Foraging Theory for Autonomous Vehicle Decision-making System Design
Journal of Intelligent and Robotic Systems
Foraging theory for autonomous vehicle speed choice
Engineering Applications of Artificial Intelligence
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Enhanced Karnik-Mendel algorithms
IEEE Transactions on Fuzzy Systems
Generalizing foraging theory for analysis and design
International Journal of Robotics Research
| Hi-index | 0.00 |
Models from behavioral ecology, specifically foraging theory, are used to describe the decisions an animal forager must make in order to maximize its rate of energy gain and thereby improve its survival probability. Using a bioinspired methodology, we view an animal as a software agent, the foraging landscape as a spatial layout of temperature zones, and nutrients as errors between the desired and actual temperatures in the zones. Then, using foraging theory, we define a decision strategy for the agent that has an objective of reducing the temperature errors in order to track a desired temperature. We describe an implementation of a multizone temperature experiment, and show that the use of multiple agents defines a distributed controller that can equilibrate the temperatures in the zones in spite of interzone, ambient, and network effects. We discuss relations to ideas from theoretical ecology, and identify a number of promising research directions. It is our hope that the results of this paper will motivate other research on bioinspired methods based on behavioral ecology