Neural Networks for Pattern Recognitionby

The appeal for RBL is that it is an unsupervised learning technique, very little domain knowledge is required, and it is mathematically sound. Nehmzow and colleagues show learning behaviors using associative memories and rules to be maintained by the robot they call instinct rules. It is impressive how their Lego robots learn behaviors to avoid obstacles, follow walls, and follow corridors. Having worked with Lego vehicles, one is impressed that such robots with limited structure and computation learn their tasks before self-destructing. (Experiments with physical robots are superior to simulations, and we are quite impressed with work describing learning using physical robots.) Colombetti and Dorigo use classifier systems on a physical robot to learn complex behaviors as composition of simple behaviors. In the section on Evolution, Harvey, Husbands, and Cliff state in their paper, "An Animal should not be considered as a solution to a problem posed 4 billion years ago." This position advocates devising evolution mechanisms that use the agent's own physiology to adapt to their world and exhibit intelligent behavior with respect to their own physiology. Papers in Evolution have in common the themes encountered in the artificial life community. Papers in the Collective Behavior section describe experiments with many interacting robots engaged in group activity. These papers explore architecture and/or behaviors of each agent that contribute to collective behaviors. Sometimes, interesting behaviors from local interactions that are not programmed into agents are observed, called emergent behaviors. In the One Page Summaries at the end of proceedings we see a diverse array of papers presented as posters that belong in artificial life. These papers make a nice transition to artificial life research and contribute to study of adaptive behavior. Artificial Life has attracted scientists from varying disciplines. Animat research shares with artificial life research the goal of creating creatures that appear lifelike. These creatures "live" and may learn, reproduce, and die. Many of these creatures are given habitats and studied for their behavior and evolution. It is yet too early in artificial life research to identify common techniques for creature building or study of them. However, a general approach in this research is to produce physical systems that interact in realistic situations. This is supported by a trend toward building inexpensive and simple robots with very simple behavior engines and embeding them in a physical environment. Under such an approach, it is possible for external observers to note …