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Hybrid Intelligent System
Hybrid intelligent system denotes a software system which employs, in parallel, a combination of methods and techniques from artificial intelligence subfields, such as: * Neuro-symbolic systems * Neuro-fuzzy systems * Hybrid connectionist-symbolic models * Fuzzy expert systems * Connectionist expert systems * Evolutionary neural networks * Genetic fuzzy systems * Rough fuzzy hybridization * Reinforcement learning with fuzzy, neural, or evolutionary methods as well as symbolic reasoning methods. From the cognitive science perspective, every natural intelligent system is hybrid because it performs mental operations on both the symbolic and subsymbolic levels. For the past few years, there has been an increasing discussion of the importance of A.I. Systems Integration. Based on notions that there have already been created simple and specific AI systems (such as systems for computer vision, speech synthesis, etc., or software that employs some of the models mentioned above) an ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Neuro-symbolic AI
Neuro-symbolic AI is a type of artificial intelligence that integrates neural and symbolic AI architectures to address the weaknesses of each, providing a robust AI capable of reasoning, learning, and cognitive modeling. As argued by Leslie Valiant and others, the effective construction of rich computational cognitive models demands the combination of symbolic reasoning and efficient machine learning. Gary Marcus argued, "We cannot construct rich cognitive models in an adequate, automated way without the triumvirate of hybrid architecture, rich prior knowledge, and sophisticated techniques for reasoning." Further, "To build a robust, knowledge-driven approach to AI we must have the machinery of symbol manipulation in our toolkit. Too much useful knowledge is abstract to proceed without tools that represent and manipulate abstraction, and to date, the only known machinery that can manipulate such abstract knowledge reliably is the apparatus of symbol manipulation." Angelo Dall ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Automated Planning
Automated planning and scheduling, sometimes denoted as simply AI planning, is a branch of artificial intelligence that concerns the realization of strategy, strategies or action sequences, typically for execution by intelligent agents, autonomous robots and unmanned vehicles. Unlike classical control system, control and Statistical classification, classification problems, the solutions are complex and must be discovered and optimized in multidimensional space. Planning is also related to decision theory. In known environments with available models, planning can be done offline. Solutions can be found and evaluated prior to execution. In dynamically unknown environments, the strategy often needs to be revised online. Models and policies must be adapted. Solutions usually resort to iterative trial and error processes commonly seen in artificial intelligence. These include dynamic programming, reinforcement learning and combinatorial optimization. Languages used to describe planning ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
DARPA LAGR Program
{{short description, United States government program involved in the development of unmanned ground vehicles The Learning Applied to Ground Vehicles (LAGR) program, which ran from 2004 until 2008, had the goal of accelerating progress in autonomous, perception-based, off-road navigation in robotic unmanned ground vehicles (UGVs). LAGR was funded by DARPA, a research agency of the United States Department of Defense. History and background While mobile robots had been in existence since the 1960s, ( e.g. Shakey), progress in creating robots that could navigate on their own, outdoors, off-road, on irregular, obstacle-rich terrain had been slow. In fact, no clear metrics were in place to measure progress. A baseline understanding of off-road capabilities began to emerge with the DARPA PerceptOR program in which independent research teams fielded robotic vehicles in unrehearsed Government tests that measured average speed and number of required operator interventions over a fixed ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
IEEE
The Institute of Electrical and Electronics Engineers (IEEE) is an American 501(c)(3) organization, 501(c)(3) public charity professional organization for electrical engineering, electronics engineering, and other related disciplines. The IEEE has a corporate office in New York City and an operations center in Piscataway, New Jersey. The IEEE was formed in 1963 as an amalgamation of the American Institute of Electrical Engineers and the Institute of Radio Engineers. History The IEEE traces its founding to 1884 and the American Institute of Electrical Engineers. In 1912, the rival Institute of Radio Engineers was formed. Although the AIEE was initially larger, the IRE attracted more students and was larger by the mid-1950s. The AIEE and IRE merged in 1963. The IEEE is headquartered in New York City, but most business is done at the IEEE Operations Center in Piscataway, New Jersey, opened in 1975. The Australian Section of the IEEE existed between 1972 and 1985, after which it s ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Outline Of Artificial Intelligence
The following outline is provided as an overview of and topical guide to artificial intelligence: Artificial intelligence (AI) is intelligence exhibited by machines or software. It is also the name of the scientific field which studies how to create computers and computer software that are capable of intelligent behavior. AI algorithms and techniques Search * Discrete search algorithms ** Uninformed search *** Brute force search *** Search tree **** Breadth-first search **** Depth-first search *** State space search ** Informed search *** Best-first search *** A* search algorithm *** Heuristics *** Pruning (algorithm) ** Adversarial search *** Minmax algorithm ** Logic as search *** Production system (computer science), Rule based system *** Production rule, Inference rule, Horn clause *** Forward chaining *** Backward chaining ** Planning as search *** State space search *** Means–ends analysis Optimization search * Optimization (mathematics) algorithm ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
List Of Emerging Technologies
