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112-449: CHREST stores its memories in a chunking network, a tree-like structure that connects and stores knowledge and information acquired, allowing for greater efficiency in information processing. Figure 1 highlights the links between perceived knowledge, memory, and acquired experiences that are formed based on “familiar patterns” between new and old information. CHREST is developed by Fernand Gobet at Brunel University and Peter C. Lane at

224-581: A loss function . Variants of gradient descent are commonly used to train neural networks. Another type of local search is evolutionary computation , which aims to iteratively improve a set of candidate solutions by "mutating" and "recombining" them, selecting only the fittest to survive each generation. Distributed search processes can coordinate via swarm intelligence algorithms. Two popular swarm algorithms used in search are particle swarm optimization (inspired by bird flocking ) and ant colony optimization (inspired by ant trails ). Formal logic

336-475: A "degree of truth" between 0 and 1. It can therefore handle propositions that are vague and partially true. Non-monotonic logics , including logic programming with negation as failure , are designed to handle default reasoning . Other specialized versions of logic have been developed to describe many complex domains. Many problems in AI (including in reasoning, planning, learning, perception, and robotics) require

448-668: A brief exposure to pieces on a chessboard, skilled chess players were able to encode and recall much larger chunks than novice chess players. However, this effect is mediated by specific knowledge of the rules of chess; when pieces were distributed randomly (including scenarios that were not common or allowed in real games), the difference in chunk size between skilled and novice chess players was significantly reduced. Several successful computational models of learning and expertise have been developed using this idea, such as EPAM (Elementary Perceiver and Memorizer) and CHREST (Chunk Hierarchy and Retrieval Structures). Chunking may be demonstrated in

560-410: A chess position to a subject for a short period of time, usually for 5 seconds, then asking subjects to recreate the position. Common independent variables in this methodology are the skill level of the subject, time spent illustrating the position, and the general depth and significance of the position. In the domain of perception, simulations of eye movement during the initial 5 seconds of illustrating

672-445: A chess position to a subject for a short period of time, usually for 5 seconds, then asking subjects to recreate the position. Common independent variables in this methodology are the skill level of the subject, time spent illustrating the position, and the general depth and significance of the position. Though this methodology has generated a substantial amount of high-level models addressing memory and cognition in chess play, exampled by

784-864: A chess position, as well as recognition of templates and chunks have been completed using CHREST. CHREST also accounts for the outcome when presented with varying modifications and randomisation of positions, the significance of time spent illustrating and presenting each position, and the categorisation of the errors made and chunks replaced in the network across varying skill levels from novice-level players to grandmasters. Extensive research has been conducted by N Charness on chess and general expertise, problem-solving strategies and memorisation by population groups of different ages. Tests for memorisation and recall revealed that younger players performed better relative to older players when presented with varying chess positions. Charness noted that though older players performed worse relative to younger players when both parties were on

896-399: A chunk is given, it is stored as a single item despite being a relatively large amount of information. This finding suggests that chunks should be less susceptible to decay or interference when they are recalled. The study used visual stimuli where all the items were given simultaneously. Items of two and three were found to be recalled easier than singles, and more singles were recalled when in

1008-423: A chunk of information." Miller (1956) noted that according to this theory, it should be possible to increase short-term memory for low-information-content items effectively by mentally recoding them into a smaller number of high-information-content items. He imagined this process is useful in scenarios such as "a man just beginning to learn radio-telegraphic code hears each dit and dah as a separate chunk. Soon he

1120-460: A contradiction from premises that include the negation of the problem to be solved. Inference in both Horn clause logic and first-order logic is undecidable , and therefore intractable . However, backward reasoning with Horn clauses, which underpins computation in the logic programming language Prolog , is Turing complete . Moreover, its efficiency is competitive with computation in other symbolic programming languages. Fuzzy logic assigns

1232-491: A distinction between the notions of input and output chunks from the ideas of short-term and long-term memory. Input chunks reflect the limitation of working memory during the encoding of new information (how new information is stored in long-term memory), and how it is retrieved during subsequent recall. Output chunks reflect the organization of over-learned motor programs that are generated on-line in working memory. Sakai et al. (2003) showed that participants spontaneously organize

