Next Article in Journal
AI and the Singularity: A Fallacy or a Great Opportunity?
Next Article in Special Issue
Machine Reading Comprehension for Answer Re-Ranking in Customer Support Chatbots
Previous Article in Journal
Novel Parameterized Utility Function on Dual Hesitant Fuzzy Rough Sets and Its Application in Pattern Recognition
Previous Article in Special Issue
Automatic Acquisition of Annotated Training Corpora for Test-Code Generation
Open AccessFeature PaperArticle

Evolution, Robustness and Generality of a Team of Simple Agents with Asymmetric Morphology in Predator-Prey Pursuit Problem

1
Department of Information System Design, Doshisha University, Kyotanabe, Kyoto 610-0321, Japan
2
Department of Biology, University of Oklahoma, Norman, Oklahoma, OK 73019, USA
*
Author to whom correspondence should be addressed.
This paper is extended version of our paper presented in 18th International Conference AIMSA 2018, Varna, Bulgaria, 12–14 September 2018.
Information 2019, 10(2), 72; https://doi.org/10.3390/info10020072
Received: 21 January 2019 / Revised: 12 February 2019 / Accepted: 17 February 2019 / Published: 20 February 2019
(This article belongs to the Special Issue Artificial Intelligence—Methodology, Systems, and Applications)
One of the most desired features of autonomous robotic systems is their ability to accomplish complex tasks with a minimum amount of sensory information. Often, however, the limited amount of information (simplicity of sensors) should be compensated by more precise and complex control. An optimal tradeoff between the simplicity of sensors and control would result in robots featuring better robustness, higher throughput of production and lower production costs, reduced energy consumption, and the potential to be implemented at very small scales. In our work we focus on a society of very simple robots (modeled as agents in a multi-agent system) that feature an “extreme simplicity” of both sensors and control. The agents have a single line-of-sight sensor, two wheels in a differential drive configuration as effectors, and a controller that does not involve any computing, but rather—a direct mapping of the currently perceived environmental state into a pair of velocities of the two wheels. Also, we applied genetic algorithms to evolve a mapping that results in effective behavior of the team of predator agents, towards the goal of capturing the prey in the predator-prey pursuit problem (PPPP), and demonstrated that the simple agents featuring the canonical (straightforward) sensory morphology could hardly solve the PPPP. To enhance the performance of the evolved system of predator agents, we propose an asymmetric morphology featuring an angular offset of the sensor, relative to the longitudinal axis. The experimental results show that this change brings a considerable improvement of both the efficiency of evolution and the effectiveness of the evolved capturing behavior of agents. Finally, we verified that some of the best-evolved behaviors of predators with sensor offset of 20° are both (i) general in that they successfully resolve most of the additionally introduced, unforeseen initial situations, and (ii) robust to perception noise in that they show a limited degradation of the number of successfully solved initial situations. View Full-Text
Keywords: simple agents; micro-robots; asymmetric morphology; predator-prey problem; genetic algorithms simple agents; micro-robots; asymmetric morphology; predator-prey problem; genetic algorithms
Show Figures

Figure 1

MDPI and ACS Style

Georgiev, M.; Tanev, I.; Shimohara, K.; Ray, T. Evolution, Robustness and Generality of a Team of Simple Agents with Asymmetric Morphology in Predator-Prey Pursuit Problem. Information 2019, 10, 72.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop