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Is this robot like me? Links between human and robot personality traits

A relatively unexplored question for human-robot social interaction is whether a robots personality should match that of the human user, or be different in the sense that humans do not want the robot to be like them. In this study, 28 adults interacted individually with a non-humanoid robot that demonstrated two robot behaviour styles (socially interactive, socially ignorant) in a simulated living room situation. Questionnaires assessed the extent to which adult ratings of their own personality traits were similar or different to the two robot behaviours. Results revealed that overall subjects did not view their own personality as similar to either of the two robot behaviour styles. Subjects viewed themselves as having stronger personality characteristics compared to the two robot behaviour styles. Important group differences were found, factors such as subject gender, age and technological experience were important in how subjects viewed their personality as being similar to the robot personality. Design implications for future studies are discussed

Behaviour delay and robot expressiveness in child-robot interactions: a user study on interaction kinesics

This paper presents results of a novel study on interaction kinesics where 18 children interacted with a humanoid child-sized robot called KASPAR. Based on findings in psychology and social sciences we propose the temporal behaviour matching hypothesis which predicts that children will adapt to and match the robots temporal behaviour. Each child took part in six experimental trials involving two games in which the dynamics of interactions played a key part: a body expression imitation game, where the robot imitated expressions demonstrated by the children, and a drumming game where the robot mirrored the childrens drumming. In both games KASPAR responded either with or without a delay. Additionally, in the drumming game, KASPAR responded with or without exhibiting facial/gestural expressions. Individual case studies as well as statistical analysis of the complete sample are presented. Results show that a delay of the robots drumming response lead to larger pauses (with and without robot nonverbal gestural expressions) and longer drumming durations (with nonverbal gestural expressions only). In the imitation game, the robots delay lead to longer imitation eliciting behaviour with longer pauses for the children, but systematic individual differences are observed in regards to the effects on the childrens pauses. Results are generally consistent with the temporal behaviour matching hypothesis, i.e. children adapted the timing of their behaviour, e.g. by mirroring to the robots temporal behaviour.

Grounded Sensorimotor Interaction Histories in an Information Theoretic Metric Space for Robot Ontogeny

We motivate and present a definition of an embodied, grounded individual sensorimotor interaction history, which captures the time-extended behavior characteristics of humans and many animals. We present an architecture that connects temporally extended individual experience with capacity for action, whereby a robot can develop over ontogeny through interaction. Central to this is an information theoretic metric space of sensorimotor experience, which is dynamically constructed and reconstructed as the robot acts. We present results of robotic experiments that establish the predictive efficacy of the space and we show the robot developing the capacity to play the simple interaction game “peekaboo.” A quantitative investigation of the appropriate horizon length of experience for the game reveals the relationship between the length of experience and the cycle time of interaction, and suggests the importance of multiple, and possibly self-adaptive, horizon lengths.

Statistical measures of the structure of genomic sequences: entropy, complexity, and position information.

Identifying regions of DNA with extreme statistical characteristics is an important aspect of the structural analysis of complete genomes. Linguistic methods, mainly based on estimating word frequency, can be used for this as they allow for the delineation of regions of low complexity. Low complexity may be due to biased nucleotide composition, by tandem- or dispersed repeats, by palindrome-hairpin structures, as well as by a combination of all these features. We developed software tools in which various numerical measures of text complexity are implemented, including combinatorial and linguistic ones. We also added Hurst exponent estimate to the software to measure dependencies in DNA sequences. By applying these tools to various functional genomic regions, we demonstrate that the complexity of introns and regulatory regions is lower than that of coding regions, whilst Hurst exponent is larger. Further analysis of promoter sequences revealed that the lower complexity of these regions is associated with long-range correlations caused by transcription factor binding sites.

Improving computational predictions of cis-regulatory binding sites.

