Recent years have seen significant developments in Artificial Intelligence and we are now beginning to see some applications applied to sexual technologies. This has led to certain individuals and groups calling for a ban on the development of artificial sexual companions or “sexbots”. This is largely due to fears that commercialising sex in this way could could re-enforce existing gender inequalities, and sexual objectification. We argue that one possible risk mitigation strategy could be to develop ethical safeguards into sexual robots. In this paper we explore the problem of implementing artificial ethical agents through a review of the relevant literature. We begin by exploring the motivation to the problem, and the science fiction that has inspired it. Following this, the practical, and philosophical implementation challenges are discussed before providing an overview of the current state of the art. The paper concludes by introducing the grand challenges of the field.

Procedural animation systems are capable of synthesising life-like organic motion automatically. However, due to extensive parameterisation, tuning these systems can be very difficult. Not only are there potentially hundreds of interlinked parameters, the resultant animation can be very subjective and the process is difficult to automate effectively. In this paper, we describe a crowd-sourced approach to procedural animation parameter optimisation using genetic algorithms. We test our approach by asking users to interactively rate a population of virtual dolphins to a prescribed behavioural criterion. Our results show that within a few generations a group of users can successfully tune the system towards a desired behaviour. Our secondary motivation is to investigate whether there are differences in animation and behavioural preference between observations made using a standard desktop monitor and those made in virtual reality (VR). We describe a study where users tuned two sets of dolphin animation systems in parallel, one using a normal monitor and another using an Oculus Rift. Our results indicate that being immersed in VR leads to some key differences in preferred behaviour.

Procedural animation systems are capable of synthesising life-like organic motion automatically. However due to extensive parameterisation, tuning these systems can be very difficult. Not only are there potentially hundreds of interlinked parameters, the resultant animation can be very subjective and the process is difficult to automate effectively.In this paper we describe a crowd-sourced approach to procedural animation parameter optimisation using genetic algorithms. We test our approach by asking users to interactively rate a population of virtual dolphins to a prescribed behavioural criteria. Our results show that within a few generations a group of users can successfully tune the system toward a desired behaviour.Our secondary motivation is to investigate if there are differences in animation and behavioural preference between observations made using a standard desktop monitor and those made using Virtual Reality (VR). We describe a study where users tuned two sets of dolphin animation systems in parallel, one using a normal monitor and another using an Oculus Rift. Our results indicate that being immersed in VR leads to some key differences in preferred behaviour.

Although there have been studies demonstrating that users will respond favorably to synthetic companions and team-mates in computer games, there has been little research into how a player's behavior may change when a known non-player character (NPC) assumes a human identity or persona. This is a common scenario in modern computer games, where players interact with NPCs assuming the guise of human characters. To explore this question, an online game was developed in which a human player had a primary objective of surviving against increasingly difficult waves of enemies. As a secondary objective, the player was tasked with protecting an unarmed NPC companion which assumed either a human, or non-human identity, but with identical underlying Artificial Intelligence. The intention was to explore whether the human player would be more or less protective of a synthetic companion simply due to the identity assumed. The results of the study demonstrate that player's behavior does change based on identity, and clearly indicates that the player was more protective of the companion assuming a human identity. Furthermore, the results show that this phenomenon extends beyond simple human and non-human identities, and that the specific persona, or gender of the NPC may influence the player's empathy towards it.

Procedural animation systems are capable of synthesizing vivid organic character motion automatically. However, these systems can consist of hundreds of interlinked parameters, yielding a very large search space of potential animations and emergent behaviours. Parametrised Animation is also notoriously difficult to automatically optimise, as the resultant motion or behaviour can be subtle, complex and subjective. We describe a web-based simulation that enables multiple users to interactively rate the animation of a randomly generated population of virtual creatures based on a prescribed criteria. A record of each individual rating is stored and used to seed subsequent generations, thereby guiding the system towards exhibiting a desired type of motion or behaviour. We start by introducing issues inherent to procedural animation techniques and survey related work. We then describe our prototype implementation that allows multiple users to interactively shape the parameters of a snake-like animation system in real-time. We conclude by discussing the next steps of our research, including the optimisation of procedurally animated dolphins.

Work in progress for the development of a novel virtual training environment for training a kidney biopsy procedure is presented. Our goal is to provide an affordable high fidelity simulation through the integration of some of the latest off-the-shelf technology components. The range of forces that are encountered during this procedure have been recorded using a custom designed force sensitive glove and then applied within the simulation.

In this paper, we propose an approach inspired by Gardenfors conceptual spaces, to represent concepts (specifically colour) in a situated embodied agent. Through this we also extend the work of Braitenberg by proposing an alternate vehicle capable of recognising colour concepts. Furthermore, we demonstrate how it can learn to distinguish between different colours by evolving our vehicle’s conceptual space using Template Based Evolution. Background and motivation Categorisation and the ability to recognise and represent concepts has been described as a fundamental cognitive ability and a primary research goal of AI (Gardenfors and Williams, 2001). The traditional approach from classical AI is to represent concepts symbolically using a top-down design process. The bottom-up subsymbolic (connectionist) approach, in contrast, emphasizes the importance of symbol grounding and the crucial role that the body has to play, by having the agent situated in the world and embodied— able to perceive and interact with the world through sensorymotor co-ordination (Pfeifer and Scheier, 2001). Being able to recognise similarity is key to many biological phenomena, such as how simplistic agents are able to learn what food is safe to eat. Whilst prototypical items are easily represented through the symbolic approach, this does not take into account possible variations that naturally occur in real-life. Take the problem of colour recognition, for example. In natural systems, it is very unlikely to find exactly the same colour—no leaf on a tree is ever the same shade of green, or a berry is ever the same shade of red. Gardenfors (2004) presents an alternative methodology, called Conceptual Spaces which models knowledge in the form of regions in a geometric space made up of quality dimensions. By modeling in this way, Gardenfors argues that an agent is able to recognise variations on a defined concept prototype. A primary motivation for this paper is to see if a Conceptual Spaces representation can be implemented in a situated, embodied agent in a manner analogous to the approach adopted by Pfeifer and Scheier (2001) in order to demonstrate how concepts such as colour can be learnt in an unsupervised way.

ABSTRACT We describe a system for dynamically animating the locomotive behaviour of arthropods (insects, spiders and numerous other species) in real‐time, facilitating realistic and autonomous traversal across an arbitrary environment. By combining a decentralised reactive behavioural model with a hybrid approach to motion that utilises the comparative advantages of physical simulation and kinematic control, our system is capable of automatically generating complex organic motion over a wide range of surface features, independent of structural complexity. The reactive embodiment of the creature, combined with the physical simulation of the virtual world enables the formation of emergent behaviours that are entirely based on circumstance, including rigid‐body interaction, grip recovery and adaptive wall climbing. Copyright © 2012 John Wiley & Sons, Ltd.