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Uutuutta etsivät moniagenttijärjestelmät
This paper considers novelty-seeking multi-agent systems as a step towards more efficient generation of creative artifacts. We describe a simple multi-agent architecture where agents have limited resources and exercise self-criticism, veto power and voting to collectively regulate which artifacts are selected to the domain i.e., the cultural storage of the system. To overcome their individual resource limitations, agents have a limited access to the artifacts already in the domain which they can use to guide their search for novel artifacts. Creating geometric images called spirographs as a case study, we show that novelty-seeking multi-agent systems can be more productive in generating novel artifacts than a single-agent or monolithic system. In particular, veto power is in our case an effective collaborative decision-making strategy for enhancing novelty of domain artifacts, and self-criticism of agents can significantly reduce the collaborative effort in decision making.Peer reviewe
Learning a world model and planning with a self-organizing, dynamic neural system
We present a connectionist architecture that can learn a model of the
relations between perceptions and actions and use this model for behavior
planning. State representations are learned with a growing self-organizing
layer which is directly coupled to a perception and a motor layer. Knowledge
about possible state transitions is encoded in the lateral connectivity. Motor
signals modulate this lateral connectivity and a dynamic field on the layer
organizes a planning process. All mechanisms are local and adaptation is based
on Hebbian ideas. The model is continuous in the action, perception, and time
domain.Comment: 9 pages, see http://www.marc-toussaint.net
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