Towards Verifiably Ethical Robot Behaviour

Ensuring that autonomous systems work ethically is both complex and difficult. However, the idea of having an additional `governor' that assesses options the system has, and prunes them to select the most ethical choices is well understood. Recent work has produced such a governor consisting of a `consequence engine' that assesses the likely future outcomes of actions then applies a Safety/Ethical logic to select actions. Although this is appealing, it is impossible to be certain that the most ethical options are actually taken. In this paper we extend and apply a well-known agent verification approach to our consequence engine, allowing us to verify the correctness of its ethical decision-making.Comment: Presented at the 1st International Workshop on AI and Ethics, Sunday 25th January 2015, Hill Country A, Hyatt Regency Austin. Will appear in the workshop proceedings published by AAA

Soft-decision minimum-distance sequential decoding algorithm for convolutional codes

The maximum-likelihood decoding of convolutional codes has generally been considered impractical for other than relatively short constraint length codes, because of the exponential growth in complexity with increasing constraint length. The soft-decision minimum-distance decoding algorithm proposed in the paper approaches the performance of a maximum-likelihood decoder, and uses a sequential decoding approach to avoid an exponential growth in complexity. The algorithm also utilises the distance and structural properties of convolutional codes to considerably reduce the amount of searching needed to find the minimum soft-decision distance paths when a back-up search is required. This is done in two main ways. First, a small set of paths called permissible paths are utilised to search the whole of the subtree for the better path, instead of using all the paths within a given subtree. Secondly, the decoder identifies which subset of permissible paths should be utilised in a given search and which may be ignored. In this way many unnecessary path searches are completely eliminated. Because the decoding effort required by the algorithm is low, and the decoding processes are simple, the algorithm opens the possibility of building high-speed long constraint length convolutional decoders whose performance approaches that of the optimum maximum-likelihood decoder. The paper describes the algorithm and its theoretical basis, and gives examples of its operation. Also, results obtained from practical implementations of the algorithm using a high-speed microcomputer are presented

Modelling a wireless connected swarm of mobile robots

It is a characteristic of swarm robotics that modelling the overall swarm behaviour in terms of the low-level behaviours of individual robots is very difficult. Yet if swarm robotics is to make the transition from the laboratory to real-world engineering realisation such models would be critical for both overall validation of algorithm correctness and detailed parameter optimisation. We seek models with predictive power: models that allow us to determine the effect of modifying parameters in individual robots on the overall swarm behaviour. This paper presents results from a study to apply the probabilistic modelling approach to a class of wireless connected swarms operating in unbounded environments. The paper proposes a probabilistic finite state machine (PFSM) that describes the network connectivity and overall macroscopic behaviour of the swarm, then develops a novel robot-centric approach to the estimation of the state transition probabilities within the PFSM. Using measured data from simulation the paper then carefully validates the PFSM model step by step, allowing us to assess the accuracy and hence the utility of the model. © Springer Science + Business Media, LLC 2008

Robot narratives

There is evidence that humans understand how the world goes through narrative. We discuss what it might mean for embodied robots to understand the world, and communicate that understanding, in a similar manner. We suggest an architecture for adding narrative to robot cognition, and an experimental scenario for investigating the narrative hypothesis in a combination of physical and simulated robots

Safety in Numbers: Fault Tolerance in Robot Swarms

The swarm intelligence literature frequently asserts that swarms exhibit high levels of robustness. That claim is, however, rather less frequently supported by empirical or theoretical analysis. But what do we mean by a 'robust' swarm? How would we measure the robustness or – to put it another way – fault-tolerance of a robotic swarm? These questions are not just of academic interest. If swarm robotics is to make the transition from the laboratory to real-world engineering implementation, we would need to be able to address these questions in a way that would satisfy the needs of the world of safety certification. This paper explores fault-tolerance in robot swarms through Failure Mode and Effect Analysis (FMEA) and reliability modelling. The work of this paper is illustrated by a case study of a wireless connected robot swarm, employing both simulation and real-robot laboratory experiments

Experiments in artificial culture: from noisy imitation to storytelling robots

This paper presents a series of experiments in collective social robotics, spanning more than 10 years, with the long-term aim of building embodied models of (aspects) of cultural evolution. Initial experiments demonstrated the emergence of behavioural traditions in a group of social robots programmed to imitate each other's behaviours (we call these Copybots). These experiments show that the noisy (i.e. less than perfect fidelity) imitation that comes for free with real physical robots gives rise naturally to variation in social learning. More recent experimental work extends the robots' cognitive capabilities with simulation-based internal models, equipping them with a simple artificial theory of mind. With this extended capability we explore, in our current work, social learning not via imitation but robot-robot storytelling, in an effort to model this very human mode of cultural transmission. In this paper we give an account of the methods and inspiration for these experiments,the experiments and their results, and an outline of possible directions for this programme of research. It is our hope that this paper stimulates not only discussion but suggestions for hypotheses to test with the Storybots

Towards a comprehensive taxonomy for characterizing robots

Every day a new robot is developed with advanced characteristics and technical qualities. The increasingly rapid growth of robots and their characteristics demands bridging between the application requirements and the robot specifications. This process requires a supporting conceptual structure that can capture as many robot qualities as possible. Presenting robot characteristics through the proposed conceptual structure would enable designers to optimize robot capabilities against application requirements. It would also help application developers to select the most appropriate robot. Without a formal structure, an accurate linking between the robot domain and the application domain is not possible. This paper presents a novel theoretical representation that can capture robot features and capabilities and express them as descriptive dimensions to be used to develop a capability profile. The profile is intended to unify robot description and presentation. The proposed structure is reinforced with several layers, sections, categorizations and levels to allow a detailed explanation of robot characteristics. It is hoped that the proposed structure will influence the design, development, and testing of robots for specific applications. At the same time, it would help in highlighting the corresponding outlines in robot application requirements

On embodied memetic evolution and the emergence of behavioural traditions in Robots

This paper describes ideas and initial experiments in embodied imitation using e-puck robots, developed as part of a project whose aim is to demonstrate the emergence of artificial culture in collective robot systems. Imitated behaviours (memes) will undergo variation because of the noise and heterogeneities of the robots and their sensors. Robots can select which memes to enact, and-because we have a multi-robot collective-memes are able to undergo multiple cycles of imitation, with inherited characteristics. We thus have the three evolutionary operators: variation, selection and inheritance, and-as we describe in this paper-experimental trials show that we are able to demonstrate embodied movement-meme evolution. © 2011 Springer-Verlag