Emergent Behavior Development and Control in Multi-Agent Systems

Emergence in natural systems is the development of complex behaviors that result from the aggregation of simple agent-to-agent and agent-to-environment interactions. Emergence research intersects with many disciplines such as physics, biology, and ecology and provides a theoretical framework for investigating how order appears to spontaneously arise in complex adaptive systems. In biological systems, emergent behaviors allow simple agents to collectively accomplish multiple tasks in highly dynamic environments; ensuring system survival. These systems all display similar properties: self-organized hierarchies, robustness, adaptability, and decentralized task execution. However, current algorithmic approaches merely present theoretical models without showing how these models actually create hierarchical, emergent systems. To fill this research gap, this dissertation presents an algorithm based on entropy and speciation - defined as morphological or physiological differences in a population - that results in hierarchical emergent phenomena in multi-agent systems. Results show that speciation creates system hierarchies composed of goal-aligned entities, i.e. niches. As niche actions aggregate into more complex behaviors, more levels emerge within the system hierarchy, eventually resulting in a system that can meet multiple tasks and is robust to environmental changes. Speciation provides a powerful tool for creating goal-aligned, decentralized systems that are inherently robust and adaptable, meeting the scalability demands of current, multi-agent system design. Results in base defense, k-n assignment, division of labor and resource competition experiments, show that speciated populations create hierarchical self-organized systems, meet multiple tasks and are more robust to environmental change than non-speciated populations

Neuro-Evolution for Emergent Specialization in Collective Behavior Systems

Eiben, A.E. [Promotor]Schut, M.C. [Copromotor

A sensory system for robots using evolutionary artificial neural networks.

The thesis presents the research involved with developing an Intelligent Vision System for an animat that can analyse a visual scene in uncontrolled environments. Inspiration was drawn both from Biological Visual Systems and Artificial Image Recognition Systems. Several Biological Systems including the Insect, Toad and Human Visual Systems were studied alongside popular Pattern Recognition Systems such as fully connected Feedforward Networks, Modular Neural Networks and the Neocognitron. The developed system, called the Distributed Neural Network (DNN) was based on the sensory-motor connections in the common toad, Bufo Bufo. The sparsely connected network architecture has features of modularity enhanced by the presence of lateral inhibitory connections. It was implemented using Evolutionary Artificial Neural Networks (EANN). A novel method called FUSION was used to train the DNN, which is an amalgamation of several concepts of learning in Artificial Neural Networks such as Unsupervised Learning, Supervised Learning, Reinforcement Learning, Competitive Learning, Self-organisation and Fuzzy Logic. The DNN has unique feature detecting capabilities. When the DNN was tested using images that comprised of combination of features used in the training set, the DNN was successful in recognising individual features. The combinations of features were never used in the training set. This is a unique feature of the DNN trained using Fusion that cannot be matched by any other popular ANN architecture or training method. The system proved to be robust in dealing with New and Noisy Images. The unique features of the DNN make the network suitable for applications in robotics such as obstacle avoidance and terrain recognition, where the environment is unpredictable. The network can also be used in the field of Medical Imaging, Biometrics (Face and Finger Print Recognition) and Quality Inspection in the Food Processing Industry and applications in other uncontrolled environments

Simple identification tools in FishBase

Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further development. It explores the possibility of a holistic and integrated computeraided strategy