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1344-486: A greater capacity for the alphabets; he did not. Based on these contradictions, Ericsson et al. (1980) later concluded that S. F. was able to increase his digit span due to "the use of mnemonic associations in long-term memory," which further supports that chunking may exist in short-term memory rather than long-term memory. Chunking has also been used with models of language acquisition. The use of chunk-based learning in language has been shown to be helpful. Understanding

1456-402: A group of basic words and then giving different categories of associated words to build on comprehension has shown to be an effective way to teach reading and language to children. Research studies have found that adults and infants were able to parse the words of a made-up language when they were exposed to a continuous auditory sequence of words arranged in random order. One of the explanations

1568-595: A group with threes. Chunking can be a form of data suppression that allows for more information to be stored in short-term memory. Rather than verbal short-memory measured by the number of items stored, Miller (1956) suggested that verbal short-term memory are stored as chunks. Later studies were done to determine if chunking was a form data compression when there is limited space for memory. Chunking works as data compression when it comes to redundant information and it allows for more information to be stored in short-term memory. However, memory capacity may vary. An experiment

1680-412: A linear sequence is simple from a storage point of view, there can be potential problems during retrieval. For instance, if there is a break in the sequence chain, subsequent elements will become inaccessible. On the other hand, a hierarchical representation would have multiple levels of representation. A break in the link between lower-level nodes does not render any part of the sequence inaccessible, since

1792-442: A master. Chase and Simon (1973a) discovered that the skill levels of chess players are attributed to long-term memory storage and the ability to copy and recollect thousands of chunks. The process helps acquire knowledge at a faster pace. Since it is an excellent tool for enhancing memory, a chess player who utilizes chunking has a higher chance of success. According to Chase and Simon, while re-examining (1973b), an expert chess master

1904-472: A memory mechanism is easily observed in the way individuals group numbers, and information, in day-to-day life. For example, when recalling a number such as 12101946, if numbers are grouped as 12, 10, and 1946, a mnemonic is created for this number as a month, day, and year. It would be stored as December 10, 1946, instead of a string of numbers. Similarly, another illustration of the limited capacity of working memory as suggested by George Miller can be seen from

2016-535: A mnemonic trick for extending the memory span, you will miss the more important point that is implicit in nearly all such mnemonic devices. The point is that recoding is an extremely powerful weapon for increasing the amount of information that we can deal with. Studies have shown that people have better memories when they are trying to remember items with which they are familiar. Similarly, people tend to create familiar chunks. This familiarity allows one to remember more individual pieces of content, and also more chunks as

2128-429: A path to a target goal, a process called means-ends analysis . Simple exhaustive searches are rarely sufficient for most real-world problems: the search space (the number of places to search) quickly grows to astronomical numbers . The result is a search that is too slow or never completes. " Heuristics " or "rules of thumb" can help prioritize choices that are more likely to reach a goal. Adversarial search

2240-478: A perception of their social group on their own. Infants can form chunks using shared features or spatial proximity between objects. Previous research shows that the mechanism of chunking is available in seven-month-old infants. This means that chunking can occur even before the working memory capacity has completely developed. Knowing that the working memory has a very limited capacity, it can be beneficial to utilize chunking. In infants, whose working memory capacity

2352-582: A person can also recall other non-chunked memories easier due to the benefits chunking has on the working memory. For instance, in one study, participants with more specialized knowledge could reconstruct sequences of chess moves because they had larger chunks of procedural knowledge, which means that the level of expertise and the sorting order of the information retrieved is essential in the influence of procedural knowledge chunks retained in short-term memory. Chunking has been shown to have an influence in linguistics , such as boundary perception. According to

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2464-439: A person who does not have knowledge in the expert domain would have difficulty chunking could also be seen in an experiment of novice and expert hikers to see if they could remember different mountain scenes. From this study, it was found that the expert hikers had better recall and recognition of structured stimuli. Another example could be seen with expert musicians in being able to chunk and recall encoded material that best meets

2576-506: A sequence into a number of chunks across a few sets and that these chunks were distinct among participants tested on the same sequence. They also demonstrated that the performance of a shuffled sequence was poorer when the chunk patterns were disrupted than when the chunk patterns were preserved. Chunking patterns also seem to depend on the effectors used. Perlman found in his series of experiments that tasks that are larger in size and broken down into smaller sections had faster respondents than

2688-407: A standardised testing protocol for studies involving perception, psychology, cognition, and human and artificial intelligence . The comprehensive use of chess play and chess mechanisms has been compared to the metaphor of the use of ‘ drosophila ’, the “organism of choice” for research in biological and chemical industries. Similarities between the domination of chess used as an experimental hotbed in

