8 research outputs found
Learning Linear Temporal Properties
We present two novel algorithms for learning formulas in Linear Temporal
Logic (LTL) from examples. The first learning algorithm reduces the learning
task to a series of satisfiability problems in propositional Boolean logic and
produces a smallest LTL formula (in terms of the number of subformulas) that is
consistent with the given data. Our second learning algorithm, on the other
hand, combines the SAT-based learning algorithm with classical algorithms for
learning decision trees. The result is a learning algorithm that scales to
real-world scenarios with hundreds of examples, but can no longer guarantee to
produce minimal consistent LTL formulas. We compare both learning algorithms
and demonstrate their performance on a wide range of synthetic benchmarks.
Additionally, we illustrate their usefulness on the task of understanding
executions of a leader election protocol
Reverse engineering queries in ontology-enriched systems: the case of expressive horn description logic ontologies
We introduce the query-by-example (QBE) paradigm for query answering in the presence of ontologies. Intuitively, QBE permits non-expert users to explore the data by providing examples of the information they (do not) want, which the system then generalizes into a query. Formally, we study the following question: given a knowledge base and sets of positive and negative examples, is there a query that returns all positive but none of the negative examples? We focus on description logic knowledge bases with ontologies formulated in Horn-ALCI and (unions of) conjunctive queries. Our main contributions are characterizations, algorithms and tight complexity bounds for QBE