8 research outputs found
On the Evolutionary Co-Adaptation of Morphology and Distributed Neural Controllers in Adaptive Agents
The attempt to evolve complete embodied and situated artiļ¬cial creatures in which
both morphological and control characteristics are adapted during the evolutionary
process has been and still represents a long term goal key for the artiļ¬cial life and
the evolutionary robotics community.
Loosely inspired by ancient biological organisms which are not provided with a
central nervous system and by simple organisms such as stick insects, this thesis
proposes a new genotype encoding which allows development and evolution of mor-
phology and neural controller in artiļ¬cial agents provided with a distributed neural
network.
In order to understand if this kind of network is appropriate for the evolution of
non trivial behaviours in artiļ¬cial agents, two experiments (description and results
will be shown in chapter 3) in which evolution was applied only to the controllerās
parameters were performed.
The results obtained in the ļ¬rst experiment demonstrated how distributed neural
networks can achieve a good level of organization by synchronizing the output of
oscillatory elements exploiting acceleration/deceleration mechanisms based on local
interactions.
In the second experiment few variants on the topology of neural architecture were
introduced. Results showed how this new control system was able to coordinate the
legs of a simulated hexapod robot on two diļ¬erent gaits on the basis of the external
circumstances.
After this preliminary and successful investigation, a new genotype encoding able to
develop and evolve artiļ¬cial agents with no ļ¬xed morphology and with a distributed
neural controller was proposed. A second set of experiments was thus performed
and the results obtained conļ¬rmed both the eļ¬ectiveness of genotype encoding and
the ability of distributed neural network to perform the given task.
The results have also shown the strength of genotype both in generating a wide
range of diļ¬erent morphological structures and in favouring a direct co-adaptation
between neural controller and morphology during the evolutionary process.
Furthermore the simplicity of the proposed model has showed the eļ¬ective role of
speciļ¬c elements in evolutionary experiments. In particular it has demonstrated the
importance of the environment and its complexity in evolving non-trivial behaviours
and also how adding an independent component to the ļ¬tness function could help
the evolutionary process exploring a larger space solutions avoiding a premature
convergence towards suboptimal solutions
Mental content : consequences of the embodied mind paradigm
The central difference between objectivist cognitivist semantics and embodied cognition consists in the fact that the latter is, in contrast to the former, mindful of binding meaning to context-sensitive mental systems. According to Lakoff/Johnson's experientialism, conceptual structures arise from preconceptual kinesthetic image-schematic and basic-level structures. Gallese and Lakoff introduced the notion of exploiting sensorimotor structures for higherlevel cognition. Three different types of X-schemas realise three types of environmentally embedded simulation: Areas that control movements in peri-personal space; canonical neurons of the ventral premotor cortex that fire when a graspable object is represented; the firing of mirror neurons while perceiving certain movements of conspecifics. ..
A sensory system for robots using evolutionary artificial neural networks.
The thesis presents the research involved with developing an Intelligent Vision System for an animat that can analyse a visual scene in uncontrolled environments. Inspiration was drawn both from Biological Visual Systems and Artificial Image Recognition Systems. Several Biological Systems including the Insect, Toad and Human Visual Systems were studied alongside popular Pattern Recognition Systems such as fully connected Feedforward Networks, Modular Neural Networks and the Neocognitron. The developed system, called the Distributed Neural Network (DNN) was based on the sensory-motor connections in the common toad, Bufo Bufo. The sparsely connected network architecture has features of modularity enhanced by the presence of lateral inhibitory connections. It was implemented using Evolutionary Artificial Neural Networks (EANN). A novel method called FUSION was used to train the DNN, which is an amalgamation of several concepts of learning in Artificial Neural Networks such as Unsupervised Learning, Supervised Learning, Reinforcement Learning, Competitive Learning, Self-organisation and Fuzzy Logic. The DNN has unique feature detecting capabilities. When the DNN was tested using images that comprised of combination of features used in the training set, the DNN was successful in recognising individual features. The combinations of features were never used in the training set. This is a unique feature of the DNN trained using Fusion that cannot be matched by any other popular ANN architecture or training method. The system proved to be robust in dealing with New and Noisy Images. The unique features of the DNN make the network suitable for applications in robotics such as obstacle avoidance and terrain recognition, where the environment is unpredictable. The network can also be used in the field of Medical Imaging, Biometrics (Face and Finger Print Recognition) and Quality Inspection in the Food Processing Industry and applications in other uncontrolled environments
Adaptive networks for robotics and the emergence of reward anticipatory circuits
Currently the central challenge facing evolutionary robotics is to determine
how best to extend the range and complexity of behaviour supported by evolved
neural systems. Implicit in the work described in this thesis is the idea that this
might best be achieved through devising neural circuits (tractable to evolutionary
exploration) that exhibit complementary functional characteristics. We concentrate
on two problem domains; locomotion and sequence learning. For locomotion
we compare the use of GasNets and other adaptive networks. For sequence learning
we introduce a novel connectionist model inspired by the role of dopamine
in the basal ganglia (commonly interpreted as a form of reinforcement learning).
This connectionist approach relies upon a new neuron model inspired by notions
of energy efficient signalling. Two reward adaptive circuit variants were investigated.
These were applied respectively to two learning problems; where action
sequences are required to take place in a strict order, and secondly, where action
sequences are robust to intermediate arbitrary states. We conclude the thesis
by proposing a formal model of functional integration, encompassing locomotion
and sequence learning, extending ideas proposed by W. Ross Ashby.
A general model of the adaptive replicator is presented, incoporating subsystems
that are tuned to continuous variation and discrete or conditional events.
Comparisons are made with Ross W. Ashby's model of ultrastability and his
ideas on adaptive behaviour. This model is intended to support our assertion
that, GasNets (and similar networks) and reward adaptive circuits of the type
presented here, are intrinsically complementary. In conclusion we present some
ideas on how the co-evolution of GasNet and reward adaptive circuits might lead
us to significant improvements in the synthesis of agents capable of exhibiting
complex adaptive behaviour
Volume Signalling in Real and Robot Nervous Systems
This paper presents recent work in computational modelling of diffusing gaseous neuromodulators in biological nervous systems. A variety of interesting and significant properties of such four dimensional neural signalling systems are demonstrated. It is shown that the morphology of the neuromodulator source plays a highly significant role in the diffusion patterns observed. The paper goes on to describe work in adaptive autonomous systems directly inspired by this: an exploration of the use of virtual diffusing modulators in robot nervous systems built from non-standard artificial neural networks. These virtual chemicals act over space and time modulating a variety of node and connection properties in the networks. A wide variety of rich dynamics are possible in such systems; in the work described here, evolutionary robotics techniques have been used to harness the dynamics to produce autonomous behaviour in mobile robots. Detailed comparative analyses of evolutionary searches, and search spaces, for robot controllers with and without the virtual gases are introduced. The virtual diffusing modulators are found to provide significant advantages
The Shifting Network: Volume Signalling in Real and Robot Nervous Systems
This paper presents recent work in computational modelling of diffusing gaseous neuromodulators in biological nervous systems. It goes on to describe work in adaptive autonomous systems directly inspired by this: an exploration of the use of virtual diffusing modulators in robot nervous systems built from non-standard artificial neural networks. These virtual chemicals act over space and time modulating a variety of node and connection properties in the networks. A wide variety of rich dynamics are possible in such systems; in the work described here, evolutionary robotics techniques have been used to harness the dynamics to produce autonomous behaviour in mobile robots. Detailed comparative analyses of evolutionary searches, and search spaces, for robot controllers with and without the virtual gases are introduced. The virtual diffusing modulators are found to provide significant advantages