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Project duration: 01-09-2004 - 31-08-2007
Project participants: see consortium
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Project funding: NEW TIES is a research project
funded by the European Commission via the 6th Framework's Future
and Emerging Technologies open scheme.
MAIN OBJECTIVE
(original)
The original aim as formulated
in personal discussions is to create (initiate + have evolve/emerge) an
artificial society with high mental and linguistic capabilities.In particular,
it would be great to see whether/when/how they start wondering about the
origins of their world and themselves. Pushing it to the extreme: see if
they ask themselves if WE exist. Name it artificial or evolutionary philosophy?
(cf. artificial life, evolutionary economy, etc). Interestingly, these
ideas were partly inspired by a piece of literature: Stanislaw
Lem's short story called "Non
Serviam" (originally in S. Lem, A Perfect Vacuum, English translation
published by Harcourt Brace Jovanovich, 1983, also included in the
famous AI collection Douglas R. Hofstadter and Daniel C. Dennett (Editors),
The Mind's I: Fantasies and Reflections on Self & Soul, Basic Books
Inc. 1981, pp. 296-317).
MAIN OBJECTIVES
(from the project proposal)
The goal of the project
is to realize an evolving artificial society capable of exploring its virtual
world and developing its own view of that world. The long-term target is
to learn how to design agents that are able to adapt autonomously to, and
then operate effectively in, environments whose features are not known
in advance. The main pillars of the envisioned research are world models
and the learning mechanisms generating these. We shall not implement specific
training facilities or feedback systems rewarding the learning of world
models for its own sake. Instead, we are interested in emerging world models
powered by a basic survival game. In order to obtain the expected emergent
features, we shall work on a very large scale compared to that of today’s
common practice with respect to the size of the agent population and agent
complexity. Defining culture as knowledge structures shared among agents
that reflect aspects of the environment, including the other agents, our
first scientific objective is:
1. To develop
an artificial society with an emergent culture.
This objective can be broken
into the following sub-objectives:
1.1. To identify which system
components carry the knowledge structures that make up world models.
1.2. To develop mechanisms
to monitor the development of world models.
The population's abilities
to develop advanced skills bottom-up consist of individual learning, evolutionary
learning, and social learning. This motivates the second main objective.
2. To realise
a powerful “emergence engine” consisting of a well-balanced combination
of individual learning, evolutionary learning, and social learning.
This objective can be broken
into the following sub-objectives:
2.1. To develop an understanding
of the mutual effects of the three types of learning on each other and
on the development of the individuals and the whole population.
2.2. To develop mechanisms
for adjusting the balance between the three types of learning.
We consider individual learning and evolutionary learning as the two basic mechanisms and expect that social learning has a (re)combinative effect on the learning processes significantly boosting the population’s adaptive capabilities and enabling the society to rapidly develop an "understanding" of the world collectively. One of the main innovations of this project lies in the way we approach social learning. It will be implemented in such a way that it allows passing knowledge explicitly (i.e., not only by imitation, but via direct messages) to others within the same generation. The corresponding scientific objective is:
3. To develop,
evaluate, and use a range of social learning mechanisms that allow sharing
knowledge with other members of the population.
Our approach to this objective
is twofold. On the one hand we will study various protocols of information
dissemination, implying the following sub-objective:
3.1. To set up communication
mechanisms and investigate their effects on the efficiency of social learning
mechanisms.
On the other hand we need
to address issues about the form and content of messages. This leads to
the second sub-objective, which relates to (the emergence of) language
and its use:
3.2. To implement a framework
enabling the emergence of the communication and cooperation essential to
social learning.
PROJECT SUMMARY (Abstract of the proposal)
The project is concerned
with emergence and complexity in socially-inspired artificial systems.
We will study large systems consisting of an environment and an inhabitant
population. The main goal of the project is to realize an evolving artificial
society capable of exploring the environment and developing its own image
of this environment and the society through cooperation and interaction.
We will work with virtual grid worlds and will set up environments that
are sufficiently complex and demanding that communication and cooperation
are necessary to adapt to the given tasks. The population's weaponry to
develop advanced skills bottom-up consists of individual learning, evolutionary
learning, and social learning. One of the main innovations of this project
is social learning interpreted as passing knowledge explicitly via a language
to others in the same generation. This has a synergetic effect on the learning
processes and enables the society to rapidly develop an "understanding"
of the world collectively. If the learning process stabilises, the collective
must have formed an appropriate world map. Then we will probe the collective
mind to learn how the agents perceive the environment, including themselves,
and what skills and procedures they have developed to adapt successfully.
This could yield new knowledge and surprising perspectives about the environment
and the survival task. The project represents a significant scale-up beyond
the state-of-the-art in two dimensions: the inner complexity of inhabitants
and the size of the population. To achieve and explore highly complex organisms
and behaviours, very large populations will be studied. This will make
the system at the macro level complex enough to allow significant behaviours
(cultures etc) to emerge in separate parts of the system and to interact.
To enable this we will set up a large distributed computing infrastructure,
and a shared platform to allow very large scale experiments in a p2p fashion.
.
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