an inductive logic programming approach
Machine Learning - special issue on inductive logic programming
Logical settings for concept-learning
Artificial Intelligence
Lookahead and Discretization in ILP
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
The RoboCup synthetic agent challenge 97
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Learning action descriptions of opponent behaviour in the robocup 2D simulation environment
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
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As in many multi-agent applications, most RoboCup agents are complex systems, hard to construct and hard to check if they behave as intended. We present a technique to verify multi-agent systems based on inductive reasoning. Induction allows to derive general rules from specific examples (e.g. the inputs and outputs of software systems). Using inductive logic programming, partial declarative specifications of the software can be induced. These rules can be readily interpreted by the designers or users of the software, and can in turn result in changes to the software. The approach outlined was used to test the KULRoT RoboCup simulator team, which is briefly described.