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Evolving Strategies for Competitive Multi-Agent Search (2023)
Erkin Bahceci
,
Riitta Katila
, and
Risto Miikkulainen
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.
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Citation:
arXiv:2306.10640
, 2023.
Bibtex:
@article{bahceci:arxiv23, title={Evolving Strategies for Competitive Multi-Agent Search}, author={Erkin Bahceci and Riitta Katila and Risto Miikkulainen}, journal={arXiv:2306.10640}, month={ }, url="http://nn.cs.utexas.edu/?bahceci:arxiv23", year={2023} }
People
Erkin Bahceci
Ph.D. Alumni
erkin [at] cs utexas edu
Riitta Katila
Collaborator
rkatila [at] stanford edu
Risto Miikkulainen
Faculty
risto [at] cs utexas edu
Projects
A Predictive Simulation Model of Competitive Dynamics in Innovation
2009 - 2013
Demos
Simulation of Competitive Multi-Agent Search on NK Fitness Landscapes
Erkin Bahceci
2012
Software/Data
NKVis
This package contains a 3D visualization tool for NK fitness landscapes. Two types of visualizations are provided: a
2011
Areas of Interest
Evolutionary Computation
Other Areas