Learning from interpretation transition

Inoue, Katsumi; Ribeiro, Tony; Sakama, Chiaki

doi:10.1007/s10994-013-5353-8

Cited by 58 publications

(99 citation statements)

References 43 publications

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“…Another framework, under the supported model semantics rather than the answer set semantics, is Learning from Interpretation Transitions (LFIT) [47]. In LFIT, the examples are pairs of interpretations I, J where J is the set of immediate consequences of I given B ∪ H .…”

Section: Other Learning Frameworkmentioning

confidence: 99%

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: Other Learning Frameworkmentioning

confidence: 99%

The complexity and generality of learning answer set programs

Law

Russo

Broda

2018

Artificial Intelligence

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“…The idea of context-dependent example has similarities with the concept of learning from interpretation transitions (LFIT) (Inoue et al 2014), where examples are pairs of set of atoms I , J such that B ∪ H must satisfy T B∪H (I ) = J (where T P (I ) is the set of immediate consequences of I with respect to the program P ). LFIT technically learns under the supported model semantics and uses a far smaller language than that supported by ILP context LOAS (not supporting choice rules or hard or weak constraints), but can be simply represented in ILP context LOAS .…”

Section: Related Workmentioning

confidence: 99%

Iterative Learning of Answer Set Programs from Context Dependent Examples

Law¹,

Russo²,

Broda³

2016

Theory and Practice of Logic Programming

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In recent years, several frameworks and systems have been proposed that extend Inductive Logic Programming (ILP) to the Answer Set Programming (ASP) paradigm. In ILP, examples must all be explained by a hypothesis together with a given background knowledge. In existing systems, the background knowledge is the same for all examples; however, examples may be context-dependent. This means that some examples should be explained in the context of some information, whereas others should be explained in different contexts. In this paper, we capture this notion and present a context-dependent extension of the Learning from Ordered Answer Sets framework. In this extension, contexts can be used to further structure the background knowledge. We then propose a new iterative algorithm, ILASP2i, which exploits this feature to scale up the existing ILASP2 system to learning tasks with large numbers of examples. We demonstrate the gain in scalability by applying both algorithms to various learning tasks. Our results show that, compared to ILASP2, the newly proposed ILASP2i system can be two orders of magnitude faster and use two orders of magnitude less memory, whilst preserving the same average accuracy. This paper is under consideration for acceptance in TPLP.

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“…More recently, in [3], the authors question the kind of properties that may be preserved, whatever the semantics, while discussing the merits of the usual updating modes including synchronous, fully asynchronous and generalized asynchronous updating. As a good choice of semantics is key to a sound analysis of a system, it is critical to be able to learn not only one kind of semantics, but to embrace a wide range of updating modes.So far, learning from interpretation transition (LFIT) [9] has been proposed to automatically construct a model of the dynamics of a system from the observation of its state transitions. Figure 1 shows this learning process.…”

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confidence: 99%

“…The LFIT framework proposes several modeling and learning algorithms to tackle those different semantics. To date, the following systems have been tackled: memory-less consistent systems [9], systems with memory [14], non-consistent systems [12] …”

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confidence: 99%

Learning Dynamics with Synchronous, Asynchronous and General Semantics

Ribeiro

Folschette

Magnin

et al. 2018

Inductive Logic Programming

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Abstract.Learning from interpretation transition (LFIT) automatically constructs a model of the dynamics of a system from the observation of its state transitions. So far, the systems that LFIT handles are restricted to synchronous deterministic dynamics, i.e., all variables update their values at the same time and, for each state of the system, there is only one possible next state. However, other dynamics exist in the field of logical modeling, in particular the asynchronous semantics which is widely used to model biological systems. In this paper, we focus on a method that learns the dynamics of the system independently of its semantics. For this purpose, we propose a modeling of multi-valued systems as logic programs in which a rule represents what can occurs rather than what will occurs. This modeling allows us to represent nondeterminism and to propose an extension of LFIT in the form of a semantics free algorithm to learn from discrete multi-valued transitions, regardless of their update schemes. We show through theoretical results that synchronous, asynchronous and general semantics are all captured by this method. Practical evaluation is performed on randomly generated systems and benchmarks from biological literature to study the scalability of this new algorithm regarding the three aforementioned semantics.Keywords: Dynamical semantics, learning from interpretation transition, dynamical systems, Inductive Logic Programming IntroductionLearning the dynamics of systems with many interactive components becomes more and more important due to many applications, e.g., multi-agent systems, robotics and bioinformatics. Knowledge of a system dynamics can be used by agents and robots for planning and scheduling. In bioinformatics, learning the dynamics of biological systems can correspond to the identification of the influence of genes and can help to understand their interactions. While building a model, the choice of a relevant semantics associated to the studied system represents a major issue with regard to the kind of dynamical properties to analyze. The differences and common features of different semantics w.r.t. properties of interest (attractors, oscillators, etc.) constitutes an area of research per itself, especially in the field of Boolean networks. In [8], the author exhibits the translation from Boolean networks into logic programs and discusses the point attractors in both synchronous and asynchronous semantics. In [6], A. Garg et al. address the differences and complementarity of synchronous and asynchronous semantics to model biological networks and identify attractors. The benefits of the synchronous model are to be computationally tractable, while classical state space exploration algorithms fail on asynchronous ones. For some applications, like the biological ones, asynchronous semantics is said to capture more realistic behaviors: at a given time, a single gene can change its expression level. This results in a potential combinatorial explosion of the number of reachable states. To illustr...

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Learning from interpretation transition

Cited by 58 publications

References 43 publications

The complexity and generality of learning answer set programs

The complexity and generality of learning answer set programs

Iterative Learning of Answer Set Programs from Context Dependent Examples

Learning Dynamics with Synchronous, Asynchronous and General Semantics

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