By Gerson Zaverucha, Vítor Santos Costa, Aline Paes

This e-book constitutes the completely refereed post-proceedings of the twenty third foreign convention on Inductive common sense Programming, ILP 2013, held in Rio de Janeiro, Brazil, in August 2013.

The nine revised prolonged papers have been conscientiously reviewed and chosen from forty two submissions. The convention now specializes in all features of studying in common sense, multi-relational studying and information mining, statistical relational studying, graph and tree mining, relational reinforcement studying, and other kinds of studying from based data.

**Read Online or Download Inductive Logic Programming: 23rd International Conference, ILP 2013, Rio de Janeiro, Brazil, August 28-30, 2013, Revised Selected Papers PDF**

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**Extra info for Inductive Logic Programming: 23rd International Conference, ILP 2013, Rio de Janeiro, Brazil, August 28-30, 2013, Revised Selected Papers**

**Example text**

To avoid the computation of redundant hypotheses, the meta-level encoding of RM makes use of r of clauses. This a canonical projection of the clause space RM into a set RM r makes each clause in RM represents a class of clauses in RM that are compatible with the same mode declarations in M , but that diﬀer only in the ordering of the body literals not subject to the link constraint over arguments. Every clause r [3,5]. within RM is thus represented by an equivalent clause in RM ASP encoding of mode declarations.

2 A Lower Bound on Privacy Parameter Our techniques to prove the lower bound on the privacy parameter come from diﬀerentially private itemset mining [8]. Perhaps this is no surprise since both frequent itemset mining and association rule mining have been closely connected with the context of ILP [9] in which frequent itemset mining can be encoded as a ILP problem. We prove the lower bound on the privacy parameter if an ILP algorithm must be both -diﬀerentially private and (δ, η)-useful. This is shown in Theorem 3.

2. Completeness: if general clause g is true in M + (B), then H |= g. 3. Minimality: there is no proper subset G of H which is valid and complete. 1 This formulation uses non-monotonic semantics. 20 C. Zeng et al. In the rest of the paper, we assume both L1 and L2 are ﬁxed unless otherwise speciﬁed. Note that in the literature of diﬀerential privacy [2], the terminology of “background knowledge” is diﬀerent from the context in ILP and denotes the side information an adversary possesses to attack the privacy of a speciﬁc individual in the underlying database.