The location of cis-regulatory binding sites determine the connectivity of genetic regulatory networks and therefore constitute a natural focal point for research into the many biological systems controlled by such regulatory networks. Accurate computational prediction of these binding sites would facilitate research into a multitude of key areas, including embryonic development, evolution, pharmacogenemics, cancer and many other transcriptional diseases, and is likely to be an important precursor for the reverse engineering of genome wide, genetic regulatory networks. Many algorithmic strategies have been developed for the computational prediction of cis-regulatory binding sites but currently all approaches are prone to high rates of false positive predictions, and many are highly dependent on additional information, limiting their usefulness as research tools. In this paper we present an approach for improving the accuracy of a selection of established prediction algorithms. Firstly, it is shown that species specific optimization of algorithmic parameters can, in some cases, significantly improve the accuracy of algorithmic predictions. Secondly, it is demonstrated that the use of non-linear classification algorithms to integrate predictions from multiple sources can result in more accurate predictions. Finally, it is shown that further improvements in prediction accuracy can be gained with the use of biologically inspired post-processing of predictions.

Cell dormancy in cellular automata

This paper describes a novel implementation of a two-dimensional Cellular Automaton (CA) by introducing a dormant state. An overview of the use of CA’s in the field of Artificial Life reveals that certain crucial aspects of biological realism have been sacrificed in favour of abstraction or have not been considered at all. Conway’s famous “Game of Life” model includes certain fundamental aspects of population dynamics, including the transition from living state to dead state. But even the simplest biological system consists of more stages than the binary state in the Game of Life. The aim of this research is to build an extended CA model of natural biological systems by introducing a dormant state and to investigate the effect of dormancy on simple population dynamics.

Integrating genomic binding site predictions using real-valued meta classifiers

Currently the best algorithms for predicting transcription factor binding sites in DNA sequences are severely limited in accuracy. There is good reason to believe that predictions from different classes of algorithms could be used in conjunction to improve the quality of predictions. In this paper, we apply single layer networks, rules sets, support vector machines and the Adaboost algorithm to predictions from 12 key real valued algorithms. Furthermore, we use a ‘window’ of consecutive results as the input vector in order to contextualise the neighbouring results. We improve the classification result with the aid of under- and over-sampling techniques. We find that support vector machines and the Adaboost algorithm outperform the original individual algorithms and the other classifiers employed in this work. In particular they give a better tradeoff between recall and precision.

SimianWorld - A Study of Social Organisation Using an Artificial Life Model

In studies of social behaviour it is commonly assumed that individual complexity is the origin of intricate social interactions. In primates for example, social complexity is attributed to their intelligence and it is argued by many that the cognitive capacity of primates are especially manifest in the way they regulate their social relationships. Whereas the complex societies of non-human primates are considered to be as a direct result of their cognitive abilities this assumption is not made about social insects. In the absence of certain cognitive abilities their complex societies and structurally sophisticated nests are thought to arise from self-organisation. Since it is unlikely that cognitive capacities are all-or-nothing, usually integrating a range of mechanisms, it is possible that different species use similar cognitive mechanisms resulting in different behavioural outcomes.

Design of spatially extended neural networks for specific applications

The processes and mechanisms of biological neural development provide many powerful insights for the creation of artificial neural systems. Biological neural systems are, in general, much more effective in carrying out tasks such as face recognition and motion detection than artificial neural networks. An important difference between biological and (most) artificial neurons is that biological neurons have extensive treeshaped neurites (axons and dendrites) that are themselves capable of active signal transduction and integration. We present a model, inspired by the processes of neural development, which leads to the growth and formation of neuron-to-neuron connections. The neural architectures created have treeshaped neurites and contain spatial information on branch and synapse positions. Furthermore, we have prototyped a simple but efficient way of simulating signal transduction along neurites using a finite state automaton (FSA). We expect that the combination of our neuronal development method with the FSA that mimics signal transfer provide an efficient and effective tool for exploring the relationship between neural form and network function.