426 research outputs found
Fusing novelty and surprise for evolving robot morphologies
Traditional evolutionary algorithms tend to converge to a single
good solution, which can limit their chance of discovering more
diverse and creative outcomes. Divergent search, on the other hand,
aims to counter convergence to local optima by avoiding selection
pressure towards the objective. Forms of divergent search such as
novelty or surprise search have proven to be beneficial for both
the efficiency and the variety of the solutions obtained in deceptive
tasks. Importantly for this paper, early results in maze navigation
have shown that combining novelty and surprise search yields an
even more effective search strategy due to their orthogonal nature.
Motivated by the largely unexplored potential of coupling novelty
and surprise as a search strategy, in this paper we investigate how
fusing the two can affect the evolution of soft robot morphologies.
We test the capacity of the combined search strategy against objective,
novelty, and surprise search, by comparing their efficiency and
robustness, and the variety of robots they evolve. Our key results
demonstrate that novelty-surprise search is generally more efficient
and robust across eight different resolutions. Further, surprise
search explores the space of robot morphologies more broadly than
any other algorithm examined.peer-reviewe
Characterization and cartography of some Mediterranean soft-bottom benthic communities (Ligurian Sea, Italy)
Soft-bottom benthic communities were studied along the Western coast of the Ligurian Sea with a new approach using both videocamera surveys and collected samples. The preliminary distribution of soft-bottoms and the definition of the limits and status of seagrass beds were carried out in September 1991, using an underwater vehicle provided with a videocamera and towed by a boat. Moreover, 90 benthic samples were collected at 5-40 m depth in order to characterize the macrobenthic soft-bottom communities. Six soft-bottom benthic assemblages and two sea grass biotopes (Cymodocea nodosa and Posidonia oceanica) were revealed by means of underwater images and multivariate analysis (TWINSPAN) on samples collected. The communities inhabiting the infralittoral sandy and coarse sediments corresponded to those previously described in the Mediterranean Sea, whereas a large complex transition between sandy and muddy communities was recognized on circalittoral soft-bottoms. Information obtained with this approach was used to draw a map of the investigated areas at 1:10,000 scale. The employment of the two techniques was cost effective for both time and research effort
Surprise search : beyond objectives and novelty
Grounded in the divergent search paradigm and inspired
by the principle of surprise for unconventional discovery
in computational creativity, this paper introduces surprise
search as a new method of evolutionary divergent search.
Surprise search is tested in two robot navigation tasks and
compared against objective-based evolutionary search and
novelty search. The key findings of this paper reveal that
surprise search is advantageous compared to the other two
search processes. It outperforms objective search and it is
as efficient as novelty search in both tasks examined. Most
importantly, surprise search is, on average, faster and more
robust in solving the navigation problem compared to ob-
jective and novelty search. Our analysis reveals that sur-
prise search explores the behavioral space more extensively
and yields higher population diversity compared to novelty
search.This work has been supported in part by the FP7 Marie
Curie CIG project AutoGameDesign (project no: 630665).
The authors would also like to thank Dora Lee Borg for
initial implementations of the algorithm.peer-reviewe
Constrained surprise search for content generation
In procedural content generation, it is often desirable
to create artifacts which not only fulfill certain playability
constraints but are also able to surprise the player with unexpected
potential uses. This paper applies a divergent evolutionary
search method based on surprise to the constrained problem of
generating balanced and efficient sets of weapons for the Unreal
Tournament III shooter game. The proposed constrained surprise
search algorithm ensures that pairs of weapons are sufficiently
balanced and effective while also rewarding unexpected uses of
these weapons during game simulations with artificial agents.
Results in the paper demonstrate that searching for surprise can
create functionally diverse weapons which require new gameplay
patterns of weapon use in the game.This work has been supported, in part, by the FP7 Marie
Curie CIG project AutoGameDesign (project no: 630665) and
the Horizon 2020 project CrossCult (project no: 693150).peer-reviewe
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LASS hardware processor
The problems of data analysis with hardware processors are reviewed, and a description is given for a programmable processor. This processor, the 168/E, was designed for use in the LASS multi-processor system; it has an execution speed comparable to that of the IBM 370/168 and uses the subset of IBM 370 instructions appropriate to the LASS analysis task. 2 figures, 2 tables
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