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Means–ends Analysis
Means–ends analysis (MEA) is a problem solving technique used commonly in artificial intelligence (AI) for limiting search in AI programs. It is also a technique used at least since the 1950s as a creativity tool, most frequently mentioned in engineering books on design methods. MEA is also related to means–ends chain approach used commonly in consumer behavior analysis. It is also a way to clarify one's thoughts when embarking on a mathematical proof. Problem-solving as search An important aspect of intelligent behavior as studied in AI is ''goal-based'' problem solving, a framework in which the solution to a problem can be described by finding a sequence of ''actions'' that lead to a desirable goal. A goal-seeking system is supposed to be connected to its outside environment by sensory channels through which it receives information about the environment and motor channels through which it acts on the environment. (The term "afferent" is used to describe "inward" sensory ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Artificial Intelligence
Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by animals and humans. Example tasks in which this is done include speech recognition, computer vision, translation between (natural) languages, as well as other mappings of inputs. The ''Oxford English Dictionary'' of Oxford University Press defines artificial intelligence as: the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. AI applications include advanced web search engines (e.g., Google), recommendation systems (used by YouTube, Amazon and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Tesla), automated decision-making and competing at the highest level in strategic game systems (such as chess and Go). ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Causal Layered Analysis
Causal layered analysis (CLA) is a technique used in strategic planning, futures studies and foresight to more effectively shape the future. The technique was pioneered by Sohail Inayatullah, a Pakistani-Australian futures studies researcher. Theory Causal layered analysis works by identifying many different levels, and attempting to make synchronized changes at all levels to create a coherent new future. Inayatullah's original paper as well as his TEDx talk identify four levels: # The litany: This includes quantitative trends, often exaggerated and used for political purposes. The result could be a feeling of apathy, helplessness, or projected action. Inayatullah calls this "the conventional level of futures research which can readily create a politics of fear." # Social causes, including economic, cultural, political, and historical factors. # Structure and the discourse that legitimizes and supports the structure. # Metaphor and myth History of research CLA was first intr ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Hill Climbing
numerical analysis, hill climbing is a mathematical optimization technique which belongs to the family of local search. It is an iterative algorithm that starts with an arbitrary solution to a problem, then attempts to find a better solution by making an incremental change to the solution. If the change produces a better solution, another incremental change is made to the new solution, and so on until no further improvements can be found. For example, hill climbing can be applied to the travelling salesman problem. It is easy to find an initial solution that visits all the cities but will likely be very poor compared to the optimal solution. The algorithm starts with such a solution and makes small improvements to it, such as switching the order in which two cities are visited. Eventually, a much shorter route is likely to be obtained. Hill climbing finds optimal solutions for convex problems – for other problems it will find only local optima (solutions that cannot be imp ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Gap Analysis
In management literature, gap analysis involves the comparison of actual performance with potential or desired performance. If an organization does not make the best use of current resources, or forgoes investment in capital or technology, it may produce or perform below an idealized potential. This concept is similar to an economy's production being below the production possibilities frontier. Gap analysis identifies gaps between the optimized allocation and integration of the inputs (resources), and the current allocation-level. This reveals areas that can be improved. Gap analysis involves determining, documenting and improving the difference between business requirements and current capabilities. Gap analysis naturally flows from benchmarking and from other assessments. Once the general expectation of performance in an industry is understood, it is possible to compare that expectation with the company's current level of performance. This comparison becomes the gap analysis. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Polytely
__NOTOC__ Polytely (from Greek roots ''poly-'' and ''-tel-'' meaning "many goals") comprises complex problem-solving situations characterized by the presence of multiple simultaneous goals.Funke 2001, p.72. These goals may be contradictory or otherwise conflict with one another, requiring prioritisation of desired outcomes.Funke 2001, p.72. Polytely is a feature of complex problem-solving that adds difficulty to finding an optimum solution. Funke describes polytely as a feature "not... inherent in a system, but eferringto certain decisions of the experimenter", especially decisions relating to what goals are to be followed in solving the problem.Funke 2001, p.73. In the complex problem of nuclear waste disposal, Flüeler cites both trust between state State may refer to: Arts, entertainment, and media Literature * ''State Magazine'', a monthly magazine published by the U.S. Department of State * ''The State'' (newspaper), a daily newspaper in Columbia, South