84 4 Brief Survey of Cognitive Architectures Joshua Blue is not under active development and has not been for some time; however, the project may be reanimated in future. Joshua Blue’s core knowledge representation is a semantic network of nodes connected by links along which activation spreads. Although many of the nodes have specific semantic refer- ents, as in a classical semantic net, the spread of activation through the network is designed to lead to the emergence of “assemblies” (which could also be thought of as dynamical attractors) in a manner more similar to an attractor neural network. A major difference from typical semantic or neural network models is the central role that affect plays in the system’s dynamics. The weights of the links in the knowledge base are adjusted dynamically based on the emotional context — a very direct way of ensuring that cognitive processes and mental representations are continuously influenced by affect. Qualitatively, this mimics the way that particular emotions in the human brain correlate with the dissemination throughout the brain of particular neurotransmitters, which then affect synaptic activity. A result of this architecture is that in Joshua Blue, emotion directs attention in a very direct way: affective weighting is important in determining which associated objects will become part of the focus of attention, or will be retained from memory. A notable similarity between CogPrime and Joshua Blue is that in both systems, nodes are assigned two quantitative attention values, one governing allocation of current system resources (mainly processor time; this is CogPrime’s ShortTermImportance) and one governing the long-term allocation of memory (CogPrime’s LongTermImportance). The concrete work done with Joshua Blue involved using it to control a simple agent in a sim- ulated world, with the goal that via human interaction, the agent would develop a complex and humanlike emotional and motivational structure from its s