Deliberate evolution in multi-agent systems

This paper presents an architecture for an agent capable of deliberation about the creation of new agents, and of actually creating a new agent in the multi-agent system, on the basis of this deliberation. After its creation the new agent participates fully in the running multi-agent system. The agent architecture is based on an existing generic agent model, and includes explicit formal conceptual representations of both structures of agents and (behavioural) properties of agents that can be used as requirements. Moreover, to support the deliberation process the agent has explicit knowledge of relations between structure and properties of agents. To actually create a new agent at run-time on the basis of the results of deliberation, the agent executes a creation action in the material world, which leads to a world state update to include the new agent, after which the new agent functions within the multi-agent system. This approach enables the design of evolution processes in societies of agents for which the evolution is not a merely material process which takes place in isolation from the mental worlds of the agents, but allows for interaction between mental and material processes. A combined mind-matter approach results in which the agents in a society can deliberatively influence the direction of the evolution, comparable to the potential offered by genetic engineering. The architecture has been designed using the compositional development method DESIRE, and has been tested in a prototype implementation. It is discussed how the approach introduced here can be used as a basis for automatic evolution of multi-agent systems for Electronic Commerce

Deployment and Shape Change of a Tensegrity Structure Using Path-Planning and Feedback Control

Tensegrity structures are pin-jointed assemblies of struts and cables that are held together in a stable state of stress. Shape control is a combination of control-commands with measurements to achieve a desired form. Applying shape control to a near-full-scale deployable tensegrity structure presents a rare opportunity to analytically and experimentally study and control the effects of large shape changes on a closely coupled multi-element system. Simulated cable-length changes provide an initial activation plan to reach an effective sequence for self-stress. Controlling internal forces is more sensitive than controlling movements through cable-length changes; internal force-control is thus a better objective than movement-control for small adjustments to the structure. The deployment of a tensegrity structure in previous work was carried out using predetermined commands. In this paper, two deployment methods and a method for self-stress are presented. The first method uses feedback cycles to increase speed of deployment compared with implementation of empirically predetermined control-commands. The second method consists of three parts starting with a path-planning algorithm that generates search trees at the initial point and the target point using a greedy algorithm to create a deployment trajectory. Collision and overstress avoidance for the deployment trajectory involve checks of boundaries defined by positions of struts and cables. Even actuator deployment followed by commands obtained from a search algorithm results in the successful connection of the structure at midspan. Once deployment at midspan is achieved by either method, a self-stress algorithm is implemented to correct the position and element forces in the structure to the design configuration prior to in-service loading. Modification of deployment control-commands using the feedback method (with twenty cycles) compared with empirically predetermined control-commands successfully provides a more efficient deployment trajectory prior to midspan connection with up to 50% reduction in deployment time. The path-planning method successfully enables deployment and connection at midspan with a further time reduction of 68% compared with the feedback method (with twenty cycles). The feedback control, the path-planning method and the soft-constraint algorithm successfully lead to efficient deployment and preparation for service loading. Advanced computing algorithms have potential to improve the efficiency of complex deployment challenges