Carolina, United St ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mathematical Proof
A mathematical proof is an inferential argument for a mathematical statement, showing that the stated assumptions logically guarantee the conclusion. The argument may use other previously established statements, such as theorems; but every proof can, in principle, be constructed using only certain basic or original assumptions known as axioms, along with the accepted rules of inference. Proofs are examples of exhaustive deductive reasoning which establish logical certainty, to be distinguished from empirical arguments or non-exhaustive inductive reasoning which establish "reasonable expectation". Presenting many cases in which the statement holds is not enough for a proof, which must demonstrate that the statement is true in ''all'' possible cases. A proposition that has not been proved but is believed to be true is known as a conjecture, or a hypothesis if frequently used as an assumption for further mathematical work. Proofs employ logic expressed in mathematical symbols ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cognitive Load
In cognitive psychology, cognitive load refers to the amount of working memory resources used. There are three types of cognitive load: ''intrinsic'' cognitive load is the effort associated with a specific topic; ''extraneous'' cognitive load refers to the way information or tasks are presented to a learner; and ''germane'' cognitive load refers to the work put into creating a permanent store of knowledge (a schema). Cognitive load theory was developed in the late 1980s out of a study of problem solving by John Sweller. Sweller argued that instructional design can be used to reduce cognitive load in learners. Much later, other researchers developed a way to measure perceived mental effort which is indicative of cognitive load. Task-invoked pupillary response is a reliable and sensitive measurement of cognitive load that is directly related to working memory. Information may only be stored in long term memory after first being attended to, and processed by, working memory. Workin ... [...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 function ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Automated Reasoning
In computer science, in particular in knowledge representation and reasoning and metalogic, the area of automated reasoning is dedicated to understanding different aspects of reasoning. The study of automated reasoning helps produce computer programs that allow computers to reason completely, or nearly completely, automatically. Although automated reasoning is considered a sub-field of artificial intelligence, it also has connections with theoretical computer science and philosophy. The most developed subareas of automated reasoning are automated theorem proving (and the less automated but more pragmatic subfield of interactive theorem proving) and automated proof checking (viewed as guaranteed correct reasoning under fixed assumptions). Extensive work has also been done in reasoning by analogy using induction and abduction. Other important topics include reasoning under uncertainty and non-monotonic reasoning. An important part of the uncertainty field is that of argumentation, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knowledge Representation
Knowledge representation and reasoning (KRR, KR&R, KR²) is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of ''reasoning'', such as the application of rules or the relations of sets and subsets. Examples of knowledge representation formalisms include semantic nets, systems architecture, frames, rules, and ontologies. Examples of automated reasoning engines include inference engines, theorem provers, and classifiers. History The earliest work in computerized knowledge represe ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Multilevel Flow Modeling
Multilevel Flow Modeling (MFM) is a framework for modeling industrial processes. MFM is a kind of functional modeling employing the concepts of abstraction, decomposition, and functional representation. The approach regards the purpose, rather than the physical behavior of a system as its defining element. MFM hierarchically decomposes the function of a system along the means-end and whole-part dimensions in relation to intended actions. Functions are syntactically modeled by the relations of fundamental concepts contributing as part of a subsystem. Each subsystem is considered in the context of the overall system in terms of the purpose (end) of its function (means) in the system. Using only a few fundamental concepts as building blocks allows qualitative reasoning about action success or failure. MFM defines a graphical modeling language for representing the encompassed knowledge. History MFM originated as a modeling language for capturing how human operators identify and ha ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mathematical Proof
A mathematical proof is an inferential argument for a mathematical statement, showing that the stated assumptions logically guarantee the conclusion. The argument may use other previously established statements, such as theorems; but every proof can, in principle, be constructed using only certain basic or original assumptions known as axioms, along with the accepted rules of inference. Proofs are examples of exhaustive deductive reasoning which establish logical certainty, to be distinguished from empirical arguments or non-exhaustive inductive reasoning which establish "reasonable expectation". Presenting many cases in which the statement holds is not enough for a proof, which must demonstrate that the statement is true in ''all'' possible cases. A proposition that has not been proved but is believed to be true is known as a conjecture, or a hypothesis if frequently used as an assumption for further mathematical work. Proofs employ logic expressed in mathematical symbols ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
