A logical framework for default reasoning
Artificial Intelligence
Linear resolution for consequence finding
Artificial Intelligence
Minimal Answer Computation and SOL
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
Theory Completion Using Inverse Entailment
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
Induction as Consequence Finding
Machine Learning
Consequence finding and computing answers with defaults
Journal of Intelligent Information Systems
Metabolic Network Expansion with Answer Set Programming
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
Causality: Models, Reasoning and Inference
Causality: Models, Reasoning and Inference
Evaluating abductive hypotheses using an EM algorithm on BDDs
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
SOLAR: An automated deduction system for consequence finding
AI Communications - Practical Aspects of Automated Reasoning
Using theory completion to learn a robot navigation control program
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
A consequence finding approach for full clausal abduction
DS'07 Proceedings of the 10th international conference on Discovery science
Logic-Based Steady-State Analysis and Revision of Metabolic Networks with Inhibition
CISIS '10 Proceedings of the 2010 International Conference on Complex, Intelligent and Software Intensive Systems
Completing networks using observed data
ALT'09 Proceedings of the 20th international conference on Algorithmic learning theory
Discovering rules by meta-level abduction
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Inducing causal laws by regular inference
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Induction of the indirect effects of actions by monotonic methods
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Completing causal networks by meta-level abduction
Machine Learning
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Meta-level abduction discovers missing links and unknown nodes from incomplete networks to complete paths for observations. In this work, we extend applicability of meta-level abduction to deal with networks containing both positive and negative causal effects. Such networks appear in many domains including biology, in which inhibitory effects are important in signaling and metabolic pathways. Reasoning in networks with inhibition is inevitably nonmonotonic, and involves default assumptions in abduction. We show that meta-level abduction can consistently produce both positive and negative causal relations as well as invented nodes. Case studies of meta-level abduction are presented in p53 signaling networks, in which causal rules are abduced to suppress a tumor with a new protein and to stop DNA synthesis when damage is occurred.