1,632 research outputs found
First-order logic learning in artificial neural networks
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. 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 system is tested on three examples and the results are discussed
Error management in ATLAS TDAQ : an intelligent systems approach
This thesis is concerned with the use of intelligent system techniques (IST) within
a large distributed software system, specifically the ATLAS TDAQ system which
has been developed and is currently in use at the European Laboratory for Particle
Physics(CERN). The overall aim is to investigate and evaluate a range of ITS
techniques in order to improve the error management system (EMS) currently used
within the TDAQ system via error detection and classification. The thesis work
will provide a reference for future research and development of such methods in the
TDAQ system.
The thesis begins by describing the TDAQ system and the existing EMS, with a
focus on the underlying expert system approach, in order to identify areas where
improvements can be made using IST techniques. It then discusses measures of
evaluating error detection and classification techniques and the factors specific to
the TDAQ system.
Error conditions are then simulated in a controlled manner using an experimental
setup and datasets were gathered from two different sources. Analysis and processing
of the datasets using statistical and ITS techniques shows that clusters exists in
the data corresponding to the different simulated errors.
Different ITS techniques are applied to the gathered datasets in order to realise an
error detection model. These techniques include Artificial Neural Networks (ANNs),
Support Vector Machines (SVMs) and Cartesian Genetic Programming (CGP) and
a comparison of the respective advantages and disadvantages is made.
The principle conclusions from this work are that IST can be successfully used to
detect errors in the ATLAS TDAQ system and thus can provide a tool to improve
the overall error management system. It is of particular importance that the IST can
be used without having a detailed knowledge of the system, as the ATLAS TDAQ
is too complex for a single person to have complete understanding of. The results
of this research will benefit researchers developing and evaluating IST techniques in
similar large scale distributed systems
Quantum machine learning: a classical perspective
Recently, increased computational power and data availability, as well as
algorithmic advances, have led machine learning techniques to impressive
results in regression, classification, data-generation and reinforcement
learning tasks. Despite these successes, the proximity to the physical limits
of chip fabrication alongside the increasing size of datasets are motivating a
growing number of researchers to explore the possibility of harnessing the
power of quantum computation to speed-up classical machine learning algorithms.
Here we review the literature in quantum machine learning and discuss
perspectives for a mixed readership of classical machine learning and quantum
computation experts. Particular emphasis will be placed on clarifying the
limitations of quantum algorithms, how they compare with their best classical
counterparts and why quantum resources are expected to provide advantages for
learning problems. Learning in the presence of noise and certain
computationally hard problems in machine learning are identified as promising
directions for the field. Practical questions, like how to upload classical
data into quantum form, will also be addressed.Comment: v3 33 pages; typos corrected and references adde
Application of Analogical Reasoning for Use in Visual Knowledge Extraction
There is a continual push to make Artificial Intelligence (AI) as human-like as possible; however, this is a difficult task because of its inability to learn beyond its current comprehension. Analogical reasoning (AR) has been proposed as one method to achieve this goal. Current literature lacks a technical comparison on psychologically-inspired and natural-language-processing-produced AR algorithms with consistent metrics on multiple-choice word-based analogy problems. Assessment is based on “correctness” and “goodness” metrics. There is not a one-size-fits-all algorithm for all textual problems. As contribution in visual AR, a convolutional neural network (CNN) is integrated with the AR vector space model, Global Vectors (GloVe), in the proposed, Image Recognition Through Analogical Reasoning Algorithm (IRTARA). Given images outside of the CNN’s training data, IRTARA produces contextual information by leveraging semantic information from GloVe. IRTARA’s quality of results is measured by definition, AR, and human factors evaluation methods, which saw consistency at the extreme ends. The research shows the potential for AR to facilitate more a human-like AI through its ability to understand concepts beyond its foundational knowledge in both a textual and visual problem space
- …