31,056 research outputs found
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
The study and understanding of human behaviour is relevant to computer
science, artificial intelligence, neural computation, cognitive science,
philosophy, psychology, and several other areas. Presupposing cognition as
basis of behaviour, among the most prominent tools in the modelling of
behaviour are computational-logic systems, connectionist models of cognition,
and models of uncertainty. Recent studies in cognitive science, artificial
intelligence, and psychology have produced a number of cognitive models of
reasoning, learning, and language that are underpinned by computation. In
addition, efforts in computer science research have led to the development of
cognitive computational systems integrating machine learning and automated
reasoning. Such systems have shown promise in a range of applications,
including computational biology, fault diagnosis, training and assessment in
simulators, and software verification. This joint survey reviews the personal
ideas and views of several researchers on neural-symbolic learning and
reasoning. The article is organised in three parts: Firstly, we frame the scope
and goals of neural-symbolic computation and have a look at the theoretical
foundations. We then proceed to describe the realisations of neural-symbolic
computation, systems, and applications. Finally we present the challenges
facing the area and avenues for further research.Comment: 58 pages, work in progres
Machine learning and its applications in reliability analysis systems
In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA
The 30-Year Cycle In The AI Debate
In the last couple of years, the rise of Artificial Intelligence and the
successes of academic breakthroughs in the field have been inescapable. Vast
sums of money have been thrown at AI start-ups. Many existing tech companies --
including the giants like Google, Amazon, Facebook, and Microsoft -- have
opened new research labs. The rapid changes in these everyday work and
entertainment tools have fueled a rising interest in the underlying technology
itself; journalists write about AI tirelessly, and companies -- of tech nature
or not -- brand themselves with AI, Machine Learning or Deep Learning whenever
they get a chance. Confronting squarely this media coverage, several analysts
are starting to voice concerns about over-interpretation of AI's blazing
successes and the sometimes poor public reporting on the topic. This paper
reviews briefly the track-record in AI and Machine Learning and finds this
pattern of early dramatic successes, followed by philosophical critique and
unexpected difficulties, if not downright stagnation, returning almost to the
clock in 30-year cycles since 1958.Comment: 31 pages, 5 table
How Case Based Reasoning Explained Neural Networks: An XAI Survey of Post-Hoc Explanation-by-Example in ANN-CBR Twins
This paper surveys an approach to the XAI problem, using post-hoc explanation
by example, that hinges on twinning Artificial Neural Networks (ANNs) with
Case-Based Reasoning (CBR) systems, so-called ANN-CBR twins. A systematic
survey of 1100+ papers was carried out to identify the fragmented literature on
this topic and to trace it influence through to more recent work involving Deep
Neural Networks (DNNs). The paper argues that this twin-system approach,
especially using ANN-CBR twins, presents one possible coherent, generic
solution to the XAI problem (and, indeed, XCBR problem). The paper concludes by
road-mapping some future directions for this XAI solution involving (i) further
tests of feature-weighting techniques, (iii) explorations of how explanatory
cases might best be deployed (e.g., in counterfactuals, near-miss cases, a
fortori cases), and (iii) the raising of the unwelcome and, much ignored, issue
of human user evaluation.Comment: 15 page
Artificial Intelligence and its Role in Near Future
AI technology has a long history which is actively and constantly changing
and growing. It focuses on intelligent agents, which contain devices that
perceive the environment and based on which takes actions in order to maximize
goal success chances. In this paper, we will explain the modern AI basics and
various representative applications of AI. In the context of the modern
digitalized world, AI is the property of machines, computer programs, and
systems to perform the intellectual and creative functions of a person,
independently find ways to solve problems, be able to draw conclusions and make
decisions. Most artificial intelligence systems have the ability to learn,
which allows people to improve their performance over time. The recent research
on AI tools, including machine learning, deep learning and predictive analysis
intended toward increasing the planning, learning, reasoning, thinking and
action taking ability. Based on which, the proposed research intends towards
exploring on how the human intelligence differs from the artificial
intelligence. Moreover, we critically analyze what AI of today is capable of
doing, why it still cannot reach human intelligence and what are the open
challenges existing in front of AI to reach and outperform human level of
intelligence. Furthermore, it will explore the future predictions for
artificial intelligence and based on which potential solution will be
recommended to solve it within next decades
Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework
In this paper, we argue that the future of Artificial Intelligence research
resides in two keywords: integration and embodiment. We support this claim by
analyzing the recent advances of the field. Regarding integration, we note that
the most impactful recent contributions have been made possible through the
integration of recent Machine Learning methods (based in particular on Deep
Learning and Recurrent Neural Networks) with more traditional ones (e.g.
