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Many systems proposed for the implementation of ethical reasoning involve an encoding of user values as a set of rules or a model. We consider the question of how changes of context affect these encodings. We propose the use of a reasoning cycle, in which information about the ethical reasoner's context is imported in a logical form, and we propose that context-specific aspects of an ethical encoding be prefaced by a guard formula. This guard formula should evaluate to true when the reasoner is in the appropriate context and the relevant parts of the reasoner's rule set or model should be updated accordingly. This architecture allows techniques for the model-checking of agent-based autonomous systems to be used to verify that all contexts respect key stakeholder values. We implement this framework using the hybrid ethical reasoning agents system (HERA) and the model-checking agent programming languages (MCAPL) framework.

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Robotic systems consisting of a neuron culture grown on a multielectrode array (MEA) which is connected to a virtual or mechanical robot have been studied for approximately 15 years. It is hoped that these MEA-based robots will be able to address the problem that robots based on conventional computer technology are not very good at adapting to surprising or unusual situations, at least not when compared to biological organisms. It is also hoped that insights gained from MEA-based robotics can have applications within human enhancement and medicine. In this paper, I argue that researchers within this field risk overstating their results by not paying enough attention to fundamental challenges within the field. In particular, I investigate three problems: the coding problem, the embodiment problem and the training problem. I argue that none of these problems have been solved and that they are not likely to be solved within the field. After that, I discuss whether MEA-based robotics should be considered pop science. Finally, I investigate the ethical aspects of this research.