Novelty seeking multi agent systems

Interesting:

This paper considers novelty-seeking multi-agent systems
as a step towards more efficient generation of creative
artifacts. We describe a simple multi-agent architecture
where agents have limited resources and exercise
self-criticism, veto power and voting to collectively
regulate which artifacts are selected to the domain i.e.,
the cultural storage of the system. To overcome their individual
resource limitations, agents have a limited access
to the artifacts already in the domain which they
can use to guide their search for novel artifacts.
Creating geometric images called spirographs as a case
study, we show that novelty-seeking multi-agent systems
can be more productive in generating novel artifacts
than a single-agent or monolithic system. In particular,
veto power is in our case an effective collaborative
decision-making strategy for enhancing novelty of
domain artifacts, and self-criticism of agents can significantly
reduce the collaborative effort in decision making.

Novelty is often considered a central component of creativity
(e.g. Boden (1992)). Obviously, an artifact that is not
novel can hardly be considered creative. This paper studies
the capability of cooperative multi-agent systems to seek and
produce novel artifacts, and the effects of social decisionmaking
strategies on this capability. Our focus is on seeking
novelty; other aspects of creativity, such as surprise and
value, are left for future work.

According to the systems view of Csikszentmihalyi
(1988), creative systems consist of three intertwined parts:
individual agents, society and domain. A set of interacting
agents forms a society. The domain is a cultural component
constructed by the society by selecting artifacts worth preserving.
Each part in the system is in constant interaction
with other parts, e.g. individuals try to learn from the domain
and bring about transformations, while it is the society
that collectively decides which transformations are valued
and stored in the domain.

…Saunders and Gero (2001a) present a curious agent
searching for novelty in the space of geometric images produced
by a spirograph. The agent learns a categorization
of the produced images by showing them as input to a selforganized
map, or SOM (Kohonen 1995). The novelty of a
new image is computed as the pixel-wise deviation from the
best matching cell’s image in the SOM. The agent’s curiosity
is modeled as a tendency to make smaller mutations in
the generating parameters when more novelty is found. This
helped the agent to concentrate on areas in the parameter
space where more variability was found.

Algorithm 1
Agent behavior during a single iteration

1: invent a new artifact close to the agent’s current location
and move to the new location

2: if the new artifact passes self-criticism then

3: memorize the new artifact

4: publish the new artifact as a candidate for the domain

5: end if

6: participate in social decision making to select which
artifact, among candidates published by all agents, is
added to the domain

7: select and memorize artifacts from domain