532 research outputs found
D4.2 Intelligent D-Band wireless systems and networks initial designs
This deliverable gives the results of the ARIADNE project's Task 4.2: Machine Learning based network intelligence. It presents the work conducted on various aspects of network management to deliver system level, qualitative solutions that leverage diverse machine learning techniques. The different chapters present system level, simulation and algorithmic models based on multi-agent reinforcement learning, deep reinforcement learning, learning automata for complex event forecasting, system level model for proactive handovers and resource allocation, model-driven deep learning-based channel estimation and feedbacks as well as strategies for deployment of machine learning based solutions. In short, the D4.2 provides results on promising AI and ML based methods along with their limitations and potentials that have been investigated in the ARIADNE project
Intelligent systems in manufacturing: current developments and future prospects
Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
Human-like Few-Shot Learning via Bayesian Reasoning over Natural Language
A core tension in models of concept learning is that the model must carefully
balance the tractability of inference against the expressivity of the
hypothesis class. Humans, however, can efficiently learn a broad range of
concepts. We introduce a model of inductive learning that seeks to be
human-like in that sense. It implements a Bayesian reasoning process where a
language model first proposes candidate hypotheses expressed in natural
language, which are then re-weighed by a prior and a likelihood. By estimating
the prior from human data, we can predict human judgments on learning problems
involving numbers and sets, spanning concepts that are generative,
discriminative, propositional, and higher-order.Comment: NeurIPS 2023 ora
Intelligence Primer
This primer explores the exciting subject of intelligence. Intelligence is a
fundamental component of all living things, as well as Artificial
Intelligence(AI). Artificial Intelligence has the potential to affect all of
our lives and a new era for modern humans. This paper is an attempt to explore
the ideas associated with intelligence, and by doing so understand the
implications, constraints, and potentially the capabilities of future
Artificial Intelligence. As an exploration, we journey into different parts of
intelligence that appear essential. We hope that people find this useful in
determining where Artificial Intelligence may be headed. Also, during the
exploration, we hope to create new thought-provoking questions. Intelligence is
not a single weighable quantity but a subject that spans Biology, Physics,
Philosophy, Cognitive Science, Neuroscience, Psychology, and Computer Science.
Historian Yuval Noah Harari pointed out that engineers and scientists in the
future will have to broaden their understandings to include disciplines such as
Psychology, Philosophy, and Ethics. Fiction writers have long portrayed
engineers and scientists as deficient in these areas. Today, modern society,
the emergence of Artificial Intelligence, and legal requirements all act as
forcing functions to push these broader subjects into the foreground. We start
with an introduction to intelligence and move quickly onto more profound
thoughts and ideas. We call this a Life, the Universe and Everything primer,
after the famous science fiction book by Douglas Adams. Forty-two may very well
be the right answer, but what are the questions?Comment: 34 pages, 12 Figure
A Novel Machine Learning Classifier Based on a Qualia Modeling Agent (QMA)
This dissertation addresses a problem found in supervised machine learning (ML) classification, that the target variable, i.e., the variable a classifier predicts, has to be identified before training begins and cannot change during training and testing. This research develops a computational agent, which overcomes this problem. The Qualia Modeling Agent (QMA) is modeled after two cognitive theories: Stanovich\u27s tripartite framework, which proposes learning results from interactions between conscious and unconscious processes; and, the Integrated Information Theory (IIT) of Consciousness, which proposes that the fundamental structural elements of consciousness are qualia. By modeling the informational relationships of qualia, the QMA allows for retaining and reasoning-over data sets in a non-ontological, non-hierarchical qualia space (QS). This novel computational approach supports concept drift, by allowing the target variable to change ad infinitum without re-training while achieving classification accuracy comparable to or greater than benchmark classifiers. Additionally, the research produced a functioning model of Stanovich\u27s framework, and a computationally tractable working solution for a representation of qualia, which when exposed to new examples, is able to match the causal structure and generate new inferences
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