159 research outputs found
Artificial societies and information theory: modelling of sub system formation based on Luhmann's autopoietic theory
This thesis develops a theoretical framework for the generation of artificial societies. In particular
it shows how sub-systems emerge when the agents are able to learn and have the ability
to communicate.
This novel theoretical framework integrates the autopoietic hypothesis of human societies, formulated
originally by the German sociologist Luhmann, with concepts of Shannon's information
theory applied to adaptive learning agents.
Simulations were executed using Multi-Agent-Based Modelling (ABM), a relatively new computational
modelling paradigm involving the modelling of phenomena as dynamical systems of
interacting agents. The thesis in particular, investigates the functions and properties necessary
to reproduce the paradigm of society by using the mentioned ABM approach.
Luhmann has proposed that in society subsystems are formed to reduce uncertainty. Subsystems
can then be composed by agents with a reduced behavioural complexity. For example in
society there are people who produce goods and other who distribute them.
Both the behaviour and communication is learned by the agent and not imposed. The simulated
task is to collect food, keep it and eat it until sated. Every agent communicates its energy state
to the neighbouring agents. This results in two subsystems whereas agents in the first collect
food and in the latter steal food from others. The ratio between the number of agents that
belongs to the first system and to the second system, depends on the number of food resources.
Simulations are in accordance with Luhmann, who suggested that adaptive agents self-organise
by reducing the amount of sensory information or, equivalently, reducing the complexity of the
perceived environment from the agent's perspective. Shannon's information theorem is used
to assess the performance of the simulated learning agents. A practical measure, based on the
concept of Shannon's information
ow, is developed and applied to adaptive controllers which
use Hebbian learning, input correlation learning (ICO/ISO) and temporal difference learning.
The behavioural complexity is measured with a novel information measure, called Predictive
Performance, which is able to measure at a subjective level how good an agent is performing
a task. This is then used to quantify the social division of tasks in a social group of honest,
cooperative food foraging, communicating agents
An SDP approach for l0-minimization: application to ARX model segmentation
Abstract Minimizing the β 0 -seminorm of a vector under convex constraints is a combinatorial (NP-hard) problem. Replacement of the β 0 -seminorm with the β 1 -norm is a commonly used approach to compute an approximate solution of the original β 0 -minimization problem by means of convex programming. In the theory of compressive sensing, the condition that the sensing matrix satisfies the Restricted Isometry Property (RIP) is a sufficient condition to guarantee that the solution of the β 1 -approximated problem is equal to the solution of the original β 0 -minimization problem. However, the evaluation of the conservativeness of the β 1 -relaxation approaches is recognized to be a difficult task in case the {RIP} is not satisfied. In this paper, we present an alternative approach to minimize the β 0 -norm of a vector under given constraints. In particular, we show that an β 0 -minimization problem can be relaxed into a sequence of semidefinite programming problems, whose solutions are guaranteed to converge to the optimizer (if unique) of the original combinatorial problem also in case the {RIP} is not satisfied. Segmentation of {ARX} models is then discussed in order to show, through a relevant problem in system identification, that the proposed approach outperforms the β 1 -based relaxation in detecting piece-wise constant parameter changes in the estimated model
Error analysis in the measurement of average power with application to switching controllers
The behavior of the power measurement error due to the frequency responses of first order transfer functions between the input sinusoidal voltage, input sinusoidal current and the signal multiplier was studied. It was concluded that this measurement error can be minimized if the frequency responses of the first order transfer functions are identical
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