The Influence of Collective Working Memory Strategies on Agent Teams

Past self-organizing models of collectively moving "particles" (simulated bird flocks, fish schools, etc.) typically have been based on purely reflexive agents that have no significant memory of past movements or environmental obstacles. These agent collectives usually operate in abstract environments, but as these domains take on a greater realism, the collective requires behaviors use not only presently observed stimuli but also remembered information. It is hypothesized that the addition of a limited working memory of the environment, distributed among the collective's individuals can improve efficiency in performing tasks. This is first approached in a more traditional particle system in an abstract environment. Then it is explored for a single agent, and finally a team of agents, operating in a simulated 3-dimensional environment of greater realism. In the abstract environment, a limited distributed working memory produced a significant improvement in travel between locations, in some cases improving performance over time, while in others surprisingly achieving an immediate benefit from the influence of memory. When strategies for accumulating and manipulating memory were subsequently explored for a more realistic single agent in the 3-dimensional environment, if the agent kept a local or a cumulative working memory, its performance improved on different tasks, both when navigating nearby obstacles and, in the case of cumulative memory, when covering previously traversed terrain. When investigating a team of these agents engaged in a pursuit scenario, it was determined that a communicating and coordinating team still benefited from a working memory of the environment distributed among the agents, even with limited memory capacity. This demonstrates that a limited distributed working memory in a multi-agent system improves performance on tasks in domains of increasing complexity. This is true even though individual agents know only a fraction of the collective's entire memory, using this partial memory and interactions with others in the team to perform tasks. These results may prove useful in improving existing methodologies for control of collective movements for robotic teams, computer graphics, particle swarm optimization, and computer games, and in interpreting future experimental research on group movements in biological populations

Development of a Large-Scale Integrated Neurocognitive Architecture Part 1: Conceptual Framework

The idea of creating a general purpose machine intelligence that captures many of the features of human cognition goes back at least to the earliest days of artificial intelligence and neural computation. In spite of more than a half-century of research on this issue, there is currently no existing approach to machine intelligence that comes close to providing a powerful, general-purpose human-level intelligence. However, substantial progress made during recent years in neural computation, high performance computing, neuroscience and cognitive science suggests that a renewed effort to produce a general purpose and adaptive machine intelligence is timely, likely to yield qualitatively more powerful approaches to machine intelligence than those currently existing, and certain to lead to substantial progress in cognitive science, AI and neural computation. In this report, we outline a conceptual framework for the long-term development of a large-scale machine intelligence that is based on the modular organization, dynamics and plasticity of the human brain. Some basic design principles are presented along with a review of some of the relevant existing knowledge about the neurobiological basis of cognition. Three intermediate-scale prototypes for parts of a larger system are successfully implemented, providing support for the effectiveness of several of the principles in our framework. We conclude that a human-competitive neuromorphic system for machine intelligence is a viable long- term goal, but that for the short term, substantial integration with more standard symbolic methods as well as substantial research will be needed to make this goal achievable