Archive for the ‘artificial intelligence in the news’ Category
Cuckoos have an aggressive reproduction strategy that involves the female laying her fertilised eggs in the nest of another species so that the surrogate parents unwittingly raise her brood. Sometimes the cuckoo’s egg in the nest is discovered and the surrogate parents throw it out or abandon the nest and start their own brood elsewhere.
The team base their design search on three simple principles that emerge from the cuckoo’s strategy:
* First, each cuckoo lays one egg (a design solution) at a time, and dumps it in a randomly chosen nest.
* Second, the best nests with a high quality egg (better solution) carry over to the next generation.
* Third, the number of available host nests is fixed, and a host and there is a finite probability of the cuckoo in the nest being discovered.
The team have encapsulated these three principles in a mathematical formula that they then converted to computer software code. The various design parameters and constraints are fed to the software, which tests each “egg” discarding some based on lack of fitness and sending the successful solutions through a second round and so on until an optimal solution emerges.
The team has carried out standard mathematical design tests on their cuckoo search, which itself has now been optimised and also compared it with particle swarm optimisation and other techniques to show that it is more efficient than these other approaches to engineering design of a welded beam and a spring, two key engineering components of many structures. Read more
Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the Bat Algorithm, based on the echolocation behaviour of bats. We also intend to combine the advantages of existing algorithms into the new bat algorithm. After a detailed formulation and explanation of its implementation, we will then compare the proposed algorithm with other existing algorithms, including genetic algorithms and particle swarm optimization. Simulations show that the proposed algorithm seems much superior to other algorithms, and further studies are also discussed. [download the paper pdf]
Other swarm papers by Xin-She Yang
The sight of a cockroach scurrying for cover may be nauseating, but the insect is also a biological and engineering marvel, and is providing researchers at Oregon State University with what they call “bioinspiration” in a quest to build the world’s first legged robot that is capable of running effortlessly over rough terrain.
If the engineers succeed, they may owe their success to what’s being learned from these insects and other animals, such as the guinea hen, that have their own remarkable abilities.
. . .
Within certain limitations, Schmitt said, cockroaches don’t even have to think about running – they just do it, with muscle action that is instinctive and doesn’t require reflex control. That, in fact, is part of what the engineers are trying to achieve. Right now some robots have been built that can walk, but none of them can run as well as their animal counterparts. Even walking robots absorb far too much energy and computing power to be very useful.
“If we ever develop robots that can really run over rough ground, they can’t afford to use so much of their computing abilities and energy demand to accomplish it,” Schmitt said. “A cockroach doesn’t think much about running, it just runs. And it only slows down about 20 percent when going over blocks that are three times higher than its hips. That’s just remarkable, and an indication that their stability has to do with how they are built, rather than how they react.”
. . .
In a computer model, they’ve created a concept that would allow a running robot to recover from a change in ground surface almost as well as a guinea hen. They are studying how the interplay of concepts such as energy storage and expenditure, sensor and feedback requirements, and leg angles can produce recovery from such perturbations. Ultimately, a team of OSU engineers hopes to use knowledge such as this to actually build robots that can efficiently run over rough terrain without using significant computing power.
Training a Large Scale Classifier with the Quantum Adiabatic Algorithm
Qubuit.org, Center for Quantum Computing
Introduction to Quantum Computing
The Quantum Computer
Quantum Computing and Shor’s Algorithm
Quantum Computing Day 1, Google Tech Talk on YouTube
Quantum Computing Day 2, Google Tech Talk on YouTube
Quantum Computing Day 3, Google Tech Talk on YouTube
So are we ready yet to hand over some of the control of our computers to evolving virtual creatures to do the dirty work for us? What happens when a virtual war breaks out on your home network? Do you get to watch the battles?
If we hand over this control how far are we willing to let virtual creatures evolve? Will they develop personality disorders and call work stoppages?
The future is closer than you think, turn around to look and you may get mowed down.
In the never-ending battle to protect computer networks from intruders, security experts are deploying a new defense modeled after one of nature’s hardiest creatures — the ant.
Unlike traditional security devices, which are static, these “digital ants” wander through computer networks looking for threats, such as “computer worms” — self-replicating programs designed to steal information or facilitate unauthorized use of machines. When a digital ant detects a threat, it doesn’t take long for an army of ants to converge at that location, drawing the attention of human operators who step in to investigate.
The concept, called “swarm intelligence,” promises to transform cyber security because it adapts readily to changing threats.
“In nature, we know that ants defend against threats very successfully,” explains Professor of Computer Science Errin Fulp, an expert in security and computer networks. “They can ramp up their defense rapidly, and then resume routine behavior quickly after an intruder has been stopped. We were trying to achieve that same framework in a computer system.” . . .