A swarm intelligence based approach to the mine detection problem

This research focuses on the application of swarm intelligence to the problem of mine detection. Swarm Intelligence concepts have captivated the interests of researchers mainly in collective robotics, optimization problems (traveling salesman problem (TSP), quadratic assignment problem, graph coloring etc.), and communication networks (routing) etc [1]. In the mine detection problem we are faced with sub problems such as searching for the mines over the minefield, defusing them effectively, and assuring that the field is clear of mines within the least possible time. In the problem, we assume that the mines can be diffused by the collective action of the robots for which a model based on ant colonies is given. In the first part of the project we study the ant colony system applied to the mine detection problem. The theoretical aspects such as the ant\u27s behavior (reaction of the ants to various circumstances that it faces), their motion over the minefield, and their process of defusing the mines are investigated. In the second section we highlight a certain formulation that the ants may be given for doing the task effectively. The ants do the task effectively when they are able to assure that the minefield is clear of the mines within the least possible time. A compilation of the results obtained by the various studies is tabulated. In the third and final section we talk about our emulations conducted on the Multi Agent Biorobotics Lab-built groundscout robots, which were used for the demonstration of our swarm intelligence-based algorithms at a practical basis. The various projects thus far conducted were a part of the Multi Agent Biorobotics Lab at Rochester Institute of Technology

Towards Swarm Calculus: Urn Models of Collective Decisions and Universal Properties of Swarm Performance

Methods of general applicability are searched for in swarm intelligence with the aim of gaining new insights about natural swarms and to develop design methodologies for artificial swarms. An ideal solution could be a `swarm calculus' that allows to calculate key features of swarms such as expected swarm performance and robustness based on only a few parameters. To work towards this ideal, one needs to find methods and models with high degrees of generality. In this paper, we report two models that might be examples of exceptional generality. First, an abstract model is presented that describes swarm performance depending on swarm density based on the dichotomy between cooperation and interference. Typical swarm experiments are given as examples to show how the model fits to several different results. Second, we give an abstract model of collective decision making that is inspired by urn models. The effects of positive feedback probability, that is increasing over time in a decision making system, are understood by the help of a parameter that controls the feedback based on the swarm's current consensus. Several applicable methods, such as the description as Markov process, calculation of splitting probabilities, mean first passage times, and measurements of positive feedback, are discussed and applications to artificial and natural swarms are reported

The organization of soil disposal by ants

Colonies of Pheidole ambigua ants excavate soil and drop it outside the nest entrance. The deposition of thousands of loads leads to the formation of regular ring-shaped piles. How is this pattern generated? This study investigated soil pile formation on level and sloping surfaces, both empirically and using an agent-based model. We found that ants drop soil preferentially in the direction in which the slope is least steeply uphill from the nest entrance, both when adding to an existing pile and when starting a new pile. Ants respond to cues from local slope to choose downhill directions. Ants walking on a slope increase the frequency and magnitude of changes in direction, and more of these changes of direction take them downhill than uphill. Also, ants carrying soil on a slope wait longer before dropping their soil compared to ants on a level plane. These mechanisms combine to focus soil dropping in the downhill direction, without the necessity of a direct relationship between slope and probability of dropping soil. These empirically determined rules were used to simulate soil disposal. The slight preference for turning downhill measured empirically was shown in the model to be sufficient to generate biologically realistic patterns of soil dumping when combined with memory of the direction of previous trips. From simple rules governing individual behaviour an overall pattern emerges, which is appropriate to the environment and allows a rapid response to changes

Biological System Behaviours and Natural-inspired Methods and Their Applications to Supply Chain Management

People have learnt from biological system behaviours and structures to design and develop a number of different kinds of optimisation algorithms that have been widely used in both theoretical study and practical applications in engineering and business management. An efficient supply chain is very important for companies to survive in global competitive market. An effective SCM (supply chain management) is the key for implement an efficient supply chain. Though there have been considerable amount of study of SCM, there have been very limited publications of applying the findings from the biological system study into SCM. In this paper, through systematic literature review, various SCM issues and requirements are discussed and some typical biological system behaviours and natural-inspired algorithms are evaluated for the purpose of SCM. Then the principle and possibility are presented on how to learn the biological systems' behaviours and natural-inspired algorithms for SCM and a framework is proposed as a guide line for users to apply the knowledge learnt from the biological systems for SCM. In the framework, a number of the procedures have been presented for using XML to represent both SCM requirement and bio-inspiration data. To demonstrate the proposed framework, a case study has been presented for users to find the bio-inspirations for some particular SCM problems in automotive industry

Self-organising agent communities for autonomic resource management

The autonomic computing paradigm addresses the operational challenges presented by increasingly complex software systems by proposing that they be composed of many autonomous components, each responsible for the run-time reconfiguration of its own dedicated hardware and software components. Consequently, regulation of the whole software system becomes an emergent property of local adaptation and learning carried out by these autonomous system elements. Designing appropriate local adaptation policies for the components of such systems remains a major challenge. This is particularly true where the system’s scale and dynamism compromise the efficiency of a central executive and/or prevent components from pooling information to achieve a shared, accurate evidence base for their negotiations and decisions.In this paper, we investigate how a self-regulatory system response may arise spontaneously from local interactions between autonomic system elements tasked with adaptively consuming/providing computational resources or services when the demand for such resources is continually changing. We demonstrate that system performance is not maximised when all system components are able to freely share information with one another. Rather, maximum efficiency is achieved when individual components have only limited knowledge of their peers. Under these conditions, the system self-organises into appropriate community structures. By maintaining information flow at the level of communities, the system is able to remain stable enough to efficiently satisfy service demand in resource-limited environments, and thus minimise any unnecessary reconfiguration whilst remaining sufficiently adaptive to be able to reconfigure when service demand changes