Landscape of intelligent cores: An autonomic multi-agent approach for space application

Space applications are challenged by the reliability of parallel computing systems (FPGAs) employed in space crafts due to Single-Event Upsets. The work reported in this paper aims to achieve self-managing systems which are reliable for space applications by applying autonomic computing constructs to parallel computing systems. A novel technique, 'Swarm-Array Computing' inspired by swarm robotics, and built on the foundations of autonomic and parallel computing is proposed as a path to achieve autonomy. The constitution of swarm-array computing comprising for constituents, namely the computing system, the problem / task, the swarm and the landscape is considered. Three approaches that bind these constituents together are proposed. The feasibility of one among the three proposed approaches is validated on the SeSAm multi-agent simulator and landscapes representing the computing space and problem are generated using the MATLAB.

Building communities via a learning landscape

The Learning Landscape project described here is known as RedGloo and has several objectives; among others it aims to help students to make friends, contacts and join communities based on interests and competencies. RedGloo provides a space where students can support each other with personal, academic and career development, sharing insights gained from extracurricular activities as well as their degree programmes. It has shown tendencies of becoming a learning community with several communities of practice.

Landscape state machines: Tools for evolutionary algorithm performance analyses and landscape/algorithm mapping

Many evolutionary algorithm applications involve either fitness functions with high time complexity or large dimensionality (hence very many fitness evaluations will typically be needed) or both. In such circumstances, there is a dire need to tune various features of the algorithm well so that performance and time savings are optimized. However, these are precisely the circumstances in which prior tuning is very costly in time and resources. There is hence a need for methods which enable fast prior tuning in such cases. We describe a candidate technique for this purpose, in which we model a landscape as a finite state machine, inferred from preliminary sampling runs. In prior algorithm-tuning trials, we can replace the 'real' landscape with the model, enabling extremely fast tuning, saving far more time than was required to infer the model. Preliminary results indicate much promise, though much work needs to be done to establish various aspects of the conditions under which it can be most beneficially used. A main limitation of the method as described here is a restriction to mutation-only algorithms, but there are various ways to address this and other limitations.

The role of management and landscape context in the restoration of grassland phytophagous beetles

1. Declines in area and quality of species-rich mesotrophic and calcareous grasslands have occurred all across Europe.While the European Union has promoted schemes to restore these grasslands, the emphasis for management has remained largely focused on plants. Here we focus on restoration of the phytophagous beetles of these grasslands. Although local management, particularly that which promotes the establishment of host plants, is key to restoration success, dispersal limitation is also likely to be an important limiting factor during the restoration of phytophagous beetle assemblages. 2. Using a 3-year multi-site experiment, we investigated how restoration success of phytophagous beetles was affected by hay-spreading management (intended to introduce target plant species), success in restoration of the plant communities and the landscape context within which restoration was attempted. 3. Restoration success of the plants was greatest where green hay spreading had been used to introduce seeds into restoration sites. Beetle restoration success increased over time, although hayspreading had no direct effect. However, restoration success of the beetles was positively correlated with restoration success of the plants. 4. Overall restoration success of the phytophagous beetles was positively correlated with the proportion of species-rich grassland in the landscape, as was the restoration success of the polyphagous beetles. Restoration success for beetles capable of flight and those showing oligophagous host plant specialism were also positively correlated with connectivity to species-rich grasslands. There was no indication that beetles not capable of flight showed greater dependence on landscape scale factors than flying species. 5. Synthesis and applications. Increasing the similarity of the plant community at restoration sites to target species-rich grasslands will promote restoration success for the phytophagous beetles. However, landscape context is also important, with restoration being approximately twice as successful in those landscapes containing high as opposed to low proportions of species-rich grassland. By targeting grassland restoration within landscapes containing high proportions of species-rich grassland, dispersal limitation problems associated with restoration for invertebrate assemblages are more likely to be overcome.