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Learning and Representing Temporal Knowledge in Recurrent Networks
The effective integration of knowledge representation, reasoning, and learning in a robust computational model is one of the key challenges of computer science and artificial intelligence. In particular, temporal knowledge and models have been fundamental in describing the behavior of computational systems. However, knowledge acquisition of correct descriptions of a system's desired behavior is a complex task. In this paper, we present a novel neural-computation model capable of representing and learning temporal knowledge in recurrent networks. The model works in an integrated fashion. It enables the effective representation of temporal knowledge, the adaptation of temporal models given a set of desirable system properties, and effective learning from examples, which in turn can lead to temporal knowledge extraction from the corresponding trained networks. The model is sound from a theoretical standpoint, but it has also been tested on a case study in the area of model verification and adaptation. The results contained in this paper indicate that model verification and learning can be integrated within the neural computation paradigm, contributing to the development of predictive temporal knowledge-based systems and offering interpretable results that allow system researchers and engineers to improve their models and specifications. The model has been implemented and is available as part of a neural-symbolic computational toolkit
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A neural-symbolic system for temporal reasoning with application to model verification and learning
The effective integration of knowledge representation, reasoning and learning into a robust computational model is one of the key challenges in Computer Science and Artificial Intelligence. In particular, temporal models have been fundamental in describing the behaviour of Computational and Neural-Symbolic Systems. Furthermore, knowledge acquisition of correct descriptions of the desired system’s behaviour is a complex task in several domains. Several efforts have been directed towards the development of tools that are capable of learning, describing and evolving software models.
This thesis contributes to two major areas of Computer Science, namely Artificial Intelligence (AI) and Software Engineering. Under an AI perspective, we present a novel neural-symbolic computational model capable of representing and learning temporal knowledge in recurrent networks. The model works in integrated fashion. It enables the effective representation of temporal knowledge, the adaptation of temporal models to a set of desirable system properties and effective learning from examples, which in turn can lead to symbolic temporal knowledge extraction from the corresponding trained neural networks. The model is sound, from a theoretical standpoint, but is also tested in a number of case studies.
An extension to the framework is shown to tackle aspects of verification and adaptation under the SE perspective. As regards verification, we make use of established techniques for model checking, which allow the verification of properties described as temporal models and return counter-examples whenever the properties are not satisfied. Our neural-symbolic framework is then extended to deal with different sources of information. This includes the translation of model descriptions into the neural structure, the evolution of such descriptions by the application of learning of counter examples, and also the learning of new models from simple observation of their behaviour.
In summary, we believe the thesis describes a principled methodology for temporal knowledge representation, learning and extraction, shedding new light on predictive temporal models, not only from a theoretical standpoint, but also with respect to a potentially large number of applications in AI, Neural Computation and Software Engineering, where temporal knowledge plays a fundamental role
Dimensions of Neural-symbolic Integration - A Structured Survey
Research on integrated neural-symbolic systems has made significant progress
in the recent past. In particular the understanding of ways to deal with
symbolic knowledge within connectionist systems (also called artificial neural
networks) has reached a critical mass which enables the community to strive for
applicable implementations and use cases. Recent work has covered a great
variety of logics used in artificial intelligence and provides a multitude of
techniques for dealing with them within the context of artificial neural
networks. We present a comprehensive survey of the field of neural-symbolic
integration, including a new classification of system according to their
architectures and abilities.Comment: 28 page
Extracting finite structure from infinite language
This paper presents a novel connectionist memory-rule based model capable of learning the finite-state properties of an input language from a set of positive examples. The model is based upon an unsupervised recurrent self-organizing map [T. McQueen, A. Hopgood, J. Tepper, T. Allen, A recurrent self-organizing map for temporal sequence processing, in: Proceedings of Fourth International Conference in Recent Advances in Soft Computing (RASC2002), Nottingham, 2002] with laterally interconnected neurons. A derivation of functionalequivalence theory [J. Hopcroft, J. Ullman, Introduction to Automata Theory, Languages and Computation, vol. 1, Addison-Wesley, Reading, MA, 1979] is used that allows the model to exploit similarities between the future context of previously memorized sequences and the future context of the current input sequence. This bottom-up learning algorithm binds functionally related neurons together to form states. Results show that the model is able to learn the Reber grammar [A. Cleeremans, D. Schreiber, J. McClelland, Finite state automata and simple recurrent networks, Neural Computation, 1 (1989) 372–381] perfectly from a randomly generated training set and to generalize to sequences beyond the length of those found in the training set
Self-organization of action hierarchy and compositionality by reinforcement learning with recurrent neural networks
Recurrent neural networks (RNNs) for reinforcement learning (RL) have shown
distinct advantages, e.g., solving memory-dependent tasks and meta-learning.
However, little effort has been spent on improving RNN architectures and on
understanding the underlying neural mechanisms for performance gain. In this
paper, we propose a novel, multiple-timescale, stochastic RNN for RL. Empirical
results show that the network can autonomously learn to abstract sub-goals and
can self-develop an action hierarchy using internal dynamics in a challenging
continuous control task. Furthermore, we show that the self-developed
compositionality of the network enhances faster re-learning when adapting to a
new task that is a re-composition of previously learned sub-goals, than when
starting from scratch. We also found that improved performance can be achieved
when neural activities are subject to stochastic rather than deterministic
dynamics
Adaptive Learning Method of Recurrent Temporal Deep Belief Network to Analyze Time Series Data
Deep Learning has the hierarchical network architecture to represent the
complicated features of input patterns. Such architecture is well known to
represent higher learning capability compared with some conventional models if
the best set of parameters in the optimal network structure is found. We have
been developing the adaptive learning method that can discover the optimal
network structure in Deep Belief Network (DBN). The learning method can
construct the network structure with the optimal number of hidden neurons in
each Restricted Boltzmann Machine and with the optimal number of layers in the
DBN during learning phase. The network structure of the learning method can be
self-organized according to given input patterns of big data set. In this
paper, we embed the adaptive learning method into the recurrent temporal RBM
and the self-generated layer into DBN. In order to verify the effectiveness of
our proposed method, the experimental results are higher classification
capability than the conventional methods in this paper.Comment: 8 pages, 9 figures. arXiv admin note: text overlap with
arXiv:1807.03487, arXiv:1807.0348
Diffusion of Context and Credit Information in Markovian Models
This paper studies the problem of ergodicity of transition probability
matrices in Markovian models, such as hidden Markov models (HMMs), and how it
makes very difficult the task of learning to represent long-term context for
sequential data. This phenomenon hurts the forward propagation of long-term
context information, as well as learning a hidden state representation to
represent long-term context, which depends on propagating credit information
backwards in time. Using results from Markov chain theory, we show that this
problem of diffusion of context and credit is reduced when the transition
probabilities approach 0 or 1, i.e., the transition probability matrices are
sparse and the model essentially deterministic. The results found in this paper
apply to learning approaches based on continuous optimization, such as gradient
descent and the Baum-Welch algorithm.Comment: See http://www.jair.org/ for any accompanying file
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