In this paper, we describe two sets of experiments with small groups of real robots, conducted over the course of more than 10 years, in the Bristol Robotics Lab. The long-term aim of these ongoing experiments is to explore aspects of the question'how do we have culture?' in a new way, by modelling the low-level processes and mechanisms of cultural evolution with robots. In this paper we adopt Mesoudi's definition of culture: 'information that is acquired from other individuals via social transmission mechanisms such as imitation, teaching or language' [1]. We outline two sets of experiments--the first already completed and the second in preparation--with a focus on two of these transmission mechanisms: imitation and language. The first set of experiments we describe were directly inspired by the thought experiment in [2, p. 106], which imagines a group of robots capable of imitating each other. Referred to as Copybots, their ability to imitate actions with variation makes them very simple meme machines. Another source of inspiration was Gabriel Tarde who proposed'a remarkable sociological research project' [3] when he wrote If we wish to make sociology a truly experimental science and stamp it with the seal of exactness, we must, I believe … write out with the greatest care and in the greatest possible detail the succession of minute transformations in the political or industrial world, or some other sphere of life, … in (our) native town or village, beginning in (our) own immediate surroundings (quoted in [3, p. 511]). A second and more recent set of experiments extends our robots' cognitive capabilities with simulation-based internal models. A simulation-based internal model (literally a robot with a simulation of itself, inside itself), allows a robot to be able to ask itself'what if' questions. This capability has been described as a functional imagination [4], as it enables a robot to'imagine' the consequences of its actions (and--in our implementation--the reaction of others to those actions). Our experimental implementation of a simulation-based internal model, which we refer to as a consequence engine (CE), has proven to be remarkably powerful. Our experiments with the CE were inspired by both the simulation theory of cognition [5,6] and Dennett's'Tower of Generate-and-Test' [7].

Abstract: This paper surveys the state-of-the-art in machine ethics, that is, considerations of how to implement ethical behaviour in robots, unmanned autonomous vehicles, or software systems. The emphasis is on covering the breadth of ethical theories being considered by implementors, as well as the implementation techniques being used. There is no consensus on which ethical theory is best suited for any particular domain, nor is there any agreement on which technique is best placed to implement a particular theory. Another unresolved problem in these implementations of ethical theories is how to objectively validate the implementations. The paper discusses the dilemmas being used as validating'whetstones' and whether any alternative validation mechanism exists. Finally, it speculates that an intermediate step of creating domain-specific ethics might be a possible stepping stone towards creating machines that exhibit ethical behaviour. Computers are increasingly a part of the socio-technical systems around us. Domains such as smartgrids, cloud computing, healthcare, and transport are but some examples where computers are deeply embedded. The speed and complexity of decision-making in these domains have meant that humans are ceding more and more autonomy to these computers (Nallur & Clarke 2018). Autonomy, in machines, can be defined as the effective decision-making power over goals, that influences some action in the real-world. For instance, smart traffic lights can autonomically change their timings, depending on the flow and density of traffic on the roads.

Last month, a team of self-taught AI gamers lost spectacularly against human professionals in a highly-anticipated galactic melee. Taking place as part of the International Dota 2 Championships in Vancouver, Canada, the game showed that in broader strategic thinking and collaboration, humans still remain on top. The AI was a series of algorithms developed by the Elon Musk-backed non-profit OpenAI. Collectively dubbed the OpenAI Five, the algorithms use reinforcement learning to teach themselves how to play the game--and collaborate with each other--from scratch. Unlike chess or Go, the fast-paced multi-player Dota 2 video game is considered much harder for computers.

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.