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Script theory is a psychological theory which posits that human behaviour largely falls into patterns called "scripts" because they function analogously to the way a written script does, by providing a program for action. Silvan Tomkins created script theory as a further development of his affect theory, which regards human beings' emotional responses to stimuli as falling into categories called "affects": he noticed that the purely biological response of affect may be followed by awareness and by what we cognitively do in terms of acting on that affect so that more was needed to produce a complete explanation of what he called "human being theory".
In script theory, the basic unit of analysis is called a "scene", defined as a sequence of events linked by the affects triggered during the experience of those events. Tomkins recognized that our affective experiences fall into patterns that we may group together according to criteria such as the types of persons and places involved and the degree of intensity of the effect experienced, the patterns of which constitute scripts that inform our behavior in an effort to maximize positive affect and to minimize negative affect.
Roger Schank, Robert P. Abelson and their research group, extended Tomkins' scripts and used them in early artificial intelligence work as a method of representing procedural knowledge. [1] In their work, scripts are very much like frames, except the values that fill the slots must be ordered. A script is a structured representation describing a stereotyped sequence of events in a particular context. Scripts are used in natural-language understanding systems to organize a knowledge base in terms of the situations that the system should understand.
The classic example of a script involves the typical sequence of events that occur when a person drinks in a restaurant: finding a seat, reading the menu, ordering drinks from the waitstaff... In the script form, these would be decomposed into conceptual transitions, such as MTRANS and PTRANS, which refer to mental transitions [of information] and physical transitions [of things].
Schank, Abelson and their colleagues tackled some of the most difficult problems in artificial intelligence (i.e., story understanding), but ultimately their line of work ended without tangible success. This type of work received little attention after the 1980s, but it is very influential in later knowledge representation techniques, such as case-based reasoning.
Scripts can be inflexible. To deal with inflexibility, smaller modules called memory organization packets (MOP) can be combined in a way that is appropriate for the situation.
Cognitive science is the interdisciplinary, scientific study of the mind and its processes with input from linguistics, psychology, neuroscience, philosophy, computer science/artificial intelligence, and anthropology. It examines the nature, the tasks, and the functions of cognition. Cognitive scientists study intelligence and behavior, with a focus on how nervous systems represent, process, and transform information. Mental faculties of concern to cognitive scientists include language, perception, memory, attention, reasoning, and emotion; to understand these faculties, cognitive scientists borrow from fields such as linguistics, psychology, artificial intelligence, philosophy, neuroscience, and anthropology. The typical analysis of cognitive science spans many levels of organization, from learning and decision to logic and planning; from neural circuitry to modular brain organization. One of the fundamental concepts of cognitive science is that "thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures."
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.
Natural-language understanding (NLU) or natural-language interpretation (NLI) is a subtopic of natural-language processing in artificial intelligence that deals with machine reading comprehension. Natural-language understanding is considered an AI-hard problem.
Neat and scruffy are two contrasting approaches to artificial intelligence (AI) research. The distinction was made in the 70s and was a subject of discussion until the middle 80s. In the 1990s and 21st century AI research adopted "neat" approaches almost exclusively and these have proven to be the most successful.
In psychology and cognitive science, a schema describes a pattern of thought or behavior that organizes categories of information and the relationships among them. It can also be described as a mental structure of preconceived ideas, a framework representing some aspect of the world, or a system of organizing and perceiving new information. Schemata influence attention and the absorption of new knowledge: people are more likely to notice things that fit into their schema, while re-interpreting contradictions to the schema as exceptions or distorting them to fit. Schemata have a tendency to remain unchanged, even in the face of contradictory information. Schemata can help in understanding the world and the rapidly changing environment. People can organize new perceptions into schemata quickly as most situations do not require complex thought when using schema, since automatic thought is all that is required.
Roger Carl Schank is an American artificial intelligence theorist, cognitive psychologist, learning scientist, educational reformer, and entrepreneur.
The Age of Intelligent Machines is a non-fiction book about artificial intelligence by inventor and futurist Ray Kurzweil. This was his first book and the Association of American Publishers named it the Most Outstanding Computer Science Book of 1990. It was reviewed in The New York Times and The Christian Science Monitor. The format is a combination of monograph and anthology with contributed essays by artificial intelligence experts such as Daniel Dennett, Douglas Hofstadter, and Marvin Minsky.
Affect theory is a theory that seeks to organize affects, sometimes used interchangeably with emotions or subjectively experienced feelings, into discrete categories and to typify their physiological, social, interpersonal, and internalized manifestations. The conversation about affect theory has been taken up in psychology, psychoanalysis, neuroscience, medicine, interpersonal communication, literary theory, critical theory, media studies, and gender studies, among other fields. Hence, affect theory is defined in different ways, depending on the discipline.
Silvan Solomon Tomkins was a psychologist and personality theorist who developed both affect theory and script theory. Following the publication of the third volume of his book Affect Imagery Consciousness in 1991, his body of work received renewed interest, leading to attempts by others to summarize and popularize his theories.
The following outline is provided as an overview of and topical guide to thought (thinking):
Robert Paul Abelson was a Yale University psychologist and political scientist with special interests in statistics and logic.
Computer audition (CA) or machine listening is general field of study of algorithms and systems for audio understanding by machine. Since the notion of what it means for a machine to "hear" is very broad and somewhat vague, computer audition attempts to bring together several disciplines that originally dealt with specific problems or had a concrete application in mind. The engineer Paris Smaragdis, interviewed in Technology Review, talks about these systems --"software that uses sound to locate people moving through rooms, monitor machinery for impending breakdowns, or activate traffic cameras to record accidents."
Frames are an artificial intelligence data structure used to divide knowledge into substructures by representing "stereotyped situations". They were proposed by Marvin Minsky in his 1974 article "A Framework for Representing Knowledge". Frames are the primary data structure used in artificial intelligence frame language; they are stored as ontologies of sets.
Hierarchical temporal memory (HTM) is a biologically constrained machine intelligence technology developed by Numenta. Originally described in the 2004 book On Intelligence by Jeff Hawkins with Sandra Blakeslee, HTM is primarily used today for anomaly detection in streaming data. The technology is based on neuroscience and the physiology and interaction of pyramidal neurons in the neocortex of the mammalian brain.
Affect consciousness refers to an individual's ability to consciously perceive, tolerate, reflect upon, and express affects. These four abilities are operationalized as degrees of awareness, tolerance, emotional (nonverbal) expression, and conceptual (verbal) expression of each of the following eleven affect categories:
Conceptual dependency theory is a model of natural language understanding used in artificial intelligence systems.
The history of natural language processing describes the advances of natural language processing. There is some overlap with the history of machine translation, the history of speech recognition, and the history of artificial intelligence.
The following outline is provided as an overview of and topical guide to natural language processing:
This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.
The Usage-based linguistics is a linguistics approach within a broader functional/cognitive framework, that emerged since the late 1980s, and that assumes a profound relation between linguistic structure and usage. It challenges the dominant focus, in 20th century linguistics, on considering language as an isolated system removed from its use in human interaction and human cognition. Rather, usage-based models posit that linguistic information is expressed via context-sensitive mental processing and mental representations, which have the cognitive ability to succinctly account for the complexity of actual language use at all levels. Broadly speaking, a usage-based model of language accounts for language acquisition and processing, synchronic and diachronic patterns, and both low-level and high-level structure in language, by looking at actual language use.