We investigate the computational complexity of mining guarded clauses from clausal datasets through the framework of inductive logic programming (ILP). We show that learning guarded clauses is NP-complete and thus one step below the Σ P 2 -complete task of learning Horn clauses on the polynomial hierarchy. Motivated by practical applications on large datasets we identify a natural tractable fragment of the problem. Finally, we also generalise all of our results to k-guarded clauses for constant k
AbstractUnification complexity of Horn clause programs is introduced, and its complexity is investig...
Abstract. The main contribution of the paper is a PTIME decision procedure for the satisfiability pr...
International audienceIn Inductive Logic Programming (ILP), algorithms that are purely of the bottom...
AbstractThe efficient learnability of restricted classes of logic programs is studied in the PAC fra...
In this paper we investigate the efficiency of `-- subsumption (` ` ), the basic provability relati...
The field of Inductive Logic Programming (ILP) is concerned with inducing logic pr~ grams from examp...
The efficient learnability of restricted classes of logic programs is studied in the PAC framework o...
AbstractRecently there has been an increasing amount of research on learning concepts expressed in s...
Learning logic programs, also referred to as inductive logic programming (ILP), is a relatively new ...
We design learning algorithms for synthesizing invariants using Horn implication counterexamples (Ho...
We present positive PAC-learning results for the nonmonotonic inductive logic programming setting. I...
AbstractA new algorithm for renaming a set of clauses as a Horn set is presented. Its time and space...
Inductive Logic Programming (ILP) is concerned with learning hypotheses from examples, where both ex...
When mining frequent Datalog queries, many queries will cover the same examples; i.e., they will be ...
When mining frequent Datalog queries, many queries will cover the same examples; i.e. they will be e...
AbstractUnification complexity of Horn clause programs is introduced, and its complexity is investig...
Abstract. The main contribution of the paper is a PTIME decision procedure for the satisfiability pr...
International audienceIn Inductive Logic Programming (ILP), algorithms that are purely of the bottom...
AbstractThe efficient learnability of restricted classes of logic programs is studied in the PAC fra...
In this paper we investigate the efficiency of `-- subsumption (` ` ), the basic provability relati...
The field of Inductive Logic Programming (ILP) is concerned with inducing logic pr~ grams from examp...
The efficient learnability of restricted classes of logic programs is studied in the PAC framework o...
AbstractRecently there has been an increasing amount of research on learning concepts expressed in s...
Learning logic programs, also referred to as inductive logic programming (ILP), is a relatively new ...
We design learning algorithms for synthesizing invariants using Horn implication counterexamples (Ho...
We present positive PAC-learning results for the nonmonotonic inductive logic programming setting. I...
AbstractA new algorithm for renaming a set of clauses as a Horn set is presented. Its time and space...
Inductive Logic Programming (ILP) is concerned with learning hypotheses from examples, where both ex...
When mining frequent Datalog queries, many queries will cover the same examples; i.e., they will be ...
When mining frequent Datalog queries, many queries will cover the same examples; i.e. they will be e...
AbstractUnification complexity of Horn clause programs is introduced, and its complexity is investig...
Abstract. The main contribution of the paper is a PTIME decision procedure for the satisfiability pr...
International audienceIn Inductive Logic Programming (ILP), algorithms that are purely of the bottom...