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Abductive reasoning

Abductive reasoning
Abductive reasoning (also called abduction,[1] abductive inference[2] or retroduction[3]) is a form of logical inference that goes from an observation to a hypothesis that accounts for the observation, ideally seeking to find the simplest and most likely explanation. In abductive reasoning, unlike in deductive reasoning, the premises do not guarantee the conclusion. One can understand abductive reasoning as "inference to the best explanation".[4] The fields of law,[5] computer science, and artificial intelligence research[6] renewed interest in the subject of abduction. Diagnostic expert systems frequently employ abduction. History[edit] The American philosopher Charles Sanders Peirce (1839–1914) first introduced the term as "guessing".[7] Peirce said that to abduce a hypothetical explanation from an observed circumstance is to surmise that may be true because then would be a matter of course.[8] Thus, to abduce from involves determining that is sufficient, but not necessary, for allows deriving

Reason Psychologists and cognitive scientists have attempted to study and explain how people reason, e.g. which cognitive and neural processes are engaged, and how cultural factors affect the inferences that people draw. The field of automated reasoning studies how reasoning may or may not be modeled computationally. Animal psychology considers the question of whether animals other than humans can reason. Etymology and related words[edit] In the English language and other modern European languages, "reason", and related words, represent words which have always been used to translate Latin and classical Greek terms in the sense of their philosophical usage. The original Greek term was "λόγος" logos, the root of the modern English word "logic" but also a word which could mean for example "speech" or "explanation" or an "account" (of money handled).[7]As a philosophical term logos was translated in its non-linguistic senses in Latin as ratio. Philosophical history[edit] Classical philosophy[edit]

Trying to get my head around "design thinking" I have to admit that I’ve been steering clear of talking about “design thinking” for a while now. A couple years back, when I first heard about what sounded like an exciting new angle on design strategy, I eagerly scoured the web to figure out what it was all about. At Cooper, we’ve always concerned ourselves with challenges beyond skin-deep ornamentation, and we particularly relish working for clients who value the insights that we can bring to their strategic business decisions. I’m interested in anything that gives us leverage to help businesses get beyond the assumptions that stand in the way of truly serving human needs. So when I set off to learn more, I was a bit disappointed to discover that all the information I could find about “design thinking” appeared to prominently feature the Keeley triangle, some business success stories and not a lot more. The Keeley Triangle. To be clear, I have no argument with the Keeley triangle.

Inductive reasoning Inductive reasoning (as opposed to deductive reasoning or abductive reasoning) is reasoning in which the premises seek to supply strong evidence for (not absolute proof of) the truth of the conclusion. While the conclusion of a deductive argument is certain, the truth of the conclusion of an inductive argument is probable, based upon the evidence given.[1] The philosophical definition of inductive reasoning is more nuanced than simple progression from particular/individual instances to broader generalizations. Many dictionaries define inductive reasoning as reasoning that derives general principles from specific observations, though some sources disagree with this usage.[2] Description[edit] Inductive reasoning is inherently uncertain. An example of an inductive argument: 90% of biological life forms that we know of depend on liquid water to exist. Therefore, if we discover a new biological life form it will probably depend on liquid water to exist. Inductive vs. deductive reasoning[edit]

The Eight Pillars of Innovation The greatest innovations are the ones we take for granted, like light bulbs, refrigeration and penicillin. But in a world where the miraculous very quickly becomes common-place, how can a company, especially one as big as Google, maintain a spirit of innovation year after year? Nurturing a culture that allows for innovation is the key. At that time I was Head of Marketing (a group of one), and over the past decade I’ve been lucky enough to work on a wide range of products. What’s different is that, even as we dream up what’s next, we face the classic innovator’s dilemma: should we invest in brand new products, or should we improve existing ones? "As we’ve grown to over 26,000 employees in more than 60 offices, we’ve worked hard to maintain the unique spirit that characterized Google way back when I joined as employee #16." Have a mission that matters Work can be more than a job when it stands for something you care about. Think big but start small The best part of working on the web?

