Authors:
Forrest Stonedahl
1
;
Susa H. Stonedahl
2
;
Nelly Cheboi
1
;
Danya Tazyeen
1
and
David Devore
1
Affiliations:
1
Augustana College, United States
;
2
St. Ambrose University, United States
Keyword(s):
Novelty Search, Neuroevolution, Multi-agent Robotics, Exploration.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Bioinformatics
;
Biomedical Engineering
;
Computational Intelligence
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Evolutionary Computing
;
Information Systems Analysis and Specification
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Methodologies and Technologies
;
Multi-Agent Systems
;
Operational Research
;
Robot and Multi-Robot Systems
;
Simulation
;
Soft Computing
;
Software Engineering
;
Symbolic Systems
Abstract:
This paper compares the use of novelty search and objective-based evolution to discover motion controllers for an exploration task wherein mobile robots search for immobile targets inside a bounded polygonal region and stop to mark target locations. We evolved the robots' neural-network controllers in a custom 2-D simulator, selected the best performing neurocontrollers from both novelty search and objective-based search, and compared performance relative to an unevolved (baseline) controller and a simple human-designed controller. The controllers were also transferred onto physical robots, and the real-world tests provided good empirical agreement with simulation results, showing that both novelty search and objective-based search produced controllers that were comparable or superior to the human-designed controller, and that objective-based search slightly outperformed novelty search. The best controllers had surprisingly low genotypic complexity, suggesting that this task may lac
k the type of deceptive fitness landscape that has previously favored novelty search over objective-based search.
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