Bayesian Agencies in Control

Bayesian Agencies in Control

Anet Potgieter (University of Pretoria, South Africa) and Judith Bishop (University of Pretoria, South Africa)
Copyright: © 2003 |Pages: 14
DOI: 10.4018/978-1-59140-037-0.ch010
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Most agent architectures implement autonomous agents that use extensive interaction protocols and social laws to control interactions in order to ensure that the correct behaviors result during run-time. These agents, organized into multi-agent systems in which all agents adhere to predefined interaction protocols, are well suited to the analysis, design and implementation of complex systems in environments where it is possible to predict interactions during the analysis and design phases. In these multi-agent systems, intelligence resides in individual autonomous agents, rather than in the collective behavior of the individual agents. These agents are commonly referred to as “next-generation” or intelligent components, which are difficult to implement using current component-based architectures. In most distributed environments, such as the Internet, it is not possible to predict interactions during analysis and design. For a complex system to be able to adapt in such an uncertain and non-deterministic environment, we propose the use of agencies, consisting of simple agents, which use probabilistic reasoning to adapt to their environment. Our agents collectively implement distributed Bayesian networks, used by the agencies to control behaviors in response to environmental states. Each agency is responsible for one or more behaviors, and the agencies are structured into heterarchies according to the topology of the underlying Bayesian networks. We refer to our agents and agencies as “Bayesian agents” and “Bayesian agencies.”

Complete Chapter List

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Table of Contents
Masoud Mohammadian, Rahul A. Sarker, Xin Yao
Chapter 1
Yong Liu, Xin Yao, Tetsuya Higuchi
This chapter describes negative correlation learning for designing neural network ensembles. Negative correlation learning has been firstly analysed... Sample PDF
Designing Neural Network Ensembles by Minimizing Mutual Information
Chapter 2
C. Alippi
This chapter presents a general methodology for evaluating the loss in performance of a generic neural network once its weights are affected by... Sample PDF
A Perturbation Size-Independent Analysis of Robustness in Neural Networks by Randomized Algorithms
Chapter 3
T. G.B. Amaral, M. M. Crisostomo, V. Fernao Pires
This chapter describes the application of a general regression neural network (GRNN) to control the flight of a helicopter. This GRNN is an adaptive... Sample PDF
Helicopter Motion Control Using a General Regression Neural Network
Chapter 4
Simon X. Yang
A novel biologically inspired neural network approach is proposed for real-time simultaneous map building and path planning with limited sensor... Sample PDF
A Biologically Inspired Neural Network Approach to Real-Time Map Building and Path Planning
Chapter 5
P. J. Thomas, R. J. Stonier
In this chapter an evolutionary algorithm is developed to learn a fuzzy knowledge base for the control of a soccer micro-robot from any... Sample PDF
Evolutionary Learning of Fuzzy Control in Robot-Soccer
Chapter 6
Pieter Spronck, Ida Sprinkhuizen-Kuyper, Eric Postma, Rens Kortmann
In our research we use evolutionary algorithms to evolve robot controllers for executing elementary behaviours. This chapter focuses on the... Sample PDF
Evolutionary Learning of a Box-Pushing Controller
Chapter 7
M. Mohammadian
With increased application of fuzzy logic in complex control systems, there is a need for a structured methodological approach in the development of... Sample PDF
Computational Intelligence for Modelling and Control of Multi-Robot Systems
Chapter 8
D. C. Panni, A. D. Nurse
A general method for integrating genetic algorithms within a commercially available finite element (FE) package to solve a range of structural... Sample PDF
Integrating Genetic Algorithms and the Finite Element Analysis for Structural Inverse Problems
Chapter 9
M. Gestwa, J.-M. Bauschat
This chapter discusses the possibility to model the control behaviour of a human pilot by fuzzy logic control. For this investigation a special... Sample PDF
On the Modelling of a Human Pilot Using Fuzzy Logic Control
Chapter 10
Anet Potgieter, Judith Bishop
Most agent architectures implement autonomous agents that use extensive interaction protocols and social laws to control interactions in order to... Sample PDF
Bayesian Agencies in Control
Chapter 11
Hongfei Gong, A.=gostinho Claudio da Rosa
In this chapter we present a novel method for modelling of the development of olive fly—Bactrocera oleae (Gmelin)—based on artificial life... Sample PDF
Simulation Model for the Control of Olive Fly Bactrocera Oleae Using Artificial Life Technique
Chapter 12
D. P. Solomatine
Traditionally, management and control of water resources is based on behavior-driven or physically based models based on equations describing the... Sample PDF
Applications of Data-Driven Modelling and Machine Learning in Control of Water Resources
Chapter 13
Ruhul A. Sarker, Hussein A. Abbass, Charles S. Newton
Being capable of finding a set of pareto-optimal solutions in a single run is a necessary feature for multi-criteria decision making, Evolutionary... Sample PDF
Solving Two Multi-Objective Optimization Problems Using Evolutionary Algorithm
Chapter 14
Imed Kacem, Slim Hammadi, Pierre Borne
The Job-shop Scheduling Problem (JSP) is one of hardest problems; it is classified NP-complete (Carlier & Chretienne, 1988; Garey & Johnson, 1979).... Sample PDF
Flexible Job-Shop Scheduling Problems: Formulation, Lower Bounds, Encoding and Controlled Evolutionary Approach
Chapter 15
Yoshiyuki Matsumura, Kazuhiro Ohkura, Kanji Ueda
In this chapter we apply (m / m, l)-ES to noisy test functions, in order to investigate the effect of multi-parent versions of both intermediate... Sample PDF
The Effect of Multi-Parent Recombination on Evolution Strategies for Noisy Objective Functions
Chapter 16
J.-L. Fernandez-Villacanas Martin, P. Marrow, M. Shackleton
In this chapter we compare the performance of two contrasting evolutionary algorithms addressing a similar problem, of information retrieval. The... Sample PDF
On Measuring the Attributes of Evolutionary Algorithms: A Comparison of Algorithms Used for Information Retrieval
Chapter 17
Z. Ismail, N. H. Ramli, Z. Ibrahim, T. A. Majid, G. Sundaraj, W. H.W. Badaruzzaman
In this chapter, a study on the effects of transforming wind speed data, from a time series domain into a frequency domain via Fast Fourier... Sample PDF
Design Wind Speeds Using Fast Fourier Transform: A Case Study
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