49 research outputs found

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

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    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

    Comparison of different cue-based swarm aggregation strategies

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    In this paper, we compare different aggregation strategies for cue-based aggregation with a mobile robot swarm. We used a sound source as the cue in the environment and performed real robot and simulation based experiments. We compared the performance of two proposed aggregation algorithms we called as the vector averaging and naïve with the state-of-the-art cue-based aggregation strategy BEECLUST. We showed that the proposed strategies outperform BEECLUST method. We also illustrated the feasibility of the method in the presence of noise. The results showed that the vector averaging algorithm is more robust to noise when compared to the naïve method

    A Reductionist Approach to Hypothesis-Catching for the Analysis of Self-Organizing Decision-Making Systems

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    A difficulty in analyzing self-organizing decision-making systems is their high dimension-ality which needs to be reduced to allow for deep insights. Following the hypothesis that such a dimensionality reduction can only be usefully determined in an act of a low-scale scientific discovery, a recipe for a data-driven, iterative process for determining, testing, and refining hypotheses about how the system operates is presented. This recipe relies on the definition of Markov chains and their analysis based on an urn model. Positive and negative feedback loops operating on global features of the system are detected by this analysis. The workflow of this analysis process is shown in two case studies investigating the BEECLUST algorithm and collective motion in locusts. The reported recipe has the potential to be generally applicable to self-organizing collective systems and is efficient due to an incremental approach.

    Diseño de comportamientos robóticos mediante Deep Learning

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    En este trabajo se estudiará el uso de aprendizaje profundo para extraer comportamientos robótico útiles en el desarrollo de tareas robótica. Se tomará como base el estado del arte actual de este tipo de técnicas. Se propondrán comportamientos y se analizará su funcionamiento. Se discutirá la bondad de la aplicación del Deep Learning a la robótica y las ventajas y desventajas que conlleva
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