Logical characterisations of inductive learning
Handbook of defeasible reasoning and uncertainty management systems
Machine Learning
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Defeasible logic programming: an argumentative approach
Theory and Practice of Logic Programming
Argumentation Semantics for Defeasible Logic
Journal of Logic and Computation
Arguing and explaining classifications
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Elements of Argumentation
Inconsistency tolerance in weighted argument systems
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
A lattice-based approach to computing warranted beliefs in skeptical argumentation frameworks
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Dialectical abstract argumentation: a characterization of the marking criterion
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Concept convergence in empirical domains
DS'10 Proceedings of the 13th international conference on Discovery science
A defeasible reasoning model of inductive concept learning from examples and communication
Artificial Intelligence
| Hi-index | 0.00 |
This paper focuses on a logical model of induction, and specifically of the common machine learning task of inductive concept learning (ICL). We define an inductive derivation relation, which characterizes which hypothesis can be induced from sets of examples, and show its properties. Moreover, we will also consider the problem of communicating inductive inferences between two agents, which corresponds to the multi-agent ICL problem. Thanks to the introduced logical model of induction, we will show that this communication can be modeled using computational argumentation.