An investigation into the effect of ageing on expert memory with CHREST

CHREST is a cognitive architecture that models human perception, learning, memory, and problem solving, and which has successfully simulated numerous human experimental data on chess. In this paper, we describe an investigation into the effects of ageing on expert memory using CHREST. The results of the simulations are related to the literature on ageing. The study illustrates how Computational Intelligence can be used to understand complex phenomena that are affected by multiple variables dynamically evolving as a function of time and that have direct practical implications for human societies

Modelling Socially Intelligent Agents

The perspective of modelling agents rather than using them for a specificed purpose entails a difference in approach. In particular an emphasis on veracity as opposed to efficiency. An approach using evolving populations of mental models is described that goes some way to meet these concerns. It is then argued that social intelligence is not merely intelligence plus interaction but should allow for individual relationships to develop between agents. This means that, at least, agents must be able to distinguish, identify, model and address other agents, either individually or in groups. In other words that purely homogeneous interaction is insufficient. Two example models are described that illustrate these concerns, the second in detail where agents act and communicate socially, where this is determined by the evolution of their mental models. Finally some problems that arise in the interpretation of such simulations is discussed

Co-evolution of Selection and Influence in Social Networks

Many networks are complex dynamical systems, where both attributes of nodes and topology of the network (link structure) can change with time. We propose a model of co-evolving networks where both node at- tributes and network structure evolve under mutual influence. Specifically, we consider a mixed membership stochastic blockmodel, where the probability of observing a link between two nodes depends on their current membership vectors, while those membership vectors themselves evolve in the presence of a link between the nodes. Thus, the network is shaped by the interaction of stochastic processes describing the nodes, while the processes themselves are influenced by the changing network structure. We derive an efficient variational inference procedure for our model, and validate the model on both synthetic and real-world data.Comment: In Proc. of the Twenty-Fifth Conference on Artificial Intelligence (AAAI-11

A Fake Future: The Threat of Foreign Disinformation on the U.S. and its Allies

This paper attempts to explain the threat that foreign disinformation poses for the United States Intelligence Community and its allies. The paper examines Russian disinformation from both a historical and contemporary context and how its effect on Western democracies may only be exacerbated in light of Chinese involvement and evolving technologies. Fortunately, the paper also studies practices and strategies that the United States Intelligence Community and its allied foreign counterparts may use to respond. It is hoped that this study will help shed further light on Russian and Chinese disinformation campaigns and explain how the Intelligence Community can efficiently react