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Problem shaping means revising a question so that the solution process can begin or continue. It is part of the larger problem process that includes problem finding and problem solving. Problem shaping (or problem framing) often involves the application of critical thinking.
Algorithmic approach to technical problems reformulation was introduced by G. S. Altshuller in ARIZ. [1]
Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by non-human 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.
Knowledge representation and reasoning 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.
In their most common sense, the terms thought and thinking refer to conscious cognitive processes that can happen independently of sensory stimulation. Their most paradigmatic forms are judging, reasoning, concept formation, problem solving, and deliberation. But other mental processes, like considering an idea, memory, or imagination, are also often included. These processes can happen internally independent of the sensory organs, unlike perception. But when understood in the widest sense, any mental event may be understood as a form of thinking, including perception and unconscious mental processes. In a slightly different sense, the term thought refers not to the mental processes themselves but to mental states or systems of ideas brought about by these processes.
Heuristic, or heuristic technique, is any approach to problem solving or self-discovery that employs a practical method that is not guaranteed to be optimal, perfect, or rational, but is nevertheless sufficient for reaching an immediate, short-term goal or approximation. Where finding an optimal solution is impossible or impractical, heuristic methods can be used to speed up the process of finding a satisfactory solution. Heuristics can be mental shortcuts that ease the cognitive load of making a decision.
Critical thinking is the analysis of available facts, evidence, observations, and arguments to form a judgment. The subject is complex; several different definitions exist, which generally include the rational, skeptical, and unbiased analysis or evaluation of factual evidence. Critical thinking is self-directed, self-disciplined, self-monitored, and self-corrective thinking, and accordingly, a critical thinker is one who practices the skills of critical thinking or has been schooled in its disciplines. Richard W. Paul has suggested that the mind of a critical thinker engages both the intellectual abilities and personal traits necessary for critical thinking. Critical thinking presupposes assent to rigorous standards of excellence and mindful command of their use. It entails effective communication and problem-solving abilities as well as a commitment to overcome native egocentrism and sociocentrism.
TRIZ is “the next evolutionary step in creating an organized and systematic approach to problem solving. The development and improvement of products and technologies according to TRIZ are guided by the objective Laws of Engineering System Evolution. TRIZ Problem Solving Tools and Methods are based on them.” In another description, TRIZ is "a problem-solving, analysis and forecasting tool derived from the study of patterns of invention in the global patent literature". It was developed by the Soviet inventor and science-fiction author Genrich Altshuller (1926-1998) and his colleagues, beginning in 1946. In English the name is typically rendered as the theory of inventive problem solving, and occasionally goes by the English acronym TIPS.
How to Solve It (1945) is a small volume by mathematician George Pólya describing methods of problem solving.
In artificial intelligence, symbolic artificial intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. Symbolic AI used tools such as logic programming, production rules, semantic nets and frames, and it developed applications such as knowledge-based systems, symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems.
Creative problem-solving (CPS) is the mental process of searching for an original and previously unknown solution to a problem. To qualify, the solution must be novel and reached independently. The creative problem-solving process was originally developed by Alex Osborn and Sid Parnes.
Creativity techniques are methods that encourage creative actions, whether in the arts or sciences. They focus on a variety of aspects of creativity, including techniques for idea generation and divergent thinking, methods of re-framing problems, changes in the affective environment and so on. They can be used as part of problem solving, artistic expression, or therapy.
In artificial intelligence (AI), commonsense reasoning is a human-like ability to make presumptions about the type and essence of ordinary situations humans encounter every day. These assumptions include judgments about the nature of physical objects, taxonomic properties, and peoples' intentions. A device that exhibits commonsense reasoning might be capable of drawing conclusions that are similar to humans' folk psychology and naive physics.
Piaget's theory of cognitive development is a comprehensive theory about the nature and development of human intelligence. It was originated by the Swiss developmental psychologist Jean Piaget (1896–1980). The theory deals with the nature of knowledge itself and how humans gradually come to acquire, construct, and use it. Piaget's theory is mainly known as a developmental stage theory.
Problem solving is the process of achieving a goal by overcoming obstacles, a frequent part of most activities. Problems in need of solutions range from simple personal tasks to complex issues in business and technical fields. The former is an example of simple problem solving (SPS) addressing one issue, whereas the latter is complex problem solving (CPS) with multiple interrelated obstacles. Another classification is into well-defined problems with specific obstacles and goals, and ill-defined problems in which the current situation is troublesome but it is not clear what kind of resolution to aim for. Similarly, one may distinguish formal or fact-based problems requiring psychometric intelligence, versus socio-emotional problems which depend on the changeable emotions of individuals or groups, such as tactful behavior, fashion, or gift choices.
This is an index of education articles.
Problem finding means problem discovery. It is part of the larger problem process that includes problem shaping and problem solving. Problem finding requires intellectual vision and insight into what is missing. Problem finding plays a major role in application of creativity.
In artificial intelligence research, commonsense knowledge consists of facts about the everyday world, such as "Lemons are sour", or "Cows say moo", that all humans are expected to know. It is currently an unsolved problem in Artificial General Intelligence. The first AI program to address common sense knowledge was Advice Taker in 1959 by John McCarthy.
Unified Structured Inventive Thinking (USIT) is a structured, problem-solving methodology for finding innovative solution concepts to engineering-design type problems. Historically, USIT is related to Systematic Inventive Thinking (SIT), which originated in Israel and is related to TRIZ, the Russian methodology. It differs from TRIZ in several ways, but most importantly it is a simpler methodology, which makes it quicker to learn and easier to apply. It requires no databases or computer software.
The following outline is provided as an overview of and topical guide to thought (thinking):
Systematic Inventive Thinking (SIT) is a thinking method developed in Israel in the mid-1990s. Derived from Genrich Altshuller’s TRIZ engineering discipline, SIT is a practical approach to creativity, innovation and problem solving, which has become a well known methodology for innovation. At the heart of SIT’s method is one core idea adopted from Genrich Altshuller's TRIZ which is also known as Theory of Inventive Problem Solving (TIPS): that inventive solutions share common patterns. Focusing not on what makes inventive solutions different – but on what they share in common – is core to SIT’s approach.
Thomas L. Griffiths is an Australian academic who is the Henry R. Luce Professor of Information Technology, Consciousness, and Culture at Princeton University. He studies human decision-making and its connection to problem-solving methods in computation. His book with Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions, was named one of the "Best Books of 2016" by MIT Technology Review.
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