Application of multiobjective genetic programming to the design of robot failure recognition systems

We present an evolutionary approach using multiobjective genetic programming (MOGP) to derive optimal feature extraction preprocessing stages for robot failure detection. This data-driven machine learning method is compared both with conventional (nonevolutionary) classifiers and a set of domain-dependent feature extraction methods. We conclude MOGP is an effective and practical design method for failure recognition systems with enhanced recognition accuracy over conventional classifiers, independent of domain knowledge

Evolving robot software and hardware

This paper summarizes the keynote I gave on the SEAMS 2020 conference. Noting the power of natural evolution that makes living systems extremely adaptive, I describe how artificial evolution can be employed to solve design and optimization problems in software. Thereafter, I discuss the Evolution of Things, that is, the possibility of evolving physical artefacts and zoom in on a (r)evolutionary way of creating 'bodies' and 'brains' of robots for engineering and fundamental research

Back to the Future of the Body

What can the past tell us about the future(s) of the body? The origins of this collection of papers lie in the work of the Birkbeck Institute for the Humanities which has been involved in presenting a series of international workshops and conferences on the theme of the cultural life of the body. The rationale for these events was that, in concepts as diverse as the cyborg, the questioning of mind/body dualism, the contemporary image of the suicide bomber and the patenting of human genes, we can identify ways in which the future of the human body is at stake. This volume represents an attempt, not so much to speculate about what might happen, but to develop strategies for bodily empowerment so as to get “back to the future of the body”. The body, it is contended, is not to be thought of as an “object” or a “sign” but as an active participant in the shaping of cultural formations. And this is emphatically not an exercise in digging corpses out of the historical archive. The question is, rather, what can past lived and thought experiences of the body tell us about what the body can be(come)? Dominic Janes edited this book and contributed this chapter

Adaptive information and animal behaviour: Why motorists stop at red traffic lights

Argues that information, in the animal behaviour or evolutionary context, is correlation/covariation. The alternation of red and green traffic lights is information because it is (quite strictly) correlated with the times when it is safe to drive through the intersection; thus driving in accordance with the lights is adaptive (causative of survival). Daylength is usefully, though less strictly, correlated with the optimal time to breed. Information in the sense of covariance implies what is adaptive; if an animal can infer what the information implies, it increases its chances of survival

On Fodor on Darwin on Evolution

Jerry Fodor argues that Darwin was wrong about "natural selection" because (1) it is only a tautology rather than a scientific law that can support counterfactuals ("If X had happened, Y would have happened") and because (2) only minds can select. Hence Darwin's analogy with "artificial selection" by animal breeders was misleading and evolutionary explanation is nothing but post-hoc historical narrative. I argue that Darwin was right on all counts. Until Darwin's "tautology," it had been believed that either (a) God had created all organisms as they are, or (b) organisms had always been as they are. Darwin revealed instead that (c) organisms have heritable traits that evolved across time through random variation, with survival and reproduction in (changing) environments determining (mindlessly) which variants were successfully transmitted to the next generation. This not only provided the (true) alternative (c), but also the methodology for investigating which traits had been adaptive, how and why; it also led to the discovery of the genetic mechanism of the encoding, variation and evolution of heritable traits. Fodor also draws erroneous conclusions from the analogy between Darwinian evolution and Skinnerian reinforcement learning. Fodor’s skepticism about both evolution and learning may be motivated by an overgeneralization of Chomsky’s “poverty of the stimulus argument” -- from the origin of Universal Grammar (UG) to the origin of the “concepts” underlying word meaning, which, Fodor thinks, must be “endogenous,” rather than evolved or learned