"Fuzzy Logic is basically a multivalued logic that allows intermediate values to be defined between conventional evaluations like yes/no, true/false, black/white, etc. Notions like rather warm or pretty cold can be formulated mathematically and processed by computers."
– Peter Bauer, Stephan Nouak, and Roman Winkler. A Brief Course in Fuzzy Logic and Fuzzy Control. Available from ESRU [Energy Systems Research Unit], Department of Mechanical Engineering, University of Strathclyde. 1996.
Inference Engine It accepts and promotes human interpretation by making fuzzy inference according to inputs and IF-THEN rules. A number of other concepts are associated with fuzzy logic such as fuzzy set theory, fuzzy modelling, the fuzzy control system that have been developed for further enhancement. In control systems theory, if the fuzzy interpretation of the problem is appropriate and if the fuzzy theory is developed precise and correct, then fuzzy controllers can be accordingly designed and they work quite well to their advantages. Most of the fuzzy logic control systems are knowledge-based systems which mean either their fuzzy models or their fuzzy logic controllers are described by fuzzy logic IF-THEN rules.
In recent years, the term'machine learning' has become very popular among developers and business alike, even though research in the field has been going on for decades. Essentially, machine learning is about teaching machines to learn concepts and techniques the way humans do. While computer scientists were making huge strides in increasing computational performance by utilizing advancements in hardware to enable machines to solve complex calculations, hypotheses by their fellow researchers from AI on the ability of machines to think and act like humans were met with skepticism. A sub-field of AI, machine learning, saw rapid growth when companies such as Google and Facebook began to find new ways to utilize the troves of data for more profit.
We present CGO-AS, a generalized Ant System (AS) implemented in the framework of Cooperative Group Optimization (CGO), to show the leveraged optimization with a mixed individual and social learning. In CGO-AS, each ant (agent) is added with an individual memory, and is implemented with a novel search strategy to use individual and social cues in a controlled proportion. The presented CGO-AS is therefore especially useful in exposing the power of the mixed individual and social learning for improving optimization. The results prove that a cooperative ant group using both individual and social learning obtains a better performance than the systems solely using either individual or social learning.
David Fleck, an associate professor at the UC College of Medicine, and his co-authors used artificial intelligence called "genetic fuzzy trees" to predict how bipolar patients would respond to lithium. The study authors found that even the best of eight common models used in treating bipolar disorder predicted who would respond to lithium treatment with 75 percent accuracy. But the model UC researchers developed using AI predicted how patients would respond to lithium treatment with 88 percent accuracy and 80 percent accuracy in validation. It turns out that the same kind of artificial intelligence that outmaneuvered Air Force pilots last year in simulation after simulation at Wright-Patterson Air Force Base is equally adept at making beneficial decisions that can help doctors treat disease.
The tools used to create ALPHA as well as the ALPHA project have been developed by Psibernetix, Inc., recently founded by UC College of Engineering and Applied Science 2015 doctoral graduate Nick Ernest, now president and CEO of the firm; as well as David Carroll, programming lead, Psibernetix, Inc.; with supporting technologies and research from Gene Lee; Kelly Cohen, UC aerospace professor; Tim Arnett, UC aerospace doctoral student; and Air Force Research Laboratory sponsors. They tackled the problem using language-based control (vs. States UC's Cohen, "Genetic fuzzy systems have been shown to have high performance, and a problem with four or five inputs can be solved handily. That's the "tree" part of the term "Genetic Fuzzy Tree" system. Programming that's language based, genetic and generational Most AI programming uses numeric-based control and provides very precise parameters for operations.
The Bing search engine saw some very red-faced Redmondites when Saudi Arabian users found that "Daesh" has been translated as "Saudi Arabia". In this case, Bing has a crowdsourcing function allowing groups of people to suggest a possible answer. Once infected, the malware reads emails, messages, listens to phone calls, monitors social networks, grabs passwords and other lovely surprises. This memory will be commercially available in 2018 and promises "several thousand times faster rewrites and many thousands of times more rewrite cycles than embedded flash memory".
Breakthroughs in genetic fuzzy systems, most notably the development of the Genetic Fuzzy Tree methodology, have allowed fuzzy logic based Artificial Intelligences to be developed that can be applied to incredibly complex problems. Within this white paper, the authors introduce ALPHA, an Artificial Intelligence that controls flights of Unmanned Combat Aerial Vehicles in aerial combat missions within an extreme-fidelity simulation environment. To this day, this represents the most complex application of a fuzzy-logic based Artificial Intelligence to an Unmanned Combat Aerial Vehicle control problem. The quality of these preliminary results in a problem that is not only complex and rife with uncertainties but also contains an intelligent and unrestricted hostile force has significant implications for this type of Artificial Intelligence.
Developed by a University of Cincinnati (US) doctoral candidate, an Artificial Intelligence (AI) called ALPHA has consistently beaten other AIs and a retired United States Air Force Colonel in a high-fidelity, air-combat simulator using what's known as a genetic-fuzzy system that relies on off-the-shelf PC processors to do what was thought to be the reserve of supercomputers. Seated at the simulator controls is retired U.S. Air Force Colonel Gene Lee So far, AI pilots haven't been able to do any better and often times fare worse than their human counterparts. In a recent test last October, Colonel Gene Lee, a highly experienced fighter pilot and trainer who has flown against AIs since the early 1980s, took on ALPHA in simulated dog fights. By teaming artificial intelligence with US air capabilities, programs like ALPHA could lessen the likelihood of mistakes.
Details on ALPHA -- a significant breakthrough in the application of what's called genetic-fuzzy systems are published in the most-recent issue of the Journal of Defense Management, as this application is specifically designed for use with Unmanned Combat Aerial Vehicles (UCAVs) in simulated air-combat missions for research purposes. The tools used to create ALPHA as well as the ALPHA project have been developed by Psibernetix, Inc., recently founded by UC College of Engineering and Applied Science 2015 doctoral graduate Nick Ernest, now president and CEO of the firm; as well as David Carroll, programming lead, Psibernetix, Inc.; with supporting technologies and research from Gene Lee; Kelly Cohen, UC aerospace professor; Tim Arnett, UC aerospace doctoral student; and Air Force Research Laboratory sponsors. So it's likely that future air combat, requiring reaction times that surpass human capabilities, will integrate AI wingmen -- Unmanned Combat Aerial Vehicles (UCAVs) -- capable of performing air combat and teamed with manned aircraft wherein an onboard battle management system would be able to process situational awareness, determine reactions, select tactics, manage weapons use and more. It would normally be expected that an artificial intelligence with the learning and performance capabilities of ALPHA, applicable to incredibly complex problems, would require a super computer in order to operate.
Aptly name artificial intelligence, ALPHA recently beat a veteran aerial combat expert in a high-fidelity combat simulator. Authors of the paper go on to add, " To this day, this represents the most complex application of a fuzzy-logic based Artificial Intelligence to an Unmanned Combat Aerial Vehicle control problem." So, to be one step ahead, ALPHA's creators aim at a future with collaborative manned-unmanned combat aerial vehicles. Currently, ALPHA's aim is to serve as a highly intelligent training simulator for combat pilots.