Robot spatial learning: Insights from animal and human behaviour

Robotics research could benefit by looking for clues in the understanding of natural systems for the design of artificial systems. The literature on spatial learning suggests that there is a great diversity of solutions to the problem of learning and navigating a large-scale system. There are characteristic properties that extend across species that may be applicable for designing autonomous mobile robots. Natural navigation systems have a common trait relating to the use of multiple subsystems for the control of behavior and the exploitation of dynamic, quantitative representations of space

The AI singularity and runaway human intelligence

There is increasing discussion of the possibility of AI being developed to a point where it reaches a "singularity" beyond which it will continue to improve in a runaway fashion without human help. Worst-case scenarios suppose that, in the future, homo sapiens might even be replaced by intelligent machines as the dominant "species" on our planet. This paper argues that the standard argument for the AI singularity is based on an inappropriate comparison of advanced AI to average human intelligence, arguing instead that progress in AI should be measured against the collective intelligence of the global community of human minds brought together and enhanced be smart technologies that include AI. By this argument, AI as a separate entity, is unlikely to surpass "runaway" human (or, perhaps, posthuman) intelligence whose continued advance, fueled by scientific and cultural feedback, shows no sign of abating. An alternative scenario is proposed that human collective intelligence will take an increasingly biohybrid form as we move towards a greater, deeper and more seamless integration with our technology. © 2013 Springer-Verlag Berlin Heidelberg

Sunny uplands or slippery slopes? The risks and benefits of using robots in care

This paper considers some of the ethical issues around the use of robots in caring for older people and in childcare. I argue that the debate on the use of robots in care has involved slippery slope arguments for which the likelihood of progression to worst-case outcomes needs more thorough analysis. In older care, the risk of social isolation of older people through use of care robots is indirect and may have been overstated; similarly, in childcare, the risk of psychological damage to children, through irresponsible use of robots, must be balanced against the potential positive benefits of these technologies if used appropriatel

Connectionist simulation of attitude learning: Asymmetries in the acquisition of positive and negative evaluations

Connectionist computer simulation was employed to explore the notion that, if attitudes guide approach and avoidance behaviors, false negative beliefs are likely to remain uncorrected for longer than false positive beliefs. In Study 1, the authors trained a three-layer neural network to discriminate "good" and "bad" inputs distributed across a two-dimensional space. "Full feedback" training, whereby connection weights were modified to reduce error after every trial, resulted in perfect discrimination. "Contingent feedback," whereby connection weights were only updated following outputs representing approach behavior, led to several false negative errors (good inputs misclassified as bad). In Study 2, the network was redesigned to distinguish a system for learning evaluations from a mechanism for selecting actions. Biasing action selection toward approach eliminated the asymmetry between learning of good and bad inputs under contingent feedback. Implications for various attitudinal phenomena and biases in social cognition are discussed

Almost a decade of Cognitive Science at Sheffield

Sheffield was one of the first UK universities to introduce an undergraduate degree in Cognitive Science with an initial intake of students in 1990. The authors have been involved with teaching, admininistering, and developing the degree throughout the 1990s and most recently in overseeing its transformation into a degree entitled "Psychology and Cognitive Science". This paper provides a case-study of our experience in developing and co-ordinating Cognitive Science teaching at Sheffield. We review some of the particular problems we have faced, assess our varied attempts at solving them, and identify some unresolved issues which are likely to be faced by anyone seeking to provide training in Cognitive Science at an undergraduate level

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

The early evolution of spatial behaviour: robot models of trace fossils

The evolutionary history of nervous systems can provide useful insights for biologically-inspired robot design. The study of trace fossils, the fossilised remains of animal behaviour, reveals interesting parallels with recent research in behaviour-based robotics. This paper reports robot simulations of the meandering foraging trails left by early invertebrates which demonstrate that such trails can be generated by mechanisms similar to those used for robot wall-following. We conclude with the tentative suggestion that the capacity for intelligent behaviour shown by current behaviour-based robots is similar to that of animals of the early Cambrian period approximately 530-544 million years ago

Individual Differences and Biohybrid Societies

Contemporary robot design is influenced both by task domain (e.g., industrial manipulation versus social interaction) as well as by classification differences in humans (e.g., therapy patients versus museum visitors). As the breadth of robot use increases, we ask how will people respond to the ever increasing number of intelligent artefacts in their environment. Using the Paro robot as our case study we propose an analysis of individual differences in HRI to highlight the consequences individual characteristics have on robot performance. We discuss to what extent human-human interactions are a useful model of HRI