Simulated Creatures Research Articles

Exploration of Simulated Creatures Learning to Cross a Highway Using Frequency Histograms

We study via frequency histograms, the behaviour of a model of simulated cognitive agents (creatures) learning to safely cross a cellular automaton based highway. The creatures have the ability to learn from each other by evaluating how successful other creatures in the past were for their current situation. We examine the effects of the model parameters on the learning outcomes measured through metrics such as the number of creatures that have successfully crossed. In particular, we focus on the effects of the knowledge base transfer on the creatures’ success of learning. The presented model is general enough so that the considered cognitive agent, called creature, maybe even interpreted as an abstraction of an autonomous vehicle (AV), encountering suddenly another moving vehicle on its trajectory. The AV has to decide whether to continue or to break/stop in order to avoid being destroyed.

An information-theoretic landscape analysis of neuro-controlled embodied organisms

Recently, there has been a lot of interest in evolving controllers for both physically simulated creatures as well as for real physical robots. However, a range of different ANN architectures are used for controller evolution, and, in the majority of the work conducted, the choice of the architecture used is made arbitrarily. No fitness landscape analysis was provided for the underlying fitness landscape of the controller’s search space. As such, the literature remains largely inconclusive as to which ANN architecture provides the most efficient and effective space for searching the range of possible controllers through evolutionary methods. This represents the motivation for this paper where we compare the search space for four different types of ANN architecture for controller evolution through an information-theoretic analysis of the fitness landscape associated with each type of architecture.

Recent developments in the evolution of morphologies and controllers for physically simulated creatures.

Karl Sims' work on evolving body shapes and controllers for three-dimensional, physically simulated creatures generated wide interest on its publication in 1994. The purpose of this article is threefold: (a) to highlight a spate of recent work by a number of researchers in replicating, and in some cases extending, Sims' results using standard PCs (Sims' original work was done on a Connection Machine CM-5 parallel computer). In particular, a re-implementation of Sims' work by the authors will be described and discussed; (b) to illustrate how off-the-shelf physics engines can be used in this sort of work, and also to highlight some deficiencies of these engines and pitfalls when using them; and (c) to indicate how these recent studies stand in respect to Sims' original work.

Computational Studies of Exploration by Smell

Research on exploratory and searching behavior of animals and robots has attracted an increasing amount of interest recently. Existing works have focused mostly on exploratory behavior guided by vision and audition. Research on smell-guided exploration has been lacking, even though animals may use the sense of smell more widely than sight or hearing to search for food and to evade danger. This article contributes to the study of smell-guided exploration. It describes a series of increasingly complex neural networks, each of which allows a simulated creature to search for food and to evade danger by using smell. Other behaviors such as obstacle negotiation and risk taking emerge naturally from the creature's interaction with the environment. Comparative studies of these networks show that there is no significant performance advantage for a creature to have more than two sensors. This result may help to explain why real animals have only one or two smell-sensing organs.

Group Behaviors for Systems with Significant Dynamics

Birds, fish, and many other animals travel as a flock, school, or herd. Animals in these groups must remain in close proximity while avoiding collisions with neighbors and with obstacles. We would like to reproduce this behavior for groups of simulated creatures traveling fast enough that dynamics plays a significant role in determining their movement. In this paper, we describe an algorithm for controlling the movements of creatures that travel as a group and evaluate the performance of the algorithm with three simulated systems: legged robots, humanlike bicycle riders, and point-mass systems. Both the legged robots and the bicyclists are dynamic simulations that must control balance, facing direction, and forward speed as well as position within the group. The simpler point-mass systems are included because they help us to understand the effects of the dynamics on the performance of the algorithm.