2800-441: A string of binary digits and (in one case) mentally group them into groups of five, recode each group into a name (for example, "twenty-one" for 10101), and remember the names. With sufficient practice, people found it possible to remember as many as forty binary digits. Miller wrote: It is a little dramatic to watch a person get 40 binary digits in a row and then repeat them back without error. However, if you think of this merely as

2912-425: A time in newborns and early toddlers. A study conducted in 2014, Infants use temporal regularities to chunk objects in memory , allowed for new information and knowledge. This research showed that 14-month-old infants, like adults, can chunk using their knowledge of object categories: they remembered four total objects when an array contained two tokens of two different types (e.g., two cats and two cars), but not when

3024-419: A time when information theory was beginning to be applied in psychology, Miller observed that some human cognitive tasks fit the model of a "channel capacity" characterized by a roughly constant capacity in bits, but short-term memory did not. A variety of studies could be summarized by saying that short-term memory had a capacity of about "seven plus-or-minus two" chunks. Miller (1956) wrote, "With binary items,

3136-726: A tool that can be used for reasoning (using the Bayesian inference algorithm), learning (using the expectation–maximization algorithm ), planning (using decision networks ) and perception (using dynamic Bayesian networks ). Probabilistic algorithms can also be used for filtering, prediction, smoothing, and finding explanations for streams of data, thus helping perception systems analyze processes that occur over time (e.g., hidden Markov models or Kalman filters ). The simplest AI applications can be divided into two types: classifiers (e.g., "if shiny then diamond"), on one hand, and controllers (e.g., "if diamond then pick up"), on

3248-400: A whole. One well-known chunking study was conducted by Chase and Ericsson, who worked with an undergraduate student, SF, for over two years. They wanted to see if a person's digit span memory could be improved with practice. SF began the experiment with a normal span of 7 digits. SF was a long-distance runner, and chunking strings of digits into race times increased his digit span. By the end of

3360-669: A wide range of techniques, including search and mathematical optimization , formal logic , artificial neural networks , and methods based on statistics , operations research , and economics . AI also draws upon psychology , linguistics , philosophy , neuroscience , and other fields. Artificial intelligence was founded as an academic discipline in 1956, and the field went through multiple cycles of optimism, followed by periods of disappointment and loss of funding, known as AI winter . Funding and interest vastly increased after 2012 when deep learning outperformed previous AI techniques. This growth accelerated further after 2017 with

3472-490: A wide variety of techniques to accomplish the goals above. AI can solve many problems by intelligently searching through many possible solutions. There are two very different kinds of search used in AI: state space search and local search . State space search searches through a tree of possible states to try to find a goal state. For example, planning algorithms search through trees of goals and subgoals, attempting to find

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3584-915: A “network of nodes”, and are interconnected by the similarity of their contents and are depicted as a discrimination network, storing and sorting chunks in the network. Chunks are essentially “clusters of information that can be used as units of perception”, thus when applied in situations of chess play, fragments and sections of chess positions will be used as the stimuli fed to the system. According to Gobet et al. and Smith et al., cognitive templates, or better known as schemas, form when chunks adapt based on recurring environmental patterns and structures. Templates are cognitive structures that represent environmental perception, allowing for cognitive organisation, recall, behavioural guidance, situational prediction and overall understanding. Each template has slots where values can be “slotted in”, which allows for faster understanding when faced with similar information already existing in

3696-1139: Is intelligence exhibited by machines , particularly computer systems . It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. Such machines may be called AIs. Some high-profile applications of AI include advanced web search engines (e.g., Google Search ); recommendation systems (used by YouTube , Amazon , and Netflix ); interacting via human speech (e.g., Google Assistant , Siri , and Alexa ); autonomous vehicles (e.g., Waymo ); generative and creative tools (e.g., ChatGPT , and AI art ); and superhuman play and analysis in strategy games (e.g., chess and Go ). However, many AI applications are not perceived as AI: "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore ." The various subfields of AI research are centered around particular goals and

3808-641: Is a body of knowledge represented in a form that can be used by a program. An ontology is the set of objects, relations, concepts, and properties used by a particular domain of knowledge. Knowledge bases need to represent things such as objects, properties, categories, and relations between objects; situations, events, states, and time; causes and effects; knowledge about knowledge (what we know about what other people know); default reasoning (things that humans assume are true until they are told differently and will remain true even when other facts are changing); and many other aspects and domains of knowledge. Among

