Layered control architectures in natural and artificial systems

We review recent research in robotics and neuroscience with the aim of highlighting some points of agreement and convergence. Specifically, we compare Brooks’ [9] subsumption architecture for robot control with a part of the neuroscience literature that can be interpreted as demonstrating hierarchical control systems in animal brains. We focus first on work that follows the tradition of Hughlings Jackson [23] who, in neuroscience and neuropsychology, is particularly associated with the notion of layered competence. From this perspective we further argue that recent work on the defense system of the rat can be interpreted by analogy to Brooks’ subsumption architecture. An important focus is the role of multiple learning systems in the brain, and of hierarchical learning mechanisms in the rat defense system

Layered control architectures in natural and artificial systems

We review recent research in robotics and neuroscience with the aim of highlighting some points of agreement and convergence. Specifically, we compare Brooks’ [9] subsumption architecture for robot control with a part of the neuroscience literature that can be interpreted as demonstrating hierarchical control systems in animal brains. We focus first on work that follows the tradition of Hughlings Jackson [23] who, in neuroscience and neuropsychology, is particularly associated with the notion of layered competence. From this perspective we further argue that recent work on the defense system of the rat can be interpreted by analogy to Brooks’ subsumption architecture. An important focus is the role of multiple learning systems in the brain, and of hierarchical learning mechanisms in the rat defense system

Layered control architectures in robots and vertebrates

We revieiv recent research in robotics, neuroscience, evolutionary neurobiology, and ethology with the aim of highlighting some points of agreement and convergence. Specifically, we com pare Brooks' (1986) subsumption architecture for robot control with research in neuroscience demonstrating layered control systems in vertebrate brains, and with research in ethology that emphasizes the decomposition of control into multiple, intertwined behavior systems. From this perspective we then describe interesting parallels between the subsumption architecture and the natural layered behavior system that determines defense reactions in the rat. We then consider the action selection problem for robots and vertebrates and argue that, in addition to subsumption- like conflict resolution mechanisms, the vertebrate nervous system employs specialized selection mechanisms located in a group of central brain structures termed the basal ganglia. We suggest that similar specialized switching mechanisms might be employed in layered robot control archi tectures to provide effective and flexible action selection

Artificial Societies of Intelligent Agents

In this thesis we present our work, where we developed artificial societies of intelligent agents, in order to understand and simulate adaptive behaviour and social processes. We obtain this in three parallel ways: First, we present a behaviours production system capable of reproducing a high number of properties of adaptive behaviour and of exhibiting emergent lower cognition. Second, we introduce a simple model for social action, obtaining emergent complex social processes from simple interactions of imitation and induction of behaviours in agents. And third, we present our approximation to a behaviours virtual laboratory, integrating our behaviours production system and our social action model in animats. In our behaviours virtual laboratory, the user can perform a wide variety of experiments, allowing him or her to test the properties of our behaviours production system and our social action model, and also to understand adaptive and social behaviour. It can be accessed and downloaded through the Internet. Before presenting our proposals, we make an introduction to artificial intelligence and behaviour-based systems, and also we give notions of complex systems and artificial societies. In the last chapter of the thesis, we present experiments carried out in our behaviours virtual laboratory showing the main properties of our behaviours production system, of our social action model, and of our behaviours virtual laboratory itself. Finally, we discuss about the understanding of adaptive behaviour as a path for understanding cognition and its evolution

Computational models of learning and beyond: Symmetries of associative learning

The authors propose in this chapter to use abstract algebra to unify different models of theories of associative learning -- as complementary to current psychological, mathematical and computational models of associative learning phenomena and data. The idea is to compare recent research in associative learning to identify the symmetries of behaviour. This approach, a common practice in Physics and Biology, would help us understand the structure of conditioning as opposed to the study of specific linguistic (either natural or formal) expressions that are inherently incomplete and often contradictory

Learning and reversal in the sub-cortical limbic system: a computational model

The basal ganglia are a group of nuclei that signal to and from the cerebral cortex. They play an important role in cognition and in the initiation and regulation of normal motor activity. A range of characteristic motor diseases such as Parkinson's and Huntington's have been associated with the degeneration and lesioning of the dopaminergic neurons that target these regions. The study of dopaminergic activity has numerous benefits from understanding how and what effects neurodegenerative diseases have on behavior to determining how the brain responds and adapts to rewards. The study is also useful in understanding what motivates agents to select actions and do the things that they do. The striatum is a major input structure of the basal ganglia and is a target structure of dopaminergic neurons which originate from the mid brain. These dopaminergic neurons release dopamine which is known to exert modulatory influences on the striatal projections. Action selection and control are involved in the dorsal regions of the striatum while the dopaminergic projections to the ventral striatum are involved in reward based learning and motivation. There are many computational models of the dorsolateral striatum and the basal ganglia nuclei which have been proposed as neural substrates for prediction, control and action selection. However, there are relatively few models which aim to describe the role of the ventral striatal nucleus accumbens and its core and shell sub divisions in motivation and reward related learning. This thesis presents a systems level computational model of the sub-cortical nuclei of the limbic system which focusses in particular, on the nucleus accumbens shell and core circuitry. It is proposed that the nucleus accumbens core plays a role in enabling reward driven motor behaviour by acquiring stimulus-response associations which are used to invigorate responding. The nucleus accumbens shell mediates the facilitation of highly rewarding behaviours as well as behavioural switching. In this model, learning is achieved by implementing isotropic sequence order learning and a third factor (ISO-3) that triggers learning at relevant moments. This third factor is modelled by phasic dopaminergic activity which enables long term potentiation to occur during the acquisition of stimulus-reward associations. When a stimulus no longer predicts reward, tonic dopaminergic activity is generated. This enables long term depression. Weak depression has been simulated in the core so that stimulus-response associations which are used to enable instrumental response are not rapidly abolished. However, comparatively strong depression is implemented in the shell so that information about the reward is quickly updated. The shell influences the facilitation of highly rewarding behaviours enabled by the core through a shell-ventral pallido-medio dorsal pathway. This pathway functions as a feed-forward switching mechanism and enables behavioural flexibility. The model presented here, is capable of acquiring associations between stimuli and rewards and simulating reversal learning. In contrast to earlier work, the reversal is modelled by the attenuation of the previously learned behaviour. This allows for the reinstatement of behaviour to recur quickly as observed in animals. The model will be tested in both open- and closed-loop experiments and compared against animal experiments