11,423 research outputs found
Evolutionary Networks for Multi-Behavioural Robot Control : A thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Science Massey University, Albany, New Zealand
Artificial Intelligence can be applied to a wide variety of real world problems, with
varying levels of complexity; nonetheless, real world problems often demand for
capabilities that are difficult, if not impossible to achieve using a single Artificial
Intelligence algorithm. This challenge gave rise to the development of hybrid systems
that put together a combination of complementary algorithms. Hybrid approaches
come at a cost however, as they introduce additional complications for the developer,
such as how the algorithms should interact and when the independent algorithms
should be executed. This research introduces a new algorithm called Cascading
Genetic Network Programming (CGNP), which contains significant changes to the
original Genetic Network Programming. This new algorithm has the facility to
include any Artificial Intelligence algorithm into its directed graph network, as either
a judgement or processing node. CGNP introduces a novel ability for a scalable
multiple layer network, of independent instances of the CGNP algorithm itself. This
facilitates problem subdivision, independent optimisation of these underlying layers
and the ability to develop varying levels of complexity, from individual motor control
to high level dynamic role allocation systems. Mechanisms are incorporated to
prevent the child networks from executing beyond their requirement, allowing the
parent to maintain control. The ability to optimise any data within each node
is added, allowing for general purpose node development and therefore allowing
node reuse in a wide variety of applications without modification. The abilities
of the Cascaded Genetic Network Programming algorithm are demonstrated and
proved through the development of a multi-behavioural robot soccer goal keeper, as
a testbed where an individual Artificial Intelligence system may not be sufficient.
The overall role is subdivided into three components and individually optimised
which allow the robot to pursue a target object or location, rotate towards a target
and provide basic functionality for defending a goal. These three components are
then used in a higher level network as independent nodes, to solve the overall multi-
behavioural goal keeper. Experiments show that the resulting controller defends the
goal with a success rate of 91%, after 12 hours training using a population of 400
and 60 generations
Adaptive Process Control with Fuzzy Logic and Genetic Algorithms
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision-making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented
Adaptive process control using fuzzy logic and genetic algorithms
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented
Evolution engine technology in exhaust gas recirculation for heavy-duty diesel engine
In this present year, engineers have been researching and inventing to get the optimum of less emission in every vehicle for a better environmental friendly. Diesel engines are known reusing of the exhaust gas in order to reduce the exhaust emissions such as NOx that contribute high factors in the pollution. In this paper, we have conducted a study that EGR instalment in the vehicle can be good as it helps to prevent highly amount of toxic gas formation, which NOx level can be lowered. But applying the EGR it can lead to more cooling and more space which will affect in terms of the costing. Throughout the research, fuelling in the engine affects the EGR producing less emission. Other than that, it contributes to the less of performance efficiency when vehicle load is less
Penguasaan kemahiran generik di kalangan graduan hospitaliti di politeknik : satu kajian berkenaan keperluan industri perhotelan, persepsi pensyarah dan pelajar
Kajian yang dijalankan ini bertujuan untuk mengenal pasti kepentingan
kemahiran generik mengikut keperluan industri perhotelan di Malaysia dengan persepsi pensyarah dan persepsi pelajar Jabatan Hospitaliti. Oleh kerana matlamat kurikulum pendidikan kini adalah untuk melahirkan graduan yang dapat memenuhi keperluan pihak industri, maka kajian ini dijalankan untuk menilai hubungan di antara keperluan industri perhotelan di Malaysia dengan persepsi pensyarah dan pelajar Jabatan Hospitaliti di Politeknik. Terdapat 13 kemahiran generik yang diperolehi daripada Kementerian Pelajaran dan Latihan Ontario (1997) dijadikan
sebagai skop kepada instrumen kajian. Responden kajian terdiri daripada tiga pihak utama iaitu industri perhotelan di Malaysia yang melibatkan 40 buah hotel yang diwakili oleh MAH Chapter dan jawatankuasa dalam Malaysian Associated of Hotel (MAH), pensyarah Unit Hotel dan Katering dan pelajar semester akhir Diploma Hotel dan Katering di Politeknik Johor Bahru, Johor dan Politeknik Merlimau, Melaka. Kajian rintis yang dijalankan menunjukkan nilai Alpha Cronbach pada 0.8781. Data yang diperolehi dianalisis secara deskriptif dan inferensi dengan menggunakan perisian Statistical Package for Social Science (SPSS) versi 11.5. Melalui dapatan kajian, satu senarai berkenaan kemahiran generik yang diperlukan
oleh industri perhotelan telah dapat dihasilkan. Selain itu, senarai kemahiran generik menurut persepsi pensyarah dan juga persepsi pelajar turut dihasilkan. Hasil statistik dan graf garis yang diperolehi menunjukkan terdapat perbezaan di antara kemahiran generik yang diperlukan oleh industri perhotelan di Malaysia dengan kemahiran generik menurut persepsi pensyarah dan persepsi pelajar Politeknik. Dapatan analisis menggunakan korelasi Pearson mendapati bahawa tidak terdapat
perhubungan yang signifikan di antara kemahiran generik yang diperlukan oleh industri perhotelan dengan persepsi pensyarah dan persepsi pelajar. Namun begitu, terdapat hubungan yang signifikan di antara persepsi pensyarah dengan persepsi pelajar berkenaan dengan amalan kemahiran generik di Politeknik
AI and OR in management of operations: history and trends
The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested
A genetic algorithm for the design of a fuzzy controller for active queue management
Active queue management (AQM) policies are those
policies of router queue management that allow for the detection of network congestion, the notification of such occurrences to the
hosts on the network borders, and the adoption of a suitable control
policy. This paper proposes the adoption of a fuzzy proportional
integral (FPI) controller as an active queue manager for Internet
routers. The analytical design of the proposed FPI controller is
carried out in analogy with a proportional integral (PI) controller,
which recently has been proposed for AQM. A genetic algorithm is
proposed for tuning of the FPI controller parameters with respect
to optimal disturbance rejection. In the paper the FPI controller
design metodology is described and the results of the comparison
with random early detection (RED), tail drop, and PI controller
are presented
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