NUI Galway – UL Alliance First Annual ENGINEERING AND - ARAN ...

Evolving a Robust Open-Ended Language
Brendan Fahy and Colm O’Riordan
CIRG, Department of Information Technology, College of Engineering and Informatics
b.fahy1@nuigalway.ie, colm.oriordan@nuigalway.ie
Abstract
Artificial life is the study of life-like characteristics in
artificially-created systems, such as robots and
computer programs, in order to increase our
understanding of how nature works, and to optimize
performance of artificial systems. Using word-meaning
pairs, simulated agents can evolve an ability to
communicate using a shared lexicon. This language
will complexify to match increasing complexity in the
environment, and can complexify in an open-ended
manner even without a corresponding increase in
complexity of the environment.
1. Introduction
New computational and problem-solving paradigms
can be discovered by the study of natural life systems
and complex dynamical systems[1].
Much research has been done on self-complexifying
algorithms [2] and evolution of language using genetic
algorithms and neural networks [3, 4]. Communication
is a key part of any social interaction, and language is a
key part of communication. Self-complexifying
algorithms become capable of more advanced behavior
as they evolve. In order for robots to be able to
communicate about new artefacts which they have
never encountered before, their language needs to be
able to evolve and grow in an open-ended manner.
Artefacts in this sense can refer to objects in the
environment or events which may come about as a
result of social interaction between agents.
Language itself is never static. Human languages are
constantly in being adapted and updated, flushing
antiquated and unused material and adopting new and
more relevant elements.
2. A Shared Lexicon – Words and
Meanings
The language is evolved using word-meaning pairs
as genes. There are no absolute “right” or “wrong”
word-meaning pairings. If two individuals in the
population have the same word for the same meaning,
then they can communicate this word to each other
successfully. If they have different meanings for the
same word, the hearer will interpret the message
incorrectly. The establishment of a working shared
lexicon depends on the pairing of specific words with
the same meanings across the population.
Dynamically generating words is trivial; a word is
merely a signal. As long as it is shared, it works,
whatever it is. Achieving meaningful representations of
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meanings is more difficult. A pre-defined list of
meanings has no room for growth at runtime. Meanings
must be represented in a way that allows them to be
dynamically created and interpreted by the program.
3. Current Work
Simulations have thus far shown that when selective
pressure is placed on both the ability to speak and the
equal ability to hear and understand signals, a shared
concise lexicon will evolve and propagate through all
individuals in the population. We have also shown that
a random population seeded by some members using a
lexicon will most likely use that seeded solution when
final convergence occurs. Thresholds have been
observed at which success of the seed is guaranteed. A
robust lexicon is unaffected by invasion from randomly
created individuals and other established lexica, to an
existing point at which creoles (hybrid languages) often
start to emerge.
Running the simulations in an n-dimensional grid
world allows the language to evolve word-meaning
pairs for each dimension, so that they can communicate
about the new complexities in the environment. Given
enough time the language can complexify indefinitely
to match the number of dimensions.
4. Future Work
The next step is to achieve a level of open-ended
evolution that is not constrained by the level of
complexity of the environment. In this case, the social
interactions between the agents in the population
become the new artefacts to be added to the language.
5. References
[1] Steels, L., Language as a Complex Adaptive System, in
Parallel Problem Solving from Nature PPSN VI, M.
Schoenauer, et al., Editors. 2000, Springer Berlin /
Heidelberg. p. 17-26.
[2] Stanley, K.O. and R. Miikkulainen, Evolving Neural
Networks through Augmenting Topologies. Evolutionary
Computation, 2002. 10(2): p. 99-127.
[3]Cangelosi, A. and D. Parisi, The emergence of a
"language" in an evolving population of neural networks.
1998.
[4]Werner, G. and M. Dyer. Evolution of Communication in
Artificial Organisms. in Artificial Life II. 1992: Addison-
Wesley Pub.