335 research outputs found

    Social learning in a multi-agent system

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    In a persistent multi-agent system, it should be possible for new agents to benefit from the accumulated learning of more experienced agents. Parallel reasoning can be applied to the case of newborn animals, and thus the biological literature on social learning may aid in the construction of effective multi-agent systems. Biologists have looked at both the functions of social learning and the mechanisms that enable it. Many researchers have focused on the cognitively complex mechanism of imitation; we will also consider a range of simpler mechanisms that could more easily be implemented in robotic or software agents. Research in artificial life shows that complex global phenomena can arise from simple local rules. Similarly, complex information sharing at the system level may result from quite simple individual learning rules. We demonstrate in simulation that simple mechanisms can outperform imitation in a multi-agent system, and that the effectiveness of any social learning strategy will depend on the agents' environment. Our simple mechanisms have obvious advantages in terms of robustness and design costs

    Extremism propagation in social networks with hubs

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    One aspect of opinion change that has been of academic interest is the impact of people with extreme opinions (extremists) on opinion dynamics. An agent-based model has been used to study the role of small-world social network topologies on general opinion change in the presence of extremists. It has been found that opinion convergence to a single extreme occurs only when the average number of network connections for each individual is extremely high. Here, we extend the model to examine the effect of positively skewed degree distributions, in addition to small-world structures, on the types of opinion convergence that occur in the presence of extremists. We also examine what happens when extremist opinions are located on the well-connected nodes (hubs) created by the positively skewed distribution. We find that a positively skewed network topology encourages opinion convergence on a single extreme under a wider range of conditions than topologies whose degree distributions were not skewed. The importance of social position for social influence is highlighted by the result that, when positive extremists are placed on hubs, all population convergence is to the positive extreme even when there are twice as many negative extremists. Thus, our results have shown the importance of considering a positively skewed degree distribution, and in particular network hubs and social position, when examining extremist transmission

    Social Learning in a Multi-Agent System

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    In a persistent multi-agent system, it should be possible for new agents to benefit from the accumulated learning of more experienced agents. Parallel reasoning can be applied to the case of newborn animals, and thus the biological literature on social learning may aid in the construction of effective multi-agent systems. Biologists have looked at both the functions of social learning and the mechanisms that enable it. Many researchers have focused on the cognitively complex mechanism of imitation; we will also consider a range of simpler mechanisms that could more easily be implemented in robotic or software agents. Research in artificial life shows that complex global phenomena can arise from simple local rules. Similarly, complex information sharing at the system level may result from quite simple individual learning rules. We demonstrate in simulation that simple mechanisms can outperform imitation in a multi-agent system, and that the effectiveness of any social learning strategy will depend on the agents' environment. Our simple mechanisms have obvious advantages in terms of robustness and design costs

    First Data On Aquaculture of the Tripletail, \u3ci\u3eLobotes surinamensis\u3c/i\u3e, a Promising Candidate Species For U.S. Marine Aquaculture

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    The Tripletail, Lobotes surinamensis, is a warm-water pelagic fish that is increasingly targeted by U.S. anglers. The superior quality of Tripletail flesh coupled with the lack of domestic commercial fisheries stimulated interests to develop aquaculture of this species. In this work, photo-thermal conditioning of captive-held broodstocks promoted maturation in females, but spontaneous spawning was not observed. GnRHa slow-release implants induced ovulation in late vitellogenic females but fertility remained below 10% when GnRHa was administered alone. However, spawns with high fertility (up to 85%) were obtained when a dopamine antagonist was administered in conjunction with GnRHa implants indicating dopamine inhibition impaired final gamete maturation, in particular sperm production in males, in aquaculture conditions. Tripletail larvae successfully initiated exogenous feeding on enriched rotifers followed by Artemia nauplii and were weaned to prepared feeds at 25 days post hatch, yet with low survival through the late phases of larval culture. Pilot grow-out trials at low density in recirculating systems revealed impressive growth rates averaging over 170 g/month through a market size above 1 kg. While protocols for hatchery culture and grow-out still need to be optimized, current data suggest that Tripletail could become a successful species for U.S. marine aquaculture
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