8,713 research outputs found
Computational tasks in robotics and factory automation
The design of Manufacturing Planning and Control Systems (MPCSs) — systems that negotiate with Customers and Suppliers to exchange products in return for money in order to generate profit, is discussed.\ud
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The computational task of MPCS components are systematically specified as a starting point for the development of computational engines, as computer systems and programs, that execute the specified computation. Key issues are the overwhelming complexity and frequently changing application of MPCSs
Evolvability signatures of generative encodings: beyond standard performance benchmarks
Evolutionary robotics is a promising approach to autonomously synthesize
machines with abilities that resemble those of animals, but the field suffers
from a lack of strong foundations. In particular, evolutionary systems are
currently assessed solely by the fitness score their evolved artifacts can
achieve for a specific task, whereas such fitness-based comparisons provide
limited insights about how the same system would evaluate on different tasks,
and its adaptive capabilities to respond to changes in fitness (e.g., from
damages to the machine, or in new situations). To counter these limitations, we
introduce the concept of "evolvability signatures", which picture the
post-mutation statistical distribution of both behavior diversity (how
different are the robot behaviors after a mutation?) and fitness values (how
different is the fitness after a mutation?). We tested the relevance of this
concept by evolving controllers for hexapod robot locomotion using five
different genotype-to-phenotype mappings (direct encoding, generative encoding
of open-loop and closed-loop central pattern generators, generative encoding of
neural networks, and single-unit pattern generators (SUPG)). We observed a
predictive relationship between the evolvability signature of each encoding and
the number of generations required by hexapods to adapt from incurred damages.
Our study also reveals that, across the five investigated encodings, the SUPG
scheme achieved the best evolvability signature, and was always foremost in
recovering an effective gait following robot damages. Overall, our evolvability
signatures neatly complement existing task-performance benchmarks, and pave the
way for stronger foundations for research in evolutionary robotics.Comment: 24 pages with 12 figures in the main text, and 4 supplementary
figures. Accepted at Information Sciences journal (in press). Supplemental
videos are available online at, see http://goo.gl/uyY1R
Probabilistic thread algebra
We add probabilistic features to basic thread algebra and its extensions with
thread-service interaction and strategic interleaving. Here, threads represent
the behaviours produced by instruction sequences under execution and services
represent the behaviours exhibited by the components of execution environments
of instruction sequences. In a paper concerned with probabilistic instruction
sequences, we proposed several kinds of probabilistic instructions and gave an
informal explanation for each of them. The probabilistic features added to the
extension of basic thread algebra with thread-service interaction make it
possible to give a formal explanation in terms of non-probabilistic
instructions and probabilistic services. The probabilistic features added to
the extensions of basic thread algebra with strategic interleaving make it
possible to cover strategies corresponding to probabilistic scheduling
algorithms.Comment: 25 pages (arXiv admin note: text overlap with arXiv:1408.2955,
arXiv:1402.4950); some simplifications made; substantially revise
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