Evolving Morphological and Behavioral Diversity Without Predefined Behavior Primitives - Pichler, Peter-Paul; Lola Cañamero (Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems, 2008)
Virtual ecosystems, where natural selection is used to evolve complex agent behavior, are often preferred to traditional genetic algorithms because the absence of an explicitly defined fitness allows for a less constrained evolutionary process. However, these model ecosystems typically pre-specify a discrete set of possible action primitives the agents can perform. We think that this also constrains the evolutionary process with the modellers preconceptions of what possible solutions could be. Therefore, we propose an ecosystem model to evolve complete agents where all higher-level behavior results strictly from the interplay between extremely simple components and where no ‘behavior primitives’ are defined. On the basis of four distinct survival strategies we show that such primitives are not necessary to evolve behavioral diversity even in a simple and homogeneous environment.

Optimal Noise in Spiking Neural Networks for the Detection of Chemicals by Simulated Agents - Oros, Nicolas; Volker Steuber; Davey, Neil; Lola Cañamero; Roderick G Adams (Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems, 2008)
We created a spiking neural controller for an agent that could use two different types of information encoding strategies depending on the level of chemical concentration present in the environment. The first goal of this research was to create a simulated agent that could react and stay within a region where there were two different overlapping chemicals having uniform concentrations. The agent was controlled by a spiking neural network that encoded sensory information using temporal coincidence of incoming spikes when the level of chemical concentration was low, and as firing rates at high level of concentration. With this architecture, we could study synchronization of firing in a simple manner and see its effect on the agent’s behaviour. The next experiment we did was to use a more realistic model by having an environment composed of concentration gradients and by adding input current noise to all neurons. We used a realistic model of diffusive noise and showed that it could improve the agent’s behaviour if used within a certain range. Therefore, an agent with neuronal noise was better able to stay within the chemical concentration than an agent without.

Why Should You Care? An Arousal-Based Model of Exploratory Behavior for Autonomous Robots - Antoine Hiolle; Lola Cañamero (Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems, 2008)
The question of how autonomous robots could be part of our everyday life is of a growing interest. We present here an experiment in which an autonomous robot explores its environment and tries to familiarize itself with the features available using a neural-network-based architecture. The lack of stability of its learning structures increases the arousal level of the robot, pushing the robot to look for comfort from its caretaker to reduce the arousal. In this paper, we studied how the behavior of the caretaker influences the course of the robot exploration and learning experience by providing certain amount of comfort during this exploration. We then draw some conclusions on how to use this architecture together with related work, to enhance the adaptability of autonomous robots development.

The Evolution of Affect-Related Displays, Recognition and Related Strategies - Robert Lowe; Lola Cañamero; Nehaniv, Chrystopher L; Daniel Polani (ALIFE IX: Proceeding of the 9th international conference on the simulation and synthesis of living systems, 2004)
This paper presents an ecologically motivated, bottom-up approach to investigating the evolution of expression, perception and related behaviour of affective internal states that complements game-theoretic studies of the evolutionary success of animal display. Our results show that the perception of displays related to affect greatly influences both the types of display produced and also the survival prospects of agents. Relative to agents that do not perceive rival agent internal state, affect perceivers prosper if the initial environment in which they reside provides numerous opportunities for interaction with other agents and resources. Conversely, where the initial environment with sparse resources does not allow for regular interaction, ability to perceive affect is not as facilitatory to survival. Furthermore, the agents evolve particular display strategies distorting the expression of affect and greatly influencing the proportion of affect perceiving to nonaffect perceiving agents over evolutionary time.

Meaningful Information, Sensor Evolution, and the Temporal Horizon of Embodied Organisms - Nehaniv, Chrystopher L; Daniel Polani; Kerstin Dautenhahn; René te Boekhorst; Lola Cañamero (Artificial Life VIII: Proceedings of the Eighth International Conference on Artificial Life, 2002)
We survey and outline how an agent-centered, information-theoretic approach to meaningful information extending classical Shannon information theory by means of utility measures relevant for the goals of particular agents can be applied to sensor evolution for real and constructed organisms. Furthermore, we discuss the relationship of this approach to the programme of freeing artificial life and robotic systems from reactivity, by describing useful types of information with broader temporal horizon, for signaling, communication, affective grounding, two-process learning, individual learning, imitation and social learning, and episodic experiential information (memories, narrative, and culturally transmitted information).