3920-422: Is able to access information in long-term memory storage quickly due to the ability to recall chunks. Chunks stored in long-term memory are related to the decision of the movement of board pieces due to obvious patterns. Chunking models for education Many years of research has concluded that chunking is a reliable process for gaining knowledge and organization of information. Chunking provides explanation to

4032-449: Is able to organize these sounds into letters and then he can deal with the letters as chunks. Then the letters organize themselves as words, which are still larger chunks, and he begins to hear whole phrases." Thus, a telegrapher can effectively "remember" several dozen dits and dahs as a single phrase. Naïve subjects can remember a maximum of only nine binary items, but Miller reports a 1954 experiment in which people were trained to listen to

4144-465: Is able to quantitatively predict unambiguous outcomes (Gobet and Lane; Gobet). Additional research credited to Adriaan de Groot and Herbert Simon specifically in the domain of chess accounted for significant quantities of psychological data, with a strong focus on the memory of chess players. Prior to de Groot and Simon's theories and implementation, the standard paradigm for experimentation in chess play and chess research typically consists of illustrating

4256-404: Is an effective method to improve patients' verbal working memory performance. Patients with schizophrenia also experience working memory deficits which influence executive function; memory training procedures positively influence cognitive and rehabilitative outcomes. Chunking has been proven to decrease the load on the working memory in many ways. As well as remembering chunked information easier,

4368-459: Is an input, at least one hidden layer of nodes and an output. Each node applies a function and once the weight crosses its specified threshold, the data is transmitted to the next layer. A network is typically called a deep neural network if it has at least 2 hidden layers. Learning algorithms for neural networks use local search to choose the weights that will get the right output for each input during training. The most common training technique

4480-462: Is an interdisciplinary umbrella that comprises systems that recognize, interpret, process, or simulate human feeling, emotion, and mood . For example, some virtual assistants are programmed to speak conversationally or even to banter humorously; it makes them appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human–computer interaction . However, this tends to give naïve users an unrealistic conception of

4592-444: Is an unsolved problem. Knowledge representation and knowledge engineering allow AI programs to answer questions intelligently and make deductions about real-world facts. Formal knowledge representations are used in content-based indexing and retrieval, scene interpretation, clinical decision support, knowledge discovery (mining "interesting" and actionable inferences from large databases ), and other areas. A knowledge base

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4704-422: Is anything that perceives and takes actions in the world. A rational agent has goals or preferences and takes actions to make them happen. In automated planning , the agent has a specific goal. In automated decision-making , the agent has preferences—there are some situations it would prefer to be in, and some situations it is trying to avoid. The decision-making agent assigns a number to each situation (called

4816-532: Is believed that individuals create higher-order cognitive representations of the items within the chunk. The items are more easily remembered as a group than as the individual items themselves. These chunks can be highly subjective because they rely on an individual's perceptions and past experiences, which are linked to the information set. The size of the chunks generally ranges from two to six items but often differs based on language and culture. According to Johnson (1970), there are four main concepts associated with

4928-413: Is classified based on previous experience. There are many kinds of classifiers in use. The decision tree is the simplest and most widely used symbolic machine learning algorithm. K-nearest neighbor algorithm was the most widely used analogical AI until the mid-1990s, and Kernel methods such as the support vector machine (SVM) displaced k-nearest neighbor in the 1990s. The naive Bayes classifier

5040-451: Is involved in the recall of information in short-term memory. It may be easier to recall information in short-term memory if the information has been represented through chunking in long-term memory. Norris and Kalm (2021) argued that "reintegration can be achieved by treating recall from memory as a process of Bayesian inference whereby representations of chunks in LTM (long-term memory) provide

5152-413: Is labelled by a solution of the problem and whose leaf nodes are labelled by premises or axioms . In the case of Horn clauses , problem-solving search can be performed by reasoning forwards from the premises or backwards from the problem. In the more general case of the clausal form of first-order logic , resolution is a single, axiom-free rule of inference, in which a problem is solved by proving

