Thus, the final step in making military robots will be to have them make their own robotic offspring as part of evolution on the battlefield. Robots will evolve their brains and bodies in response to the dynamic chaos of the moment in order to carry out their longer-term mission. Evolutionary adaptations will occur in a population of military Evolvabots with feedback from the battlefield environment using a fitness function the battlefield commanders generate. That fitness function might reward performance such as rate of target detection, rate of target hit, probability of survival, and robustness, or the ability to continue to operate once damaged.
When I presented this fitness idea to Chuck Pell, he disagreed: “The basic principle for robots in war should be this: [robots should be] unmanned, expendable, and cause maximum damage.” When I pointed out that a simple fitness function of maximum damage doesn’t leave room for evolving very complicated robots, he said that was the point. Chuck’s perspective is that complicated robots are expensive, and that’s a problem. With expensive robots, he explained, commanders won’t want to lose them, and those in charge will alter their tactics accordingly. The inescapable consequence, which I’ll call Pell’s Principle, “is that robot capabilities are proportional to cost, and cost is inversely proportional to expendability.” The logical conclusion of Pell’s Principle is to build and use swarms of simple, small, expendable robots (Figure 8.2).
FIGURE 8.2. Pell’s Principle in action. Swarms of simple, expendable robots overwhelm more complicated systems. In any environment swarms succeed by getting close and manipulating sensors, motors, and communications. The robotic systems represented here are based on real systems under development. Note the bio-inspired and diverse designs. Watercolor pencil sketch by Charles Pell.
“You can’t stop all the little robots,” said Chuck, “robots like MicroHunter.”[210] MicroHunter was a microAUV (Autonomous Underwater Vehicle) built using the same cycloptic helical klinotaxis system that we used to build Tadro (Figure 8.3; MicroHunter was introduced in Chapter 4). But whereas Tadro is a surface swimmer, MicroHunter swims underwater, spiraling its way to a light source from anywhere you could put it in an Olympic-sized pool. MicroHunter, funded by a DARPA contract to Chuck as an employee of Nekton Research (see Chapter 7), and Hugh Crenshaw, then professor at Duke University, caught their program officer’s eye when the duo reported that it had a 100 percent success rate finding the target.[211] “Nothing is ever that good,” said Chuck, “so they sent statisticians and a former Navy SEAL to investigate.”
SEALs, the US Navy’s special operations experts, are world renowned for their abilities underwater.[212] So even though Chuck and Hugh knew that MicroHunter could hit the three-meter light target when unchallenged, they figured that a group of four MicroHunters, swimming slowly, wouldn’t stand a chance with a special ops SCUBA diver in the pool. To everyone’s surprise and the SEAL’s chagrin, the MicroHunters and the SEAL played to a draw, with the MicroHunters hitting the target 50 percent of the time in six three-minute trials. That’s seriously excellent performance for a piece of embodied intelligence with but a single sensor and a single degree of freedom on the motor output side. Now try to imagine defending against not just four MicroHunters but a swarm of fifty. Your only defense may be evolution.
FIGURE 8.3. MicroHunter, a fully autonomous micro-aquatic robot with just one moving part, a propeller. MicroHunters, seven-and seventy-gram versions shown here, were developed by researchers and engineers at Nekton Research and Duke University with funding from DARPA’s MicroSystems Technology Office, Distributed Robotics Effort. Photo by Charles Pell.
We’ve had enough theory and practice of evolution in action in this book that I’m guessing you’ll be able to guess what I’m about to say. Here we go: any military that evolves robots on the battlefield will likely do so using the following principles:
* Robots are expendable. Given Pell’s Principle (see page 223), the only way to get large numbers of robots on the battlefield is to have them be expendable, and that usually means inexpensive. Large numbers also ensure that sufficient variation is in place to allow selection to act on the population. Large numbers also serve the tactical advantage just explained with regards to overwhelming the adversary with a swarm of simple autonomous agents.
* Robots are simple. This, too, follows from Pell’s Principle. The way to make robots expendable is to make them simple. Simple also usually means inexpensive to make and quick to produce. Employ the KISS principle in your design. Find the minimum brain, body, and behavior needed to seed your population. Choose which characters evolve.
* Robots evolve quickly. Given that generation time is one factor that limits the rate of evolution, make the generation time short. Short generation time will minimize the response time between a change on the battlefield and the change in the behavior and hardware of the population of robots.
* Robots evolve in small cohorts (small and genetically isolated subpopulations). Given that evolutionary change occurs rapidly in small, isolated populations, create many small companies of robots rather than a single large battalion. Keep in mind that random influences will dominate if the population is too small. Note that this may, at first, seem to run counter to principle 1 and using large numbers. You can have multiple populations or companies in simultaneous operation.
* Robots diversify in generational time. Given that evolutionary change will be rapid with principles (3) and (4), then allow the companies of robots to speciate, to evolve along different evolutionary trajectories. Diversification will allow more and different kinds of adaptation to occur simultaneously, thus increasing the chances of both tracking changes in the environment and finding the best solution at a given time and place.
That’s the offensive view of military Evolvabots. What about defense? How would you stop an army, navy, or air force of evolving robots? Keep in mind that even if it’s your militia of Evolvabots, you will need ways to constrain and control them too. This is starting to sound like nearly every story and movie on robots in which they rise up and break the shackles of their creators to take over the world. Although we aren’t making a movie, let’s run with the plot line anyway. To avoid military defeat or robotic overthrow, here’s what you do:
* Limit initial production prior to battle. Control numbers and types of robots. Constrain raw materials. Limit, reduce, or eliminate energy sources. If the population is yours, you may want to design in hard production or run-time limits to avoid the enemy co-opting the group.
* Limit reproduction on the battlefield. Because reproduction is key to the evolutionary process and usually involves some vulnerable moments, like finding mates and creating offspring, you should target these situations. Also, target the machine-making machines, because they need to be on the battlefield to keep generation times short. Limits to production (see item #1, above) can also be employed on the battlefield by cutting supply lines.
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MicroHunter was invented by Chuck Pell, Hugh Crenshaw, Jason Janet, and Mathieu Kemp and was assigned to Nekton Technologies, Inc., US Patent 6,378,801. C. Pell, H. Crenshaw, J. Janet, and M. Kemp, “Devices and Methods for Orienting and Steering in Three-Dimensional Space,” 2002. A great place to get an overview of MicroHunter is in J. Wakefield, “Mimicking Mother Nature,”
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For more on MicroHunter, see M. Kemp, H. Crenshaw, B. Hobson, J. Janet, R. Moody, C. Pell, H. Pinnix, and B. Schulz, “Micro-AUVs I: Platform Design and Multiagent System Development,” in