Monte-Carlo tree search, goal babbling exploration or addressable memory
systems). Regarding embodiment, we note that the traditional benchmark tasks
(e.g. visual classification or board games) are becoming obsolete as
state-of-the-art learning algorithms approach or even surpass human performance
in most of them, having recently encouraged the development of first-person 3D
game platforms embedding realistic physics. Building upon this analysis, we
first propose an embodied cognitive architecture integrating heterogenous
sub-fields of Artificial Intelligence into a unified framework. We demonstrate
the utility of our approach by showing how major contributions of the field can
be expressed within the proposed framework. We then claim that benchmarking
environments need to reproduce ecologically-valid conditions for bootstrapping
the acquisition of increasingly complex cognitive skills through the concept of
a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017
conference (Lisbon, Portugal
From Images to Sentences through Scene Description Graphs using Commonsense Reasoning and Knowledge
In this paper we propose the construction of linguistic descriptions of
images. This is achieved through the extraction of scene description graphs
(SDGs) from visual scenes using an automatically constructed knowledge base.
SDGs are constructed using both vision and reasoning. Specifically, commonsense
reasoning is applied on (a) detections obtained from existing perception
methods on given images, (b) a "commonsense" knowledge base constructed using
natural language processing of image annotations and (c) lexical ontological
knowledge from resources such as WordNet. Amazon Mechanical Turk(AMT)-based
evaluations on Flickr8k, Flickr30k and MS-COCO datasets show that in most
cases, sentences auto-constructed from SDGs obtained by our method give a more
relevant and thorough description of an image than a recent state-of-the-art
image caption based approach. Our Image-Sentence Alignment Evaluation results
are also comparable to that of the recent state-of-the art approaches
Artificial Intelligence in the Context of Human Consciousness
Artificial intelligence (AI) can be defined as the ability of a machine to learn and make decisions based on acquired information. AI’s development has incited rampant public speculation regarding the singularity theory: a futuristic phase in which intelligent machines are capable of creating increasingly intelligent systems. Its implications, combined with the close relationship between humanity and their machines, make achieving understanding both natural and artificial intelligence imperative. Researchers are continuing to discover natural processes responsible for essential human skills like decision-making, understanding language, and performing multiple processes simultaneously. Artificial intelligence attempts to simulate these functions through techniques like artificial neural networks, Markov Decision Processes, Human Language Technology, and Multi-Agent Systems, which rely upon a combination of mathematical models and hardware
A Transfer Learning Method for Goal Recognition Exploiting Cross-Domain Spatial Features
The ability to infer the intentions of others, predict their goals, and
deduce their plans are critical features for intelligent agents. For a long
time, several approaches investigated the use of symbolic representations and
inferences with limited success, principally because it is difficult to capture
the cognitive knowledge behind human decisions explicitly. The trend, nowadays,
is increasingly focusing on learning to infer intentions directly from data,
using deep learning in particular. We are now observing interesting
applications of intent classification in natural language processing, visual
activity recognition, and emerging approaches in other domains. This paper
discusses a novel approach combining few-shot and transfer learning with
cross-domain features, to learn to infer the intent of an agent navigating in
physical environments, executing arbitrary long sequences of actions to achieve
their goals. Experiments in synthetic environments demonstrate improved
performance in terms of learning from few samples and generalizing to unseen
configurations, compared to a deep-learning baseline approach
STAR-RT: Visual attention for real-time video game playing
In this paper we present STAR-RT - the first working prototype of Selective
Tuning Attention Reference (STAR) model and Cognitive Programs (CPs). The
Selective Tuning (ST) model received substantial support through psychological
and neurophysiological experiments. The STAR framework expands ST and applies
it to practical visual tasks. In order to do so, similarly to many cognitive
architectures, STAR combines the visual hierarchy (based on ST) with the
executive controller, working and short-term memory components and fixation
controller. CPs in turn enable the communication among all these elements for
visual task execution. To test the relevance of the system in a realistic
context, we implemented the necessary components of STAR and designed CPs for
playing two closed-source video games - Canabaltand Robot Unicorn Attack. Since
both games run in a browser window, our algorithm has the same amount of
information and the same amount of time to react to the events on the screen as
a human player would. STAR-RT plays both games in real time using only visual
input and achieves scores comparable to human expert players. It thus provides
an existence proof for the utility of the particular CP structure and
primitives used and the potential for continued experimentation and
verification of their utility in broader scenarios.Comment: 21 page, 13 figure
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