2,251 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
Towards adaptive multi-robot systems: self-organization and self-adaptation
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible
Reinforcement Learning
Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field
Computation Approaches for Continuous Reinforcement Learning Problems
Optimisation theory is at the heart of any control process, where we seek to control the behaviour of a system through a set of actions. Linear control problems have been extensively studied, and optimal control laws have been identified. But the world around us is highly non-linear and unpredictable. For these dynamic systems, which don’t possess the nice mathematical properties of the linear counterpart, the classic control theory breaks and other methods have to be employed. But nature thrives by optimising non-linear and over-complicated systems. Evolutionary Computing (EC) methods exploit nature’s way by imitating the evolution process
and avoid to solve the control problem analytically.
Reinforcement Learning (RL) from the other side regards the optimal control problem as a sequential one. In every discrete time step an action is applied. The transition of the system to a new state is accompanied by a sole numerical value, the “reward” that designate the quality of the control action. Even though the amount of feedback information is limited into a sole
real number, the introduction of the Temporal Difference method made possible to have accurate predictions of the value-functions. This paved the way to optimise complex structures, like the Neural Networks, which are used to approximate the value functions.
In this thesis we investigate the solution of continuous Reinforcement Learning control problems by EC methodologies. The accumulated reward of such problems throughout an episode suffices as information to formulate the required measure, fitness, in order to optimise a population of candidate solutions. Especially, we explore the limits of applicability of a specific branch of EC, that of Genetic Programming (GP). The evolving population in the GP case is comprised
from individuals, which are immediately translated to mathematical functions, which can serve
as a control law.
The major contribution of this thesis is the proposed unification of these disparate Artificial Intelligence paradigms. The provided information from the systems are exploited by a step by step basis from the RL part of the proposed scheme and by an episodic basis from GP. This makes possible to augment the function set of the GP scheme with adaptable Neural Networks. In the quest to achieve stable behaviour of the RL part of the system a modification of the Actor-Critic
algorithm has been implemented.
Finally we successfully apply the GP method in multi-action control problems extending the spectrum of the problems that this method has been proved to solve. Also we investigated the capability of GP in relation to problems from the food industry. These type of problems exhibit also non-linearity and there is no definite model describing its behaviour
Advances in Reinforcement Learning
Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic
Decision tree learning for intelligent mobile robot navigation
The replication of human intelligence, learning and reasoning by means of computer
algorithms is termed Artificial Intelligence (Al) and the interaction of such
algorithms with the physical world can be achieved using robotics. The work described in
this thesis investigates the applications of concept learning (an approach which takes its
inspiration from biological motivations and from survival instincts in particular) to robot
control and path planning. The methodology of concept learning has been applied using
learning decision trees (DTs) which induce domain knowledge from a finite set of training
vectors which in turn describe systematically a physical entity and are used to train a robot
to learn new concepts and to adapt its behaviour.
To achieve behaviour learning, this work introduces the novel approach of hierarchical
learning and knowledge decomposition to the frame of the reactive robot architecture.
Following the analogy with survival instincts, the robot is first taught how to survive in
very simple and homogeneous environments, namely a world without any disturbances or
any kind of "hostility". Once this simple behaviour, named a primitive, has been established, the robot is trained to adapt new knowledge to cope with increasingly complex
environments by adding further worlds to its existing knowledge. The repertoire of the
robot behaviours in the form of symbolic knowledge is retained in a hierarchy of clustered
decision trees (DTs) accommodating a number of primitives. To classify robot perceptions,
control rules are synthesised using symbolic knowledge derived from searching the
hierarchy of DTs.
A second novel concept is introduced, namely that of multi-dimensional fuzzy associative
memories (MDFAMs). These are clustered fuzzy decision trees (FDTs) which are trained
locally and accommodate specific perceptual knowledge. Fuzzy logic is incorporated to
deal with inherent noise in sensory data and to merge conflicting behaviours of the DTs.
In this thesis, the feasibility of the developed techniques is illustrated in the robot
applications, their benefits and drawbacks are discussed
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