5264-567: Is led and directed by pre-existing knowledge. This phenomenon is closely observed in chess experiments, where perception and eye movements are closely associated, while also being proportionate to attention span. This process is governed by the chunks held in heuristics and memory . In the case of chess experiments, perception is equated with eye movements (which are approximately correspondent to attention), which are directed by chunks held in memory and heuristics . Models based on CHREST have been used, among other things, to simulate data on

5376-481: Is not completely developed, it can be even more helpful to chunk memories. These studies were done using the violation-of-expectation method and recording the amount of time the infants watched the objects in front of them. Although the experiment showed that infants can use chunking, researchers also concluded that an infant's ability to chunk memories will continue to develop over the next year of their lives. Working memory appears to store no more than three objects at

5488-400: Is reportedly the "most widely used learner" at Google, due in part to its scalability. Neural networks are also used as classifiers. An artificial neural network is based on a collection of nodes also known as artificial neurons , which loosely model the neurons in a biological brain. It is trained to recognise patterns; once trained, it can recognise those patterns in fresh data. There

5600-404: Is the process of proving a new statement ( conclusion ) from other statements that are given and assumed to be true (the premises ). Proofs can be structured as proof trees , in which nodes are labelled by sentences, and children nodes are connected to parent nodes by inference rules . Given a problem and a set of premises, problem-solving reduces to searching for a proof tree whose root node

5712-551: Is used as a strategy, one can expect a higher proportion of correct recalls. Various kinds of memory training systems and mnemonics include training and drills in specially-designed recoding or chunking schemes. Such systems existed before Miller's paper, but there was no convenient term to describe the general strategy and no substantive and reliable research. The term "chunking" is now often used in reference to these systems. As an illustration, patients with Alzheimer's disease typically experience working memory deficits; chunking

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5824-440: Is used for game-playing programs, such as chess or Go. It searches through a tree of possible moves and counter-moves, looking for a winning position. Local search uses mathematical optimization to find a solution to a problem. It begins with some form of guess and refines it incrementally. Gradient descent is a type of local search that optimizes a set of numerical parameters by incrementally adjusting them to minimize

5936-455: Is used for reasoning and knowledge representation . Formal logic comes in two main forms: propositional logic (which operates on statements that are true or false and uses logical connectives such as "and", "or", "not" and "implies") and predicate logic (which also operates on objects, predicates and relations and uses quantifiers such as " Every X is a Y " and "There are some X s that are Y s"). Deductive reasoning in logic

6048-436: Is used in AI programs that make decisions that involve other agents. Machine learning is the study of programs that can improve their performance on a given task automatically. It has been a part of AI from the beginning. There are several kinds of machine learning. Unsupervised learning analyzes a stream of data and finds patterns and makes predictions without any other guidance. Supervised learning requires labeling

6160-905: Is when the knowledge gained from one problem is applied to a new problem. Deep learning is a type of machine learning that runs inputs through biologically inspired artificial neural networks for all of these types of learning. Computational learning theory can assess learners by computational complexity , by sample complexity (how much data is required), or by other notions of optimization . Natural language processing (NLP) allows programs to read, write and communicate in human languages such as English . Specific problems include speech recognition , speech synthesis , machine translation , information extraction , information retrieval and question answering . Early work, based on Noam Chomsky 's generative grammar and semantic networks , had difficulty with word-sense disambiguation unless restricted to small domains called " micro-worlds " (due to

6272-582: The University of Hertfordshire . It is the successor of EPAM , a cognitive model originally developed by Herbert A. Simon and Edward Feigenbaum . The architecture contains a number of capacity parameters (e.g., capacity of visual short-term memory , set at three chunks) and time parameters (e.g., time to learn a chunk or time to put information into short-term memory). This makes it possible to derive precise and quantitative predictions about human behaviour. The model includes interaction with elements in

6384-520: The bar exam , SAT test, GRE test, and many other real-world applications. Machine perception is the ability to use input from sensors (such as cameras, microphones, wireless signals, active lidar , sonar, radar, and tactile sensors ) to deduce aspects of the world. Computer vision is the ability to analyze visual input. The field includes speech recognition , image classification , facial recognition , object recognition , object tracking , and robotic perception . Affective computing

6496-416: The transformer architecture , and by the early 2020s hundreds of billions of dollars were being invested in AI (known as the " AI boom "). The widespread use of AI in the 21st century exposed several unintended consequences and harms in the present and raised concerns about its risks and long-term effects in the future, prompting discussions about regulatory policies to ensure the safety and benefits of

