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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 the capability of computer, computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. It is a field of research in computer science that develops and studies methods and software that enable machines to machine perception, perceive their environment and use machine learning, learning and intelligence to take actions that maximize their chances of achieving defined goals. High-profile applications of AI include advanced web search engines (e.g., Google Search); recommendation systems (used by YouTube, Amazon (company), Amazon, and Netflix); virtual assistants (e.g., Google Assistant, Siri, and Amazon Alexa, Alexa); autonomous vehicles (e.g., Waymo); Generative artificial intelligence, generative and Computational creativity, creative tools (e.g., ChatGPT and AI art); and Superintelligence, superhuman play and analysis in strategy games (e.g., ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knowledge Representation
Knowledge representation (KR) aims to model information in a structured manner to formally represent it as knowledge in knowledge-based systems whereas knowledge representation and reasoning (KRR, KR&R, or KR²) also aims to understand, reason, and interpret knowledge. KRR is widely used in the field of artificial intelligence (AI) with the goal to represent 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 natural-language dialog. KR incorporates findings from psychology about how humans solve problems and represent knowledge, in order to design formalisms that make complex systems easier to design and build. KRR also incorporates findings from logic to automate various kinds of ''reasoning''. Traditional KRR focuses more on the declarative representation of knowledge. Related knowledge representation formalisms mainly include vocabularies, thesaurus, semantic networks, axiom system ... [...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 ... [...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 productive physical 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 be ... [...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 states (as a factor in nuclear proliferation: "Some states disarm whilst others re-arm – both do it for the sake of our planet's peace"), and safe and sustainable disposal of nuclear waste as situations where considering in terms of ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Futures Techniques
Futures techniques used in the multi-disciplinary field of futurology by futurists in Americas and Australasia, and futurology by futurologists in EU, include a diverse range of forecasting methods, including anticipatory thinking, backcasting, simulation, and visioning. Some of the anticipatory methods include, the delphi method, causal layered analysis, environmental scanning, morphological analysis, and scenario planning. Anticipatory thinking protocols Delphi method The Delphi method is a popular technique used in futurology. It was developed by Gordon and Helmer in 1953 at RAND. It can be defined as a method for structuring a group communication process, so that the process is effective in allowing a group of individuals, as a whole, to deal with a complex problem. It uses the iterative, independent questioning of a panel of experts to assess the timing, probability, significance and implications of factors, trends and events in the relation to the problem bein ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mathematical Proof
A mathematical proof is a deductive reasoning, deductive Argument-deduction-proof distinctions, argument for a Proposition, 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 that establish logical certainty, to be distinguished from empirical evidence, empirical arguments or non-exhaustive inductive reasoning that 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 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cognitive Load
In cognitive psychology, cognitive load is the effort being used in the working memory. According to work conducted in the field of instructional design and pedagogy, broadly, there are three types of cognitive load: * ''Intrinsic'' cognitive load is the effort associated with a specific topic. * ''Germane cognitive load refers to the work put into creating a permanent store of knowledge (a schema).'' * ''Extraneous'' cognitive load refers to the way information or tasks are presented to a learner. However, over the years, the additivity of these types of cognitive load has been investigated and questioned. Now it is believed that they circularly influence each other. 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. ... [...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] |
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 argumentat ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Causal Layered Analysis
Causal layered analysis (CLA) is a future research theory that integrates various epistemic modes, creates spaces for alternative futures, and consists of four layers: litany, social, and structural, worldview, and myth/metaphor. The method was created by Sohail Inayatullah, a Pakistani-Australian futures studies researcher. Summary Causal layered analysis (CLA) is a theory and method that seeks to integrate empiricist, interpretive, critical, and action learning modes of research. In this method, forecasts, the meanings individuals give to these forecasts, the critical assumptions used, the narratives these are based on, and the actions and interventions that result are all valued and explored in CLA. This is true for both the external material world and the inner psychological world. As a method, its utility is not in predicting the future per se, as in creating transformative spaces for the analysis and the creation of alternative futures. It is also useful in developing more ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mathematical Proof
A mathematical proof is a deductive reasoning, deductive Argument-deduction-proof distinctions, argument for a Proposition, 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 that establish logical certainty, to be distinguished from empirical evidence, empirical arguments or non-exhaustive inductive reasoning that 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 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
