Brave induction: a logical framework for learning from incomplete information

Sakama, Chiaki; Inoue, Katsumi

doi:10.1007/s10994-009-5113-y

Cited by 32 publications

(30 citation statements)

References 39 publications

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“…The purpose of the hypothesis space is two-fold: firstly, it allows the task to be restricted to those solutions which are in some way interesting; secondly, it aids the computational search for inductive solutions. Tasks for brave and cautious induction and for induction of stable models were originally presented with no hypothesis space [28,30] as they were mainly considered theoretically without the specifications of efficient algorithmic computations. The only publicly available algorithms for brave induction [38,39] make use of a hypothesis space defined by mode declarations [40].…”

Section: Learning Frameworkmentioning

confidence: 99%

“…The structure of E depends on the type of ILP framework. Each of the papers [28], [30], [31] and [23] presented learning frameworks with different languages for B and S M ; for example, induction of stable models was presented only for normal logic programs. It would be unfair to say that induction from stable models is not general enough to learn programs with choice rules, simply because they were not considered in the original paper (in fact, induction from stable models is general enough to learn some programs with choice rules).…”

Section: Notation and Terminologymentioning

confidence: 99%

“…Brave induction (I L P b ), first presented in [28], defines an inductive task in which all examples are ground atoms that should be covered in at least one answer set, i.e. entailed under brave entailment in ASP.…”

Section: Framework Definitionsmentioning

confidence: 99%

“…[28] presented the notions of brave induction (I L P b ) and cautious induction (I L P c ), based respectively on the well established notions of entailment under the answer set semantics [13,29] of brave entailment (when an atom is true in at least one answer set) and cautious entailment (when and an atom is true in all answer sets). In brave induction, at least one answer set must cover the examples, whereas in cautious induction, every answer set must cover the examples.…”

Section: Introductionmentioning

confidence: 99%

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The complexity and generality of learning answer set programs

Law

Russo

Broda

2018

Artificial Intelligence

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Traditionally most of the work in the field of Inductive Logic Programming (ILP) has addressed the problem of learning Prolog programs. On the other hand, Answer Set Programming is increasingly being used as a powerful language for knowledge representation and reasoning, and is also gaining increasing attention in industry. Consequently, the research activity in ILP has widened to the area of Answer Set Programming, witnessing the proposal of several new learning frameworks that have extended ILP to learning answer set programs. In this paper, we investigate the theoretical properties of these existing frameworks for learning programs under the answer set semantics. Specifically, we present a detailed analysis of the computational complexity of each of these frameworks with respect to the two decision problems of deciding whether a hypothesis is a solution of a learning task and deciding whether a learning task has any solutions. We introduce a new notion of generality of a learning framework, which enables us to define a framework to be more general than another in terms of being able to distinguish one ASP hypothesis solution from a set of incorrect ASP programs. Based on this notion, we formally prove a generality relation over the set of existing frameworks for learning programs under answer set semantics. In particular, we show that our recently proposed framework, Context-dependent Learning from Ordered Answer Sets, is more general than brave induction, induction of stable models, and cautious induction, and maintains the same complexity as cautious induction, which has the highest complexity of these frameworks.

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Section: Learning Frameworkmentioning

confidence: 99%

Section: Notation and Terminologymentioning

confidence: 99%

Section: Framework Definitionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The complexity and generality of learning answer set programs

Law

Russo

Broda

2018

Artificial Intelligence

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“…The goal of our learning task is to find a hypothesis H ⊆ SM such that B ∪ H has at least one answer set 2 which contains every positive example and contains no negative example. In the ILP literature, this is known as brave induction [32].…”

Section: The Asp Program That Formalisesmentioning

confidence: 99%

Privacy dynamics

Çalıklı

Law

Bandara

et al. 2016

Proceedings of the 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

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Privacy violations in online social networks (OSNs) often arise as a result of users sharing information with unintended audiences. One reason for this is that, although OSN capabilities for creating and managing social groups can make it easier to be selective about recipients of a given post, they do not provide enough guidance to the users to make informed sharing decisions. In this paper we present Privacy Dynamics, an adaptive architecture that learns privacy norms for different audience groups based on users' sharing behaviours. Our architecture is underpinned by a formal model inspired by social identity theory, a social psychology framework for analysing group processes and intergroup relations. Our formal model comprises two main concepts, the group membership as a Social Identity (SI) map and privacy norms as a set of conflict rules. In our approach a privacy norm is specified in terms of the information objects that should be prevented from flowing between two conflicting social identity groups. We implement our formal model by using inductive logic programming (ILP), which automatically learns privacy norms. We evaluate the performance of our learning approach using synthesised data representing the sharing behaviour of social network users.

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