Connectionist artificial neural networks

The 2010 International Joint Conference on Neural Networks (IJCNN), 2010

Artificial Neural Networks have previously been applied in neuro-symbolic learning to learn ground logic program rules. However, there are few results of learning relations using neuro-symbolic learning. This paper presents the system PAN, which can learn relations defined by a logic program clause. The inputs to PAN are one or more atoms, representing the conditions of a logic rule, and the output is the conclusion of the rule. The symbolic inputs may include functional terms of arbitrary depth and arity, and the output may include terms constructed from the input functors. Symbolic inputs are encoded as an integer using an invertible encoding function, which is used in reverse to extract the output terms. The main advance of this system is a convention to allow construction of Artificial Neural Networks able to learn rules with the same power of expression as first order definite clauses. The learning process is insensitive to noisy data thanks to the use of Artificial Neural Networks. The system is tested on two domains.

Machine learning, a branch of artificial intelligence, is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases. Artificial neural networks are composed of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons). Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. In this paper, we made analysis with machine learning algorithms and artificial neural networks classification from instances in data set. Furthermore, machine learning algorithms and artificial neural networks with constituted rules.

Neural Networks, 1999. …, 1999

Neural networks are used for initial determination of linguistic variables and extraction of logical rules from data. Hierarchical sets of rules for different accuracy/rejection tradeoffs are obtained. Sets of logical rules are optimized by maximization of their predictive power. To avoid global minimization methods for crisp logical rules Gaussian uncertainties of inputs are assumed. Analytical formulas reproducing Monte Carlo results for such inputs are derived, leading to a "soft trapezoidal" membership functions instead of rectangular functions used for crisp logical rules. Such approach increases accuracy, gives probabilities of classification instead of the yes/no answers, and allows to optimize sets of rules using gradient procedures. A few illustrative applications to benchmark and real life problems show the effectiveness of this approach.

Expert Systems with Applications, 1991

Rule-based expert systems either develop out of the direct involvement of a concerned expert or through the enormous efforts of intermediaries called knowledge engineers . In either case, knowledge engineering tools are inadequate in many ways to support the complex problem of expert system building. This article describes a set of experiments with adaptive neural networks which explore two types of learning, deductive and inductive, in the context of a rule-based, deterministic parser of Natural Language. Rule-based processing of Language is an important and complex domain. Experiences gained in this domain generalize to other rule-based domains. We report on those experiences and draw some general conclusions that are relevant to knowledge engineering activities and maintenance of rule-based systems.

2000

2019

We propose a novel learning paradigm for Deep Neural Networks (DNN) by using Boolean logic algebra. We first present the basic differentiable operators of a Boolean system such as conjunction, disjunction and exclusive-OR and show how these elementary operators can be combined in a simple and meaningful way to form Neural Logic Networks (NLNs). We examine the effectiveness of the proposed NLN framework in learning Boolean functions and discrete-algorithmic tasks. We demonstrate that, in contrast to the implicit learning in MLP approach, the proposed neural logic networks can learn the logical functions explicitly that can be verified and interpreted by human. In particular, we propose a new framework for learning the inductive logic programming (ILP) problems by exploiting the explicit representational power of NLN. We show the proposed neural ILP solver is capable of feats such as predicate invention and recursion and can outperform the current state of the art neural ILP solvers u...

Neural Processing Letters, 1998

Three neural-based methods for extraction of logical rules from data are presented. These methods facilitate conversion of graded response neural networks into networks performing logical functions. MLP2LN method tries to convert a standard MLP into a network performing logical operations (LN). C-MLP2LN is a constructive algorithm creating such MLP networks. Logical interpretation is assured by adding constraints to the cost function, forcing the weights to ±1 or 0. Skeletal networks emerge ensuring that a minimal number of logical rules are found. In both methods rules covering many training examples are generated before more specific rules covering exceptions. The third method, FSM2LN, is based on the probability density estimation. Several examples of performance of these methods are presented.

IEEE Transactions on Neural Networks, 1997

Envisioning neural networks as systems that learn rules calls forth the verification issues already being studied in knowledge-based systems engineering, and complicates these with neural-network concepts such as nonlinear dynamics and distributed memories. We show that the issues can be clarified and the learned rules visualized symbolically by formalizing the semantics of rule-learning in the mathematical language of two-valued predicate logic. We further show that this can, at least in some cases, be done with a fairly simple logical model. We illustrate this with a combination of two example neuralnetwork architectures, LAPART, designed to learn rules as logical inferences from binary data patterns, and the stack interval network, which converts real-valued data into binary patterns that preserve the semantics of the ordering of real values. We discuss the significance of the formal model in facilitating the analysis of the underlying logic of rule-learning and numerical data representation. We provide examples to illustrate the formal model, with the combined stack interval/LAPART networks extracting rules from numerical data.

The 1st Online Workshop on …, 1996

Simple method for extraction of logical rules from neural networks trained with backpropagation algorithm is presented. Logical interpretation is assured by adding an additional term to the cost function, forcing the weight values to be ±1 or zero. Auxiliary constraint ensures that the training process strives to a network with maximal number of zero weights, which augmented by weight pruning yields a minimal number of logical rules extracted by means of weights analysis. Rules are generated consecutively, from most general, covering many training examples, to most specific, covering a few or even single cases. If there are any exceptions to these rules, they are being detected by additional neurons.

Arxiv preprint arXiv:0804.4071, 2008