| Adaptive Environmentics |
The reasonable man adapts himself to the world; the unreasonable man persists to adapt the world to himself. Therefore, all progress depends on the unreasonable.
---George Bernard Shaw, 1903
An ant, viewed as a behaving system, is quite simple. The apparent complexity of its behavior over time is largely a reflection of the complexity of the environment in which it finds itself.
---Herbet A. Simon, 1969
Below you will find an excerpt from the paper:
M. Sipper,
On the origin of environments by means of natural selection,
AI Magazine, vol. 22, no. 4, pp. 133-140, Winter 2001.
The field of adaptive robotics studies the ways in which robots exhibiting some degree of autonomy adapt to their environments. Using both simulated and real robots, and applying techniques such as reinforcement learning, artificial neural networks, genetic algorithms, and fuzzy logic, researchers have obtained robots that display an amazing slew of behaviors and perform a multitude of tasks, including walking, pushing boxes, navigating, negotiating an obstacle course, playing ball, and foraging (Arkin, 1998a).
To cite one typical example of an ever-growing many, Yung and Ye (1999) recently wrote:
We have presented a fuzzy navigator that performs well in complex and unknown environments, using a rule base that is learned from a simple corridor-like environment. The principle of the navigator is built on the fusion of the obstacle avoidance and goal seeking behaviors aided by an environment evaluator to tune the universe of discourse of the input sensor readings and enhance its adaptability. For this reason, the navigator has been able to learn extremely quickly in a simple environment, and then operate in an unknown environment, where exploration is not required at all.
This quote typifies the underlying theme of adaptive robotics: Have a robot adapt to a given environment. Given signifies neither that the environment is known nor that it is static; it means that the robot must adapt to the quirks and idiosyncrasies imposed by the environment---which, for its part, does nothing at all to accommodate the puffing robot.
This fundamental principle of adaptive robotics---the environment's unyielding nature---is repealed in this article. Dubbed adaptive environmentics, the basic idea is to create scenarios that are mirror images of those found in adaptive robotics: The environment adapts to a given robot.
I hasten to say that in some cases, it is not possible to alter the environment, and in other cases, having the robot adapt is simply the underlying objective. Adaptive robotics has produced many interesting results based on these principles. I believe, however, that considering the flip-side setup brings along its own bag of boons; this article aims to demonstrate qualitatively the benefits of adaptive environments and present possible avenues of exploration. (I further discuss the environment's mutability in Discussion and Future Work.)
The article is organized as follows: In the next section, I describe the experimental setup, consisting of a simulated, mobile KHEPERA robot whose environment is evolved using a genetic algorithm. The section entitled Results then describes the results of three experiments intended to illustrate the approach: (1) place lamps evolutionarily to guide a photophilic KHEPERA between two given points in an obstacle-ridden course; (2) lay out lamps as in the first experiment, such that both the tour time and the number of lamps are minimized; (3) optimize the lamp layout as in the second experiment, with the added possibility of repositioning obstacles in the form of walls. I conclude in Discussion and Future Work by discussing issues requiring further investigation and suggesting several possible avenues of future research.