Lakhmi C. Jain

Bio: Lakhmi C. Jain is an academic researcher from University of Technology, Sydney. The author has contributed to research in topics: Artificial neural network & Intelligent decision support system. The author has an hindex of 41, co-authored 419 publications receiving 10015 citations. Previous affiliations of Lakhmi C. Jain include University of South Australia & University of Canberra.

The Evolution of Intelligent Agents within the World Wide Web

Abstract: The definition of an agent still needs to be agreed upon [1] and the use of multiple agents to form a team is being examined by many researchers [2]. The study of Artificial Intelligence (AI) is diverse because of each domain has encountered a bottleneck or some impasse has forced research to look further a field to find solutions [3]. Agent teaming was one of those choices. Each team consists of one or more agent which form a Multi-Agent System (MAS) [4]. Currently these have a fixed hierarchy and predetermined functionality to achieve specified goals [5]. Ideally that teams should seamlessly interoperate within its environment, autonomously adapt to new tasks and rapidly switch context as required. Learning, cooperation, collaboration and trust are other characteristics that deserve discussion and development, however, the above challenge would represent a significant leap in the natural progression to agent oriented programming.

Smart Energy Control Systems for Sustainable Buildings

Abstract: Item not available on this repository - can be purchased at http://www.springer.com/gb/book/9783319520742

A fast visual search and recognition mechanism for real-time robotics applications

Abstract: Robot navigation relies on a robust and real-time visual perception system to understand the surrounding environment This paper describes a fast visual landmark search and recognition mechanism for real-time robotics applications The mechanism models two stages of visual perception named preattentive and attentive stages The pre-attentive stage provides a global guided search by identifying regions of interest, which is followed by the attentive stage for landmark recognition The results show the mechanism validity and applicability to autonomous robot applications.

Convergence analysis on approximate reinforcement learning

Abstract: Temporal difference (TD) learning is a form of approximate reinforcement learning using an incremental learning updates. For large, stochastic and dynamic systems, however, it is still on open question for lacking the methodology to analyse the convergence and sensitivity of TD algorithms. Meanwhile, analysis on convergence and sensitivity of parameters are very expensive, such analysis metrics are obtained only by running an experiment with different parameter values. In this paper, we utilise the TD(λ) learning control algorithm with a linear function approximation technique known as tile coding in order to help soccer agent learn the optimal control processes. The aim of this paper is to propose a methodology for analysing the performance for adaptively selecting a set of optimal parameter values in TD(λ) learning algorithm.

Fuzzy Logic and Neuro-Fuzzy Systems in Medicine and Bio-Medical Engineering : A Historical Perspective

Abstract: This chapter reviews the applications of fuzzy and neuro-fuzzy systems technology in medicine and biology from a historical perspective. In addition to the presentation of the first bibliography of the early years, an analysis of past evolution and the current development of the field and its perspectives is presented. Fuzzy logic has provided a basis for representing uncertain and imprecise knowledge and formed a basis for human reasoning representation in medical and biomedical engineering applications. The tremendous advances in the theory of artificial neural networks and fuzzy logic have resulted in the flourishing applications of these techniques in medicine and biomedical engineering. Indeed, fuzzy logic techniques have been successfully used in a number of applications including medicine and bio-medical engineering. The domain of neural networks is as much a section of the fields of theoretical biology, neurology, and behavioural science, as it is a domain of automata theory and of engineering in general.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

Abstract: How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.