The emergence of specialization in heterogeneous artificial agent populations

In this dissertation, I present the Weight-Allocated Social Pressure System (WASPS). WASPS is a computational framework that when applied, can allow for the increase in agent specialization within a multi-agent population. Research has shown that specialization can lead to an overall increase in the productivity levels within a population [55]. WASPS aims to provide a mix of features from existing frameworks such as the genetic threshold and social inhibition models. It also subsumes these models, and allows hybrids of them to be created. It provides individual level behaviour as found in the genetic threshold model. As in some variations of the genetic threshold model [49], WASPS also allows for individual level learning. As found in the social inhibition models, WASPS allows for social influence, or population level learning. Unlike some models, WASPS allows agents to self-organize based on available tasks. In addition, it makes allowances for agents to allocate a resource among multiple tasks during a work period, wherein most models allow the selection of only one task. WASPS allows the assumption that agents are heterogeneous in their task performance aptitudes. It thus aims to create skill-based agent specialization within the population. This will allow more skilled agents to allocate more resources to tasks for which they have comparative advantages over their competition. Because WASPS is self-organizing, it can handle the addition and removal of agents from social networks, as well as changes in the connections between agents. WASPS does not limit the definition of many or its parameters, which allows it to deal with changing definitions for those parameters. For example, WASPS can easily adjust to deal with changing definitions of agent skill and influence. In fact, the individual level learning can be implemented in such a way that an agent can self-optimize even when it has no competitors to influence it

Memory-constrained pathfinding algorithms for partially-known environments

Pathfinding is the search for a goal state given a start state, within either static or dynamic environments. Many pathfinding algorithms exist, including established algorithms such as A*, SMA*, and D*. These algorithms all provide optimal solution paths, using all available memory. Consequently, Algorithms such as A* and D* are known to be inefficient in terms of memory space usage. SMA* and similar algorithms provide a means by which optimal solution paths can be found while being memory efficient. SMA* and such algorithms are restricted to static environments, in which state traversal costs never change. This is a severe limitation, as one of the primary fields for search algorithms are games, many of which involve dynamic environments. Presented in this paper is a dynamic variant of the established D* Lite algorithm, that is able to provide an optimal solution path, if given sufficient memory, while using as little memory as possible. It is also the case that in some areas, an optimal solution is not needed. This may be the case for robotics. Many algorithms already exist in this area, such as Anytime algorithms for time-limited searches. Real-Time algorithms for when the agent needs to move while planning its path. Also presented is an algorithm for real-time planning when an agent does not have a priori knowledge of the environment, and also limited memory capacity. This algorithm sacrifices optimality, but in turn is highly memory efficient, even in comparison to other algorithms designed for memory efficiency

Long-term rates of denudation in t he Dry Valleys, Transantarctic Mountains, southern Victoria Land, Antarctica bases on in-situ-produced cosmogenic 21Ne.

Concentrations of cosmogeni

Prolonged Interruption of Cognitive Control of Conflict Processing Over Human Faces by Task-Irrelevant Emotion Expression

As documented by Darwin 150 years ago, emotion expressed in human faces readily draws our attention and promotes sympathetic emotional reactions. How do such reactions to the expression of emotion affect our goal-directed actions? Despite the substantial advance made in the neural mechanisms of both cognitive control and emotional processing, it is not yet known well how these two systems interact. Here, we studied how emotion expressed in human faces influences cognitive control of conflict processing, spatial selective attention and inhibitory control in particular, using the Eriksen flanker paradigm. In this task, participants viewed displays of a central target face flanked by peripheral faces and were asked to judge the gender of the target face; task-irrelevant emotion expressions were embedded in the target face, the flanking faces, or both. We also monitored how emotion expression affects gender judgment performance while varying the relative timing between the target and flanker faces. As previously reported, we found robust gender congruency effects, namely slower responses to the target faces whose gender was incongruent with that of the flanker faces, when the flankers preceded the target by 0.1 s. When the flankers further advanced the target by 0.3 s, however, the congruency effect vanished in most of the viewing conditions, except for when emotion was expressed only in the flanking faces or when congruent emotion was expressed in the target and flanking faces. These results suggest that emotional saliency can prolong a substantial degree of conflict by diverting bottom-up attention away from the target, and that inhibitory control on task-irrelevant information from flanking stimuli is deterred by the emotional congruency between target and flanking stimuli