Human Minds

Articl

From the social learning theory to a social learning algorithm for global optimization

Traditionally, the Evolutionary Computation (EC) paradigm is inspired by Darwinian evolution or the swarm intelligence of animals. Bandura's Social Learning Theory pointed out that the social learning behavior of humans indicates a high level of intelligence in nature. We found that such intelligence of human society can be implemented by numerical computing and be utilized in computational algorithms for solving optimization problems. In this paper, we design a novel and generic optimization approach that mimics the social learning process of humans. Emulating the observational learning and reinforcement behaviors, a virtual society deployed in the algorithm seeks the strongest behavioral patterns with the best outcome. This corresponds to searching for the best solution in solving optimization problems. Experimental studies in this paper showed the appealing search behavior of this human intelligence-inspired approach, which can reach the global optimum even in ill conditions. The effectiveness and high efficiency of the proposed algorithm has further been verified by comparing to some representative EC algorithms and variants on a set of benchmarks

Evolutionary connectionism: algorithmic principles underlying the evolution of biological organisation in evo-devo, evo-eco and evolutionary transitions

The mechanisms of variation, selection and inheritance, on which evolution by natural selection depends, are not fixed over evolutionary time. Current evolutionary biology is increasingly focussed on understanding how the evolution of developmental organisations modifies the distribution of phenotypic variation, the evolution of ecological relationships modifies the selective environment, and the evolution of reproductive relationships modifies the heritability of the evolutionary unit. The major transitions in evolution, in particular, involve radical changes in developmental, ecological and reproductive organisations that instantiate variation, selection and inheritance at a higher level of biological organisation. However, current evolutionary theory is poorly equipped to describe how these organisations change over evolutionary time and especially how that results in adaptive complexes at successive scales of organisation (the key problem is that evolution is self-referential, i.e. the products of evolution change the parameters of the evolutionary process). Here we first reinterpret the central open questions in these domains from a perspective that emphasises the common underlying themes. We then synthesise the findings from a developing body of work that is building a new theoretical approach to these questions by converting well-understood theory and results from models of cognitive learning. Specifically, connectionist models of memory and learning demonstrate how simple incremental mechanisms, adjusting the relationships between individually-simple components, can produce organisations that exhibit complex system-level behaviours and improve the adaptive capabilities of the system. We use the term “evolutionary connectionism” to recognise that, by functionally equivalent processes, natural selection acting on the relationships within and between evolutionary entities can result in organisations that produce complex system-level behaviours in evolutionary systems and modify the adaptive capabilities of natural selection over time. We review the evidence supporting the functional equivalences between the domains of learning and of evolution, and discuss the potential for this to resolve conceptual problems in our understanding of the evolution of developmental, ecological and reproductive organisations and, in particular, the major evolutionary transitions

Emergent adaptive behaviour of GRN-controlled simulated robots in a changing environment

We developed a bio-inspired robot controller combining an artificial genome with an agent-based control system. The genome encodes a gene regulatory network (GRN) that is switched on by environmental cues and, following the rules of transcriptional regulation, provides output signals to actuators. Whereas the genome represents the full encoding of the transcriptional network, the agent-based system mimics the active regulatory network and signal transduction system also present in naturally occurring biological systems. Using such a design that separates the static from the conditionally active part of the gene regulatory network contributes to a better general adaptive behaviour. Here, we have explored the potential of our platform with respect to the evolution of adaptive behaviour, such as preying when food becomes scarce, in a complex and changing environment and show through simulations of swarm robots in an A-life environment that evolution of collective behaviour likely can be attributed to bio-inspired evolutionary processes acting at different levels, from the gene and the genome to the individual robot and robot population

Robot pain: a speculative review of its functions

Given the scarce bibliography dealing explicitly with robot pain, this chapter has enriched its review with related research works about robot behaviours and capacities in which pain could play a role. It is shown that all such roles ¿ranging from punishment to intrinsic motivation and planning knowledge¿ can be formulated within the unified framework of reinforcement learning.Peer ReviewedPostprint (author's final draft