6608-436: The " utility ") that measures how much the agent prefers it. For each possible action, it can calculate the " expected utility ": the utility of all possible outcomes of the action, weighted by the probability that the outcome will occur. It can then choose the action with the maximum expected utility. In classical planning , the agent knows exactly what the effect of any action will be. In most real-world problems, however,

6720-631: The Bayesian model. PARSER is a chunking model designed to account for human behavior by implementing psychologically plausible processes of attention, memory, and associative learning. In a recent study, it was determined that these chunking models like PARSER are seen in infants more than chunking models like Bayesian. PARSER is seen more because it is typically endowed with the ability to process up to three chunks simultaneously. When it comes to infants using their social knowledge they need to use abstract knowledge and subtle cues because they can not create

6832-548: The CHREST model is limited by the parameters of human abilities understood to the current extent of cognitive psychology. Moreover, an over-focus on problem-solving and strategy has led to information categorisation, attention, and understanding of the stimulus being ignored. Time-restricted puzzles are simulated using a set of regulated parameters that are assumed to be closest to human behaviour. Time-related variables are commonly used in CHREST and its subsequent simulations, such as

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6944-560: The acquisition of a memory skill, which was demonstrated by S. F., an undergraduate student with average memory and intelligence, who increased his digit span from seven to almost 80 within 20 months or after at least 230 hours. S. F. was able to improve his digit span partly through mnemonic associations, which is a form of chunking. S. F. associated digits, which were unfamiliar information to him, with running times, ages, and dates, which were familiar information to him. Ericsson et al. (1980) initially hypothesized that S. F. increased digit span

7056-405: The acquisition of chess expertise from novice to grandmaster, children's acquisition of vocabulary , children's acquisition of syntactic structures , and concept formation. A glaring limitation of the CHREST theory is as proposed by Herbert Simon. Simon concluded models that attempted to simulate functioning cognition in humans must not assume properties that may be unrealistic for a human, thus

7168-547: The acquisition of knowledge by feeding stimuli within the specialisation of study. In the algorithm's learning phase, chunks and templates from databases containing moves, positions, and strategies from grandmaster and expert level games are gradually fed and synthesised as knowledge. Varying networks of nodes (or chunks) of different sizes are then created, which allows for simulations of chess play across diverse levels of skill. Parameters of time and human capacity are taken into account, thus ideally creating circumstances where CHREST

7280-421: The agent can seek information to improve its preferences. Information value theory can be used to weigh the value of exploratory or experimental actions. The space of possible future actions and situations is typically intractably large, so the agents must take actions and evaluate situations while being uncertain of what the outcome will be. A Markov decision process has a transition model that describes

7392-510: The agent may not be certain about the situation they are in (it is "unknown" or "unobservable") and it may not know for certain what will happen after each possible action (it is not "deterministic"). It must choose an action by making a probabilistic guess and then reassess the situation to see if the action worked. In some problems, the agent's preferences may be uncertain, especially if there are other agents or humans involved. These can be learned (e.g., with inverse reinforcement learning ), or

7504-529: The agent to operate with incomplete or uncertain information. AI researchers have devised a number of tools to solve these problems using methods from probability theory and economics. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory , decision analysis , and information value theory . These tools include models such as Markov decision processes , dynamic decision networks , game theory and mechanism design . Bayesian networks are

7616-517: The array contained four tokens of the same type (e.g., four different cats). It demonstrates that newborns may employ spatial closeness to tie representations of particular items into chunks, benefiting memory performance as a result. Despite the fact that newborns' working memory capacity is restricted, they may employ numerous forms of information to tie representations of individual things into chunks, enhancing memory efficiency. This usage derives from Miller's (1956) idea of chunking as grouping, but

7728-409: The behavior of experts, such as a teacher. A teacher can utilize chunking in their classroom as a way to teach the curriculum. Gobet (2005) proposed that teachers can use chunking as a method to segment the curriculum into natural components. A student learns better when focusing on key features of material, so it is important to create the segments to highlight the important information. By understanding

7840-413: The behaviour of players of different skill levels. Taken together with the presence of time and capacity parameters, this enables CHREST to make unambiguous and quantitative predictions. CHREST's notability lies in the significance placed on the perception process. The procedure of perception and information processing is passive, leading to complex emergent behaviour where the secondary acquisition process

7952-454: The case of London's taxi drivers, “structural plasticity in the hