Högskolan i Skövde

The interaction of brain, body, and environment can result incomplex behavior with rich dynamics, even for relatively simpleagents. Such dynamics are, however, often difficult to analyze.In this article, we explore the case of a simple simulated roboticagent, equipped with a reactive neurocontroller and an energylevel, which the agent has been evolved to recharge. A dynamicalsystems analysis shows that a non-neural internal state (energylevel), despite its simplicity, dynamically modulates the behavioralattractors of the agent—environment system, such thatthe robot's behavioral repertoire is continually adapted toits current situation and energy level. What emerges is a dynamic,non-deterministic, and highly self-organized action selectionmechanism, originating from the dynamical coupling of four systems(non-neural internal states, neurocontroller, body, and environment)operating at very different timescales.

With the present study we report the first application of a recently proposed model for realistic microbial fuel cells (MFCs) energy generation dynamics, suitable for robotic simulations with minimal and extremely limited computational overhead. A simulated agent was adapted in order to engage in a viable interaction with its environment. It achieved energy autonomy by maintaining viable levels of the critical variables of MFCs, namely cathodic hydration and anodic substrate biochemical energy. After unsupervised adaptation by genetic algorithm, these crucial variables modulate the behavioral dynamics expressed by viable robots in their interaction with the environment. The analysis of this physically rooted and self-organized dynamic action selection mechanism constitutes a novel practical contribution of this work. We also compare two different viable strategies, a self-organized continuous and a pulsed behavior, in order to foresee the possible cognitive implications of such biologicalmechatronics hybrid symbionts in a novel scenario of ecologically grounded energy and motivational autonomy.

Starting from the situated and embodied perspective on the study of cognition as a source of inspiration, this paper programmatically outlines a path towards an experimental exploration of the role of the body in a minimal anticipatory cognitive architecture. Cognition is here conceived and synthetically analyzed as a broadly extended and distributed dynamic process emerging from the interplay between a body, a nervous system and their environment. Firstly, we show how a non-neural internal state, crucially characterized by slowly changing dynamics, can modulate the activity of a simple neurocontroller. The result, emergent from the use of a standard evolutionary robotic simulation, is a selforganized, dynamic action selection mechanism, effectively operating in a context dependent way. Secondly, we show how these characteristics can be exploited by a novel minimalist anticipatory cognitive architecture. Rather than a direct causal connection between the anticipationprocess and the selection of the appropriate behavior, it implements a model for dynamic anticipation that operates via bodily mediation (bodily-anticipation hypothesis). This allows the system to swiftly scale up to more complex tasks never experienced before, achieving flexible and robust behavior with minimal adaptive cost.

The coupling between a body (in an extended sense that encompasses  both neural and non-neural dynamics) and its environment is here conceived as a critical substrate for cognition. We propose and discuss the plan for a neurocomputational cognitive architecture for robotic agents, so far implemented in its minimal form for supporting the behavior of a simple simulated robotic agent. A non-neural internal bodily mechanism (crucially characterized by a time scale much slower than the normal sensory-motor interactions of the robot with its environment) extends the cognitive potential of a system composed of purely reactive parts with a dynamic action selection mechanism and the capacity to integrate information over time. The same non-neural mechanism is the foundation for a novel, minimalist anticipatory architecture, implementing our bodily-anticipation hypothesis and capable of swift re-adaptation to related yet novel tasks.