46,899 research outputs found
Bio-linguistic transition and Baldwin effect in an evolutionary naming-game model
We examine an evolutionary naming-game model where communicating agents are
equipped with an evolutionarily selected learning ability. Such a coupling of
biological and linguistic ingredients results in an abrupt transition: upon a
small change of a model control parameter a poorly communicating group of
linguistically unskilled agents transforms into almost perfectly communicating
group with large learning abilities. When learning ability is kept fixed, the
transition appears to be continuous. Genetic imprinting of the learning
abilities proceeds via Baldwin effect: initially unskilled communicating agents
learn a language and that creates a niche in which there is an evolutionary
pressure for the increase of learning ability.Our model suggests that when
linguistic (or cultural) processes became intensive enough, a transition took
place where both linguistic performance and biological endowment of our species
experienced an abrupt change that perhaps triggered the rapid expansion of
human civilization.Comment: 7 pages, minor changes, accepted in Int.J.Mod.Phys.C, proceedings of
Max Born Symp. Wroclaw (Poland), Sept. 2007. Java applet is available at
http://spin.amu.edu.pl/~lipowski/biolin.html or
http://www.amu.edu.pl/~lipowski/biolin.htm
Talking Helps: Evolving Communicating Agents for the Predator-Prey Pursuit Problem
We analyze a general model of multi-agent communication in which all agents communicate simultaneously to a message board. A genetic algorithm is used to evolve multi-agent languages for the predator agents in a version of the predator-prey pursuit problem. We show that the resulting behavior of the communicating multi-agent system is equivalent to that of a Mealy finite state machine whose states are determined by the agents’ usage of the evolved language. Simulations show that the evolution of a communication language improves the performance of the predators. Increasing the language size (and thus increasing the number of possible states in the Mealy machine) improves the performance even further. Furthermore, the evolved communicating predators perform significantly better than all previous work on similar preys. We introduce a method for incrementally increasing
the language size which results in an effective coarse-to-fine search that significantly reduces the evolution time required to find a solution. We present some observations on the effects of language size, experimental setup, and prey difficulty on the evolved Mealy machines. In particular, we observe that the start state is often revisited, and incrementally increasing the language size results in smaller Mealy machines. Finally, a simple rule is derived that provides a pessimistic estimate on the minimum language size that should be used for any multi-agent problem
Is Religion an Evolutionary Adaptation?
Religious people talk about things that cannot be seen, stories that cannot be verified, and beings and forces beyond the ordinary. Perhaps their gods are truly at work, or perhaps in human nature there is an impulse to proclaim religious knowledge. If so, it would have to have arisen by natural selection. It is hard to imagine how natural selection could have produced such an impulse. There is a debate among evolutionary scientists about whether or not there is any adaptive advantage to religion at all (Bulbulia 2004a; Atran and Norenzayan 2004). Some believe that it has no adaptive value itself and that it is just a hodge podge of of behaviors that have evolved because they are adaptive in other non-religious contexts. The agent-based simulation described in this article shows that a central unifying feature of religion, a belief in an unverifiable world, could have evolved along side of verifiable knowledge. The simulation makes use of an agent-based communication model with two types of information: verifiable information (real information) about a real world and unverifiable information (unreal information) about about an imaginary world. It examines the conditions necessary for the communication of unreal information to evolved along side the communication of real information. It offers support for the theory that religion is an adaptive complex and it disputes the theory that religion is a byproduct of unrelated adaptive processes.Religion, Myth, Deception, Empirical Reasoning, Rationality
Modelling Social Structures and Hierarchies in Language Evolution
Language evolution might have preferred certain prior social configurations
over others. Experiments conducted with models of different social structures
(varying subgroup interactions and the role of a dominant interlocutor) suggest
that having isolated agent groups rather than an interconnected agent is more
advantageous for the emergence of a social communication system. Distinctive
groups that are closely connected by communication yield systems less like
natural language than fully isolated groups inhabiting the same world.
Furthermore, the addition of a dominant male who is asymmetrically favoured as
a hearer, and equally likely to be a speaker has no positive influence on the
disjoint groups.Comment: 14 pages, 3 figures, 1 table. In proceedings of AI-2010, The
Thirtieth SGAI International Conference on Innovative Techniques and
Applications of Artificial Intelligence, Cambridge, England, UK, 14-16
December 201
Naming Game on Adaptive Weighted Networks
We examine a naming game on an adaptive weighted network. A weight of
connection for a given pair of agents depends on their communication success
rate and determines the probability with which the agents communicate. In some
cases, depending on the parameters of the model, the preference toward
successfully communicating agents is basically negligible and the model behaves
similarly to the naming game on a complete graph. In particular, it quickly
reaches a single-language state, albeit some details of the dynamics are
different from the complete-graph version. In some other cases, the preference
toward successfully communicating agents becomes much more relevant and the
model gets trapped in a multi-language regime. In this case gradual coarsening
and extinction of languages lead to the emergence of a dominant language,
albeit with some other languages still being present. A comparison of
distribution of languages in our model and in the human population is
discussed.Comment: 22 pages, accepted in Artificial Lif
Sharp transition towards shared vocabularies in multi-agent systems
What processes can explain how very large populations are able to converge on
the use of a particular word or grammatical construction without global
coordination? Answering this question helps to understand why new language
constructs usually propagate along an S-shaped curve with a rather sudden
transition towards global agreement. It also helps to analyze and design new
technologies that support or orchestrate self-organizing communication systems,
such as recent social tagging systems for the web. The article introduces and
studies a microscopic model of communicating autonomous agents performing
language games without any central control. We show that the system undergoes a
disorder/order transition, going trough a sharp symmetry breaking process to
reach a shared set of conventions. Before the transition, the system builds up
non-trivial scale-invariant correlations, for instance in the distribution of
competing synonyms, which display a Zipf-like law. These correlations make the
system ready for the transition towards shared conventions, which, observed on
the time-scale of collective behaviors, becomes sharper and sharper with system
size. This surprising result not only explains why human language can scale up
to very large populations but also suggests ways to optimize artificial
semiotic dynamics.Comment: 12 pages, 4 figure
Nature as a Network of Morphological Infocomputational Processes for Cognitive Agents
This paper presents a view of nature as a network of infocomputational agents organized in a dynamical hierarchy of levels. It provides a framework for unification of currently disparate understandings of natural, formal, technical, behavioral and social phenomena based on information as a structure, differences in one system that cause the differences in another system, and computation as its dynamics, i.e. physical process of morphological change in the informational structure. We address some of the frequent misunderstandings regarding the natural/morphological computational models and their relationships to physical systems, especially cognitive systems such as living beings. Natural morphological infocomputation as a conceptual framework necessitates generalization of models of computation beyond the traditional Turing machine model presenting symbol manipulation, and requires agent-based concurrent resource-sensitive models of computation in order to be able to cover the whole range of phenomena from physics to cognition. The central role of agency, particularly material vs. cognitive agency is highlighted
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