Deductive reasoning Deductive reasoning links premises with conclusions. If all premises are true, the terms are clear, and the rules of deductive logic are followed, then the conclusion reached is necessarily true. Deductive reasoning (top-down logic) contrasts with inductive reasoning (bottom-up logic) in the following way: In deductive reasoning, a conclusion is reached reductively by applying general rules that hold over the entirety of a closed domain of discourse, narrowing the range under consideration until only the conclusion(s) is left. In inductive reasoning, the conclusion is reached by generalizing or extrapolating from, i.e., there is epistemic uncertainty. Note, however, that the inductive reasoning mentioned here is not the same as induction used in mathematical proofs – mathematical induction is actually a form of deductive reasoning. Simple example[edit] An example of a deductive argument: All men are mortal.Socrates is a man.Therefore, Socrates is mortal. Law of detachment[edit] P → Q.

Project Re: Brief Case Study The core challenge for Fi was to plan, design and develop an interactive experience that smoothly delivered a wide array of content across four clearly branded sections. Four complex sets of branded content, a feature-length documentary, and the requisite global navigation elements combined to present a unique challenge for our UX & Design teams. How do you craft a digital experience that delivers all the wonder and beauty of a disparate array of creative content without crowding the interface or confusing the user? Creative fuel

Logical reasoning Informally, two kinds of logical reasoning can be distinguished in addition to formal deduction: induction and abduction. Given a precondition or premise, a conclusion or logical consequence and a rule or material conditional that implies the conclusion given the precondition, one can explain that: Deductive reasoning determines whether the truth of a conclusion can be determined for that rule, based solely on the truth of the premises. Example: "When it rains, things outside get wet. The grass is outside, therefore: when it rains, the grass gets wet." Mathematical logic and philosophical logic are commonly associated with this style of reasoning.Inductive reasoning attempts to support a determination of the rule. See also[edit] References[edit] T.

Defeasible reasoning Defeasible reasoning is a kind of reasoning that is based on reasons that are defeasible, as opposed to the indefeasible reasons of deductive logic. Defeasible reasoning is a particular kind of non-demonstrative reasoning, where the reasoning does not produce a full, complete, or final demonstration of a claim, i.e., where fallibility and corrigibility of a conclusion are acknowledged. In other words defeasible reasoning produces a contingent statement or claim. Other kinds of non-demonstrative reasoning are probabilistic reasoning, inductive reasoning, statistical reasoning, abductive reasoning, and paraconsistent reasoning. Defeasible reasoning is also a kind of ampliative reasoning because its conclusions reach beyond the pure meanings of the premises. The differences between these kinds of reasoning correspond to differences about the conditional that each kind of reasoning uses, and on what premise (or on what authority) the conditional is adopted: History[edit] Specificity[edit]

Subjective logic Subjective logic is a type of probabilistic logic that explicitly takes uncertainty and belief ownership into account. In general, subjective logic is suitable for modeling and analysing situations involving uncertainty and incomplete knowledge.[1][2] For example, it can be used for modeling trust networks and for analysing Bayesian networks. Arguments in subjective logic are subjective opinions about propositions. A binomial opinion applies to a single proposition, and can be represented as a Beta distribution. A fundamental aspect of the human condition is that nobody can ever determine with absolute certainty whether a proposition about the world is true or false. Subjective opinions[edit] Subjective opinions express subjective beliefs about the truth of propositions with degrees of uncertainty, and can indicate subjective belief ownership whenever required. where is the subject, also called the belief owner, and is the proposition to which the opinion applies. . Binomial opinions[edit]

Knowledge representation and reasoning Knowledge representation and reasoning (KR) is the field of artificial intelligence (AI) devoted to representing information about the world in a form that a computer system can utilize 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, Frames, Rules, and ontologies. Examples of automated reasoning engines include inference engines, theorem provers, and classifiers. Overview[edit] This hypothesis was not always taken as a given by researchers. History[edit] Characteristics[edit]

Inference Inference is the act or process of deriving logical conclusions from premises known or assumed to be true.[1] The conclusion drawn is also called an idiomatic. The laws of valid inference are studied in the field of logic. Alternatively, inference may be defined as the non-logical, but rational means, through observation of patterns of facts, to indirectly see new meanings and contexts for understanding. Human inference (i.e. how humans draw conclusions) is traditionally studied within the field of cognitive psychology; artificial intelligence researchers develop automated inference systems to emulate human inference. Statistical inference uses mathematics to draw conclusions in the presence of uncertainty. Examples[edit] Greek philosophers defined a number of syllogisms, correct three part inferences, that can be used as building blocks for more complex reasoning. All men are mortalSocrates is a manTherefore, Socrates is mortal. Now we turn to an invalid form. All apples are fruit. ? ? P.