While evolutionary computation is well suited for automatic discovery in
engineering, it can also be used to gain insight into how humans and
organizations could perform more effectively. Using a real-world problem of
innovation search in organizations as the motivating example, this article
first formalizes human creative problem solving as competitive multiagent
search (CMAS). CMAS is different from existing single-agent and team search
problems in that the agents interact through knowledge of other agents'
searches and through the dynamic changes in the search landscape that result
from these searches. The main hypothesis is that evolutionary computation can
be used to discover effective strategies for CMAS; this hypothesis is verified
in a series of experiments on the NK model, i.e. partially correlated and
tunably rugged fitness landscapes. Different specialized strategies are evolved
for each different competitive environment, and also general strategies that
perform well across environments. These strategies are more effective and more
complex than hand-designed strategies and a strategy based on traditional tree
search. Using a novel spherical visualization of such landscapes, insight is
gained about how successful strategies work, e.g. by tracking positive changes
in the landscape. The article thus provides a possible framework for studying
various human creative activities as competitive multi-agent search in the
future.

本文以智能搜索技术在人机系统中的应用为出发点，提出了一种新的多智能体协作搜索方法，并通过实验结果表明，该方法优于传统的手动设计策略和传统树状搜索方法，并能够用于研究不同的人类创造性活动。