125,083 research outputs found
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models
This paper addresses the general problem of reinforcement learning (RL) in
partially observable environments. In 2013, our large RL recurrent neural
networks (RNNs) learned from scratch to drive simulated cars from
high-dimensional video input. However, real brains are more powerful in many
ways. In particular, they learn a predictive model of their initially unknown
environment, and somehow use it for abstract (e.g., hierarchical) planning and
reasoning. Guided by algorithmic information theory, we describe RNN-based AIs
(RNNAIs) designed to do the same. Such an RNNAI can be trained on never-ending
sequences of tasks, some of them provided by the user, others invented by the
RNNAI itself in a curious, playful fashion, to improve its RNN-based world
model. Unlike our previous model-building RNN-based RL machines dating back to
1990, the RNNAI learns to actively query its model for abstract reasoning and
planning and decision making, essentially "learning to think." The basic ideas
of this report can be applied to many other cases where one RNN-like system
exploits the algorithmic information content of another. They are taken from a
grant proposal submitted in Fall 2014, and also explain concepts such as
"mirror neurons." Experimental results will be described in separate papers.Comment: 36 pages, 1 figure. arXiv admin note: substantial text overlap with
arXiv:1404.782
Supervised learning with artificial hydrocarbon networks: an open source implementation and its applications
Artificial hydrocarbon networks (AHN) is a novel supervised learning method
inspired on the structure and the inner chemical mechanisms of organic
compounds. As any other cutting-edge algorithm, there are two challenges to be
faced: time-consuming for encoding and complications to connect with other
technologies. Large and open source platforms have proved to be an alternative
solution to the latter challenges. In that sense, this paper aims to introduce
the ahnr package for R that implements AHN. It provides several functions to
create, train, test and visualize AHN. It also includes conventional functions
to easily interact with the trained models. For illustration purposes, it
presents several examples about the applications of AHN in engineering, as well
as, the way to use it. This package is intended to be very useful for
scientists and applied researchers interested in machine learning and data
modeling. Package availability is in the Comprehensive R Archive Network.Comment: 13 pages, 8 figure
The role of behavior modifiers in representation development
We address the problem of the development of representations and their
relationship to the environment. We study a software agent which develops in a
network a representation of its simple environment which captures and
integrates the relationships between agent and environment through a closure
mechanism. The inclusion of a variable behavior modifier allows better
representation development. This can be confirmed with an internal description
of the closure mechanism, and with an external description of the properties of
the representation network.Comment: 8 page
The State of the Art Recognize in Arabic Script through Combination of Online and Offline
Handwriting recognition refers to the identification of written characters.
Handwriting recognition has become an acute research area in recent years for
the ease of access of computer science. In this paper primarily discussed
On-line and Off-line handwriting recognition methods for Arabic words which are
often used among then across the Middle East and North Africa People. Arabic
word online handwriting recognition is a very challenging task due to its
cursive nature. Because of the characteristic of the whole body of the Arabic
script, namely connectivity between the characters, thereby the segmentation of
An Arabic script is very difficult. In this paper we introduced an Arabic
script multiple classifier system for recognizing notes written on a Starboard.
This Arabic script multiple classifier system combines one off-line and on-line
handwriting recognition systems. The Arabic script recognizers are all based on
Hidden Markov Models but vary in the way of preprocessing and normalization. To
combine the Arabic script output sequences of the recognizers, we incrementally
align the word sequences using a norm string matching algorithm. The Arabic
script combination we could increase the system performance over the excellent
character recognizer by about 3%. The proposed technique is also the necessary
step towards character recognition, person identification, personality
determination where input data is processed from all perspectives.Comment: Pages 7, Figure 6, Table 2. arXiv admin note: text overlap with
arXiv:1110.1488 by other author
Representation Learning for Dynamic Graphs: A Survey
Graphs arise naturally in many real-world applications including social
networks, recommender systems, ontologies, biology, and computational finance.
