Adaptive Behavior

The study of adaptive behavior, including learning, usually centers on the effects of natural selection for individual survival. But because reproduction is evolutionarily more important than survival, sexual selection through mate choice (Darwin, 1871) can also have profound consequences on the evolution of creatures' bodies and behaviors. This article shows, through simulation models, how one type of learning-parental imprinting—can evolve purely through sexual selection, to help in selecting appropriate mates and in tracking changes in the phenotypic makeup of the population across generations. At moderate mutation rates, when population tracking becomes an important but still soluble problem, imprinting proves more useful and evolves more quickly than at low or high mutation rates. We also show that parental imprinting can facilitate the formation of new species. In reviewing the biological literature on imprinting, we note that these results confirm some previous speculations by other researchers concerning the adaptive functions and evolutionary consequences of imprinting. Finally, we discuss how sexual selection through mate choice may have great scientific implications for our understanding of the interactions among evolution, learning, and behavior, and potentially important engineering applications for increasing the efficiency of evolutionary search and optimization methods.

Different animals employ different strategies for sampling sensory data. In animals that can see, differences in sampling strategy manifest themselves as differences in field of view and in spatially variant sampling (so-called foveal vision). In analyzing adaptive behavior in animals, or attempting to design autonomous robots, mechanisms for exploring variations in sensory sampling strategy will be required. This article describes our work exploring a minimal system for investigating the effects of variations in patterns of sensory sampling. We have reimplemented Wilson's animat (Wilson, 1985b) and then experimented with altering its sensory sampling pattern (i.e., its sensory field). Empirical results are presented which demonstrate that alterations in the sensory field pattern can have a significant effect on the animat's observable behavior. Analysis of our results involves characterizing the interaction between the animat's sensory field and the environment within which the animat resides. We found that the animat's observed behavior can, at least in part, be explained by the animat cautiously moving in a manner that attempts to maximize the generation of new information from the environment over time. We demonstrate that similar explanations can be offered for behavioral patterns in real animals. The article concludes with a discussion of the generality of the results and reflections on the prospects for further work.

We discuss the methodological foundations for our work on the development of cognitive architectures, or control systems, for situated autonomous agents. Our focus is the problems of developing sensorimotor control systems for mobile robots, but we also discuss the applicability of our approach to the study of biological systems. We argue that, for agents required to exhibit sophisticated interactions with their environments, complex sensorimotor processing is necessary, and the design, by hand, of control systems capable of such processing is likely to become prohibitively difficult as complexity increases. We propose an automatic design process involving artificial evolution, wherein the basic building blocks used for evolving cognitive architectures are noise-tolerant dynamical neural networks. These networks may be recurrent and should operate in real time. The evolution should be incremental, using an extended and modified version of a genetic algorithm.

Practical constraints suggest that initial architecture evaluations should be done largely in simulation. To support our claims and proposals, we summarize results from some preliminary simulation experiments in which visually guided robots are evolved to operate in simple environments. Significantly, our results demonstrate that robust visually guided control systems evolve from evaluation functions that do not explicitly require monitoring visual input. We outline the difficulties involved in continuing with simulations and conclude by describing specialized visuorobotic equipment, designed to eliminate the need for simulated sensors and actuators.