An associative memory for autonomous agents

To improve the abilities of a behavior based autonomous agent a biologically inspired memory is introduced. The memory content is built up from scratch. The data containers, called mnemograms, are general and flexible enough to handle different kinds of data. Using a reinforcement mechanism valuable mnemograms are separated from those of lesser value. Mnemograms are stored in three hierarchically ordered layers to separate new from older ones. This is important for organizing search runs. The data containers are linked together, thus, a first level of semantical information is created. A first simple behavior takes advantage of these structures. It enables the agent learn to distinguish between food particles and other particles

Emergent Behavior of Interacting Groups of Communicative Agents

This paper presents a simulation of the behavior of different species of birds, which share the same habitat, but manage to use different times of the day to sing their songs. Therefore, they avoid a vocal competition and improve the conditions to find a mate. Communicative agents are used to model the birds and their behavior. A simple set of rules is used to make the decisions when and how to change the time for the search for a mate. By incorporating damping and amplifying feedback loops the collective behavior of each species led the system to a solution which was favorable to all agents

Simulation of the Coevolution Of Insects And Flowers

Flowers need insects for their pollination and insects rely on the nectar and the pollen as a food resource. But instead of visiting all flowers, the insects limit their visits to a small number. This paper presents a simulation of the behavior of the insects which results in a specialized perception of blossom colors and fragrances by the insects.

Memory-based Behavior Coordination in Animats

This paper presents an action selection mechanism which makes use of a memory, especially designed for autonomous agents, to choose actions while considering earlier experiences. A reinforcement mechanism separates the more successful choices from the others. But the selection mechanism is still kept open to new actions or older ones, which failed more often, to be adaptable to future changes in the animat's environment

Organizing an Agent's Memory

The architecture of a memory for autonomous agents is presented. It is base

Self-organized Diversification of Signals of

This paper reports results of a computer simulation which models the behavior of different species of communicative agents which share the same habitat. The agents use signals to communicate. This communication is in a male/female context: males use their signals to attract females. Since both share the same habitat, the signals of the species have to be distinguishable, allowing females to identify a male of her own species. But signals used by males of the same species should be similar. The simulation shows that these conventions can emerge using mechanisms of self-organization