Multiagent systems in which agents interact with each other are now being proposed as a solution to many problems which can be grouped together under the \distributed problem solv-ing" umbrella. For such systems to work properly , it is necessary that agents learn from their environment and adapt their behaviour accordingly. In this paper we present a system which uses a combination of neuro{fu...

LEM3 is the newest version of the learnable evolution model (LEM), a non-Darwinian evolutionary computation methodology that employs machine learning to guide evolutionary processes. Due to the deep integration of different modes of operation, several novel elements in its algorithm, and the use of the advanced machine learning system AQ21, the LEM3 system is a highly efficient and effective im...

The selectivities of neurons in primary visual cortex are often considered to be adapted to the statistics of natural images. Accordingly, simple cell-like tuning emerges when unsupervised learning models that seek sparse representations of input probabilities are trained on natural scenes. However, orientation tuning develops before structured vision starts, rendering these previous results mo...

First word learning should be difficult because any pairing of a word and scene presents the learner with an infinite number of possible referents. Accordingly, theorists of children’s rapid word learning have sought constraints on word-referent mappings. These constraints are thought to work by enabling learners to resolve the ambiguity inherent in any labeled scene to determine the speaker’s ...

CB 1 receptor-compromised animals show profound deficits in extinguishing learned behavior from aversive conditioning tasks, but display normal extinction learning in appetitive operant tasks. However, it is difficult to discern whether the differential involvement of the endogenous cannabinoid system on extinction results from the hedonics or the required responses associated with the disparat...

In their study "Vocal Learning in the Functionally Referential Food Grunts of Chimpanzees", Watson et al.[1] claimed that they "provide the first evidence for vocal learning in a referential call in non-humans". We challenge this conclusion, on two counts. For one, we are not convinced that the authors controlled for arousal (or at least they did not report such data); furthermore, the vocal ch...

Attribute noise can affect classification learning. Previous work in handling attribute noise has focused on those predictable attributes that can be predicted by the class and other attributes. However, attributes can often be predictive but unpredictable. Being predictive, they are essential to classification learning and it is important to handle their noise. Being unpredictable, they requir...

First word learning should be difficult because any pairing of a word and scene presents the learner with an infinite number of possible referents. Accordingly, theorists of children's rapid word learning have sought constraints on word-referent mappings. These constraints are thought to work by enabling learners to resolve the ambiguity inherent in any labeled scene to determine the speaker's ...

Within the EU4ALL project (www.eu4all-project.eu) extensive research has been undertaken to detect the impact of legal and political frameworks on the accessibility of lifelong learning (ALL), with a clear focus on higher education in this initial phase. The introduction of appropriate technologies in educational institutions is accompanied and shaped by legal and political concepts, measures a...