Traditionally, machine learning models for graphs have been mostly designed for
static graphs. However, many applications involve evolving graphs. This
introduces important challenges for learning and inference since nodes,
attributes, and edges change over time. In this survey, we review the recent
advances in representation learning for dynamic graphs, including dynamic
knowledge graphs. We describe existing models from an encoder-decoder
perspective, categorize these encoders and decoders based on the techniques
they employ, and analyze the approaches in each category. We also review
several prominent applications and widely used datasets and highlight
directions for future research.Comment: Accepted at JMLR, 73 pages, 2 figure
A Novel Hybrid Crossover based Artificial Bee Colony Algorithm for Optimization Problem
Artificial bee colony (ABC) algorithm has proved its importance in solving a
number of problems including engineering optimization problems. ABC algorithm
is one of the most popular and youngest member of the family of population
based nature inspired meta-heuristic swarm intelligence method. ABC has been
proved its superiority over some other Nature Inspired Algorithms (NIA) when
applied for both benchmark functions and real world problems. The performance
of search process of ABC depends on a random value which tries to balance
exploration and exploitation phase. In order to increase the performance it is
required to balance the exploration of search space and exploitation of optimal
solution of the ABC. This paper outlines a new hybrid of ABC algorithm with
Genetic Algorithm. The proposed method integrates crossover operation from
Genetic Algorithm (GA) with original ABC algorithm. The proposed method is
named as Crossover based ABC (CbABC). The CbABC strengthens the exploitation
phase of ABC as crossover enhances exploration of search space. The CbABC
tested over four standard benchmark functions and a popular continuous
optimization problem
Study Of E-Smooth Support Vector Regression And Comparison With E- Support Vector Regression And Potential Support Vector Machines For Prediction For The Antitubercular Activity Of Oxazolines And Oxazoles Derivatives
A new smoothing method for solving ? -support vector regression (?-SVR),
tolerating a small error in fitting a given data sets nonlinearly is proposed
in this study. Which is a smooth unconstrained optimization reformulation of
the traditional linear programming associated with a ?-insensitive support
vector regression. We term this redeveloped problem as ?-smooth support vector
regression (?-SSVR). The performance and predictive ability of ?-SSVR are
investigated and compared with other methods such as LIBSVM (?-SVR) and P-SVM
methods. In the present study, two Oxazolines and Oxazoles molecular descriptor
data sets were evaluated. We demonstrate the merits of our algorithm in a
series of experiments. Primary experimental results illustrate that our
proposed approach improves the regression performance and the learning
efficiency. In both studied cases, the predictive ability of the ?- SSVR model
is comparable or superior to those obtained by LIBSVM and P-SVM. The results
indicate that ?-SSVR can be used as an alternative powerful modeling method for
regression studies. The experimental results show that the presented algorithm
?-SSVR, plays better precisely and effectively than LIBSVMand P-SVM in
predicting antitubercular activity
A Survey and Critique of Multiagent Deep Reinforcement Learning
Deep reinforcement learning (RL) has achieved outstanding results in recent
years. This has led to a dramatic increase in the number of applications and
methods. Recent works have explored learning beyond single-agent scenarios and
have considered multiagent learning (MAL) scenarios. Initial results report
successes in complex multiagent domains, although there are several challenges
to be addressed. The primary goal of this article is to provide a clear
overview of current multiagent deep reinforcement learning (MDRL) literature.
Additionally, we complement the overview with a broader analysis: (i) we
revisit previous key components, originally presented in MAL and RL, and
highlight how they have been adapted to multiagent deep reinforcement learning
settings. (ii) We provide general guidelines to new practitioners in the area:
describing lessons learned from MDRL works, pointing to recent benchmarks, and
outlining open avenues of research. (iii) We take a more critical tone raising
practical challenges of MDRL (e.g., implementation and computational demands).
We expect this article will help unify and motivate future research to take
advantage of the abundant literature that exists (e.g., RL and MAL) in a joint
effort to promote fruitful research in the multiagent community.Comment: Under review since Oct 2018. Earlier versions of this work had the
title: "Is multiagent deep reinforcement learning the answer or the question?
A brief survey
Architecture for Pseudo Acausal Evolvable Embedded Systems
Advances in semiconductor technology are contributing to the increasing
complexity in the design of embedded systems. Architectures with novel
techniques such as evolvable nature and autonomous behavior have engrossed lot
of attention. This paper demonstrates conceptually evolvable embedded systems
can be characterized basing on acausal nature. It is noted that in acausal
systems, future input needs to be known, here we make a mechanism such that the
system predicts the future inputs and exhibits pseudo acausal nature. An
embedded system that uses theoretical framework of acausality is proposed. Our
method aims at a novel architecture that features the hardware evolability and
autonomous behavior alongside pseudo acausality. Various aspects of this
architecture are discussed in detail along with the limitations.Comment: 4 pages, 2 figures. Submitted to SASO 200
Scope of Research on Particle Swarm Optimization Based Data Clustering
Optimization is nothing but a mathematical technique which finds maxima or
minima of any function of concern in some realistic region. Different
optimization techniques are proposed which are competing for the best solution.
Particle Swarm Optimization (PSO) is a new, advanced, and most powerful
optimization methodology that performs empirically well on several optimization
problems. It is the extensively used Swarm Intelligence (SI) inspired
optimization algorithm used for finding the global optimal solution in a
multifaceted search region. Data clustering is one of the challenging real
world applications that invite the eminent research works in variety of fields.
Applicability of different PSO variants to data clustering is studied in the
literature, and the analyzed research work shows that, PSO variants give poor
results for multidimensional data. This paper describes the different
challenges associated with multidimensional data clustering and scope of
research on optimizing the clustering problems using PSO. We also propose a
strategy to use hybrid PSO variant for clustering multidimensional numerical,
text and image data.Comment: 7 pages, 6 figures, 1 table, published with International Journal of
Computer Science Trends and Technology (IJCST
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