48 resultados para Symbolic Computation


Relevância:

10.00% 10.00%

Publicador:

Resumo:

Résumé du poster : Diabetes is both an important chronic disease and a real public health problem. It requires a great control over the body and a great mastery of the tools used in the daily struggle to reach a physiological balance. It is therefore a disease in which health education plays an important role, since patients are expected to reach a certain autonomy in the management of their disease. But how can the patients' autonomy be promoted? This is the question to which this study tried to answer from the perspective of socio-cultural psychology. The study was launched by the Cantonal Diabetes Program Vaud and aimed at evaluating a health education setting located in the east region of the Canton Vaud. It was based on both quantitative and qualitative methodological approaches. The results showed that there is a correlation between the number of hospitalizations and the quality of support provided by this particular health education setting. Moreover, the acquisition of expertise appears to be a distributed and collective process based upon the actors' active participation in various types of activities and involving and extended network. Further research is now required in order to examine how health education might be grasped through the lens of social-cultural psychology.

Relevância:

10.00% 10.00%

Publicador:

Resumo:

In cooperative multiagent systems, agents interac to solve tasks. Global dynamics of multiagent teams result from local agent interactions, and are complex and difficult to predict. Evolutionary computation has proven a promising approach to the design of such teams. The majority of current studies use teams composed of agents with identical control rules ("geneti- cally homogeneous teams") and select behavior at the team level ("team-level selection"). Here we extend current approaches to include four combinations of genetic team composition and level of selection. We compare the performance of genetically homo- geneous teams evolved with individual-level selection, genetically homogeneous teams evolved with team-level selection, genetically heterogeneous teams evolved with individual-level selection, and genetically heterogeneous teams evolved with team-level selection. We use a simulated foraging task to show that the optimal combination depends on the amount of cooperation required by the task. Accordingly, we distinguish between three types of cooperative tasks and suggest guidelines for the optimal choice of genetic team composition and level of selection

Relevância:

10.00% 10.00%

Publicador:

Resumo:

We survey the population genetic basis of social evolution, using a logically consistent set of arguments to cover a wide range of biological scenarios. We start by reconsidering Hamilton's (Hamilton 1964 J. Theoret. Biol. 7, 1-16 (doi:10.1016/0022-5193(64)90038-4)) results for selection on a social trait under the assumptions of additive gene action, weak selection and constant environment and demography. This yields a prediction for the direction of allele frequency change in terms of phenotypic costs and benefits and genealogical concepts of relatedness, which holds for any frequency of the trait in the population, and provides the foundation for further developments and extensions. We then allow for any type of gene interaction within and between individuals, strong selection and fluctuating environments and demography, which may depend on the evolving trait itself. We reach three conclusions pertaining to selection on social behaviours under broad conditions. (i) Selection can be understood by focusing on a one-generation change in mean allele frequency, a computation which underpins the utility of reproductive value weights; (ii) in large populations under the assumptions of additive gene action and weak selection, this change is of constant sign for any allele frequency and is predicted by a phenotypic selection gradient; (iii) under the assumptions of trait substitution sequences, such phenotypic selection gradients suffice to characterize long-term multi-dimensional stochastic evolution, with almost no knowledge about the genetic details underlying the coevolving traits. Having such simple results about the effect of selection regardless of population structure and type of social interactions can help to delineate the common features of distinct biological processes. Finally, we clarify some persistent divergences within social evolution theory, with respect to exactness, synergies, maximization, dynamic sufficiency and the role of genetic arguments.