Explicitly biased generalization
Computational Intelligence
Logic programs with classical negation
Logic programming
The well-founded semantics for general logic programs
Journal of the ACM (JACM)
Sub-unification: a tool for efficient induction of recursive programs
ML92 Proceedings of the ninth international workshop on Machine learning
Interactive Concept-Learning and Constructive Induction by Analogy
Machine Learning
Well founded semantics for logic programs with explicit negation
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
SLX—a top-down derivation procedure for programs with explicit negation
ILPS '94 Proceedings of the 1994 International Symposium on Logic programming
Interactive theory revision: an inductive logic programming approach
Interactive theory revision: an inductive logic programming approach
Strategies in Combined Learning via Logic Programs
Machine Learning - Special issue on multistrategy learning
A survey of paraconsistent semantics for logic programs
Handbook of defeasible reasoning and uncertainty management systems
Reasoning with Logic Programming
Reasoning with Logic Programming
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
‘Classical’ Negation in Nonmonotonic Reasoning and Logic Programming
Journal of Automated Reasoning
Learning Logical Definitions from Relations
Machine Learning
Generalizing Updates: From Models to Programs
LPKR '97 Selected papers from the Third International Workshop on Logic Programming and Knowledge Representation
Abduction over 3-Valued Extended Logic Programs
LPNMR '95 Proceedings of the Third International Conference on Logic Programming and Nonmonotonic Reasoning
Prolegomena to Logic Programming for Non-monotonic Reasoning
NMELP '96 Selected papers from the Non-Monotonic Extensions of Logic Programming
Learning extended logic programs
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
A Classification Theory Of Semantics Of Normal Logic Programs: Ii. Weak Properties
Fundamenta Informaticae
Strategies in Combined Learning via Logic Programs
Machine Learning - Special issue on multistrategy learning
Challenges for Inductive Logic Programming
EPIA '99 Proceedings of the 9th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
On the Use of Multi-dimensional Dynamic Logic Programming to Represent Societal Agents' Viewpoints
EPIA '01 Proceedings of the10th Portuguese Conference on Artificial Intelligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems, Logic Programming and Constraint Solving
Enabling Agents to Update Their Knowledge and to Prefer
EPIA '01 Proceedings of the10th Portuguese Conference on Artificial Intelligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems, Logic Programming and Constraint Solving
Learning in Clausal Logic: A Perspective on Inductive Logic Programming
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part I
Representation of Incomplete Knowledge by Induction of Default Theories
LPNMR '01 Proceedings of the 6th International Conference on Logic Programming and Nonmonotonic Reasoning
Multi-dimensional Dynamic Knowledge Representation
LPNMR '01 Proceedings of the 6th International Conference on Logic Programming and Nonmonotonic Reasoning
Nonmonotonic Inductive Logic Programming
LPNMR '01 Proceedings of the 6th International Conference on Logic Programming and Nonmonotonic Reasoning
Integrated Architectures for Machine Learning
Machine Learning and Its Applications, Advanced Lectures
Towards the Integration of Inductive and Nonmonotonic Logic Programming
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
Using methods of declarative logic programming for intelligent information agents
Theory and Practice of Logic Programming
Induction from answer sets in nonmonotonic logic programs
ACM Transactions on Computational Logic (TOCL)
Nonmonotonic inductive logic programming by instance patterns
Proceedings of the 9th ACM SIGPLAN international conference on Principles and practice of declarative programming
Determination of general concept in learning default rules
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Preferring and updating in logic-based agents
INAP'01 Proceedings of the Applications of prolog 14th international conference on Web knowledge management and decision support
Adaptive reasoning for cooperative agents
INAP'09 Proceedings of the 18th international conference on Applications of declarative programming and knowledge management
Xsb: Extending prolog with tabled logic programming
Theory and Practice of Logic Programming - Prolog Systems
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We discuss the adoption of a three-valued setting forinductive concept learning. Distinguishing between what is true, whatis false and what is unknown can be useful in situations wheredecisions have to be taken on the basis of scarce, ambiguous, ordownright contradictory information. In a three-valued setting, welearn a definition for both the target concept and its opposite,considering positive and negative examples as instances of twodisjoint classes. To this purpose, we adopt Extended Logic Programs(ELP) under a Well-Founded Semantics with explicit negation(WFSX) as the representation formalism for learning, and show howELPs can be used to specify combinations of strategies in adeclarative way also coping with contradiction and exceptions.Explicit negation is used to represent the opposite concept,while default negation is used to ensure consistency and tohandle exceptions to general rules. Exceptions are representedby examples covered by the definition for a concept that belongto the training set for the opposite concept.Standard Inductive Logic Programming techniques are employed tolearn the concept and its opposite. Depending on the adoptedtechnique, we can learn the most general or the least generaldefinition. Thus, four epistemological varieties occur,resulting from the combination of most general and least generalsolutions for the positive and negative concept. We discuss thefactors that should be taken into account when choosing andstrategically combining the generality levels for positive andnegative concepts.In the paper, we also handle the issue of strategic combinationof possibly contradictory learnt definitions of a predicate andits explicit negation.All in all, we show that extended logic programs underwell-founded semantics with explicit negation add expressivityto learning tasks, and allow the tackling of a number ofrepresentation and strategic issues in a principled way.Our techniques have been implemented and examples run on astate-of-the-art logic programming system with tabling whichimplements WFSX.