This is a list of emerging technologies, which are emerging technologies, in-development technical innovations that have significant potential in their applications. The criteria for this list is that the technology must: # Exist in some way; purely Hypothetical technology, hypothetical technologies cannot be considered emerging and should be covered in the list of hypothetical technologies instead. However, technologies being actively researched and prototyped are acceptable. # Have a Wikipedia article or adjacent citation covering them. # Not be widely used yet. Mainstream or extensively commercialized technologies can no longer be considered emerging. Listing here is not a prediction that the technology will become widely adopted, only a recognition of significant ''potential'' to become widely adopted or highly useful if ongoing work continues, is successful, and the work is not overtaken by other technologies. Agriculture Construction Economy Electronics, IT, an ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Intelligent Control
Intelligent control is a class of control techniques that use various artificial intelligence computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, reinforcement learning, evolutionary computation and genetic algorithms. Overview Intelligent control can be divided into the following major sub-domains: * Neural network control * Machine learning control * Reinforcement learning * Bayesian control * Fuzzy control * Neuro-fuzzy control * Expert Systems * Genetic control New control techniques are created continuously as new models of intelligent behavior are created and computational methods developed to support them. Neural network controller Neural networks have been used to solve problems in almost all spheres of science and technology. Neural network control basically involves two steps: * System identification * Control It has been shown that a feedforward network with nonlinear, continuous and differentiable activation func ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Artificial Intelligence Systems Integration
The core idea of artificial intelligence systems integration is making individual software components, such as speech synthesizers, interoperable with other components, such as common sense knowledgebases, in order to create larger, broader and more capable A.I. systems. The main methods that have been proposed for integration are message routing, or communication protocols that the software components use to communicate with each other, often through a middleware blackboard system. Most artificial intelligence systems involve some sort of integrated technologies, for example, the integration of speech synthesis technologies with that of speech recognition. However, in recent years, there has been an increasing discussion on the importance of systems integration as a field in its own right. Proponents of this approach are researchers such as Marvin Minsky, Aaron Sloman, Deb Roy, Kristinn R. Thórisson and Michael A. Arbib. A reason for the recent attention A.I. integrati ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Applications Of Artificial Intelligence
Artificial intelligence (AI) has been used in applications throughout industry and academia. In a manner analogous to electricity or computers, AI serves as a general-purpose technology. AI programs are designed to simulate human perception and understanding. These systems are capable of adapting to new information and responding to changing situations. Machine learning has been used for various scientific and commercial purposes including language translation, image recognition, decision-making, credit scoring, and e-commerce. Agriculture In agriculture, AI has been proposed as a way for farmers to identify areas that need irrigation, fertilization, or pesticide treatments to increase yields, thereby improving efficiency. AI has been used to attempt to classify livestock pig call emotions, automate greenhouses, detect diseases and pests, and optimize irrigation. Architecture & Design AI in architecture has created a way for architects to create things beyond human unders ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
AI Effect
The AI effect is the discounting of the behavior of an artificial intelligence program as not "real" intelligence. The author Pamela McCorduck writes: "It's part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something—play good checkers, solve simple but relatively informal problems—there was a chorus of critics to say, 'that's not thinking'." Researcher Rodney Brooks complains: "Every time we figure out a piece of it, it stops being magical; we say, 'Oh, that's just a computation." Definition "The AI effect" refers to a phenomenon where either the definition of AI or the concept of intelligence is adjusted to exclude capabilities that AI systems have mastered. This often manifests as tasks that AI can now perform successfully no longer being considered part of AI, or as the notion of intelligence itself being redefined to exclude AI achievements. Edward Geist credits John McCarthy for coining the term ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
AI Alignment
In the field of artificial intelligence (AI), alignment aims to steer AI systems toward a person's or group's intended goals, preferences, or ethical principles. An AI system is considered ''aligned'' if it advances the intended objectives. A ''misaligned'' AI system pursues unintended objectives. It is often challenging for AI designers to align an AI system because it is difficult for them to specify the full range of desired and undesired behaviors. Therefore, AI designers often use simpler ''proxy goals'', such as Reinforcement learning from human feedback, gaining human approval. But proxy goals can overlook necessary constraints or reward the AI system for merely ''appearing'' aligned. AI systems may also find loopholes that allow them to accomplish their proxy goals efficiently but in unintended, sometimes harmful, ways (reward hacking). Advanced AI systems may develop unwanted Instrumental convergence, instrumental strategies, such as seeking power or survival because s ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Analogy
Analogy is a comparison or correspondence between two things (or two groups of things) because of a third element that they are considered to share. In logic, it is an inference or an argument from one particular to another particular, as opposed to deduction, induction, and abduction. It is also used where at least one of the premises, or the conclusion, is general rather than particular in nature. It has the general form ''A is to B as C is to D''. In a broader sense, analogical reasoning is a cognitive process of transferring some information or meaning of a particular subject (the analog, or source) onto another (the target); and also the linguistic expression corresponding to such a process. The term analogy can also refer to the relation between the source and the target themselves, which is often (though not always) a similarity, as in the biological notion of analogy. Analogy plays a significant role in human thought processes. It has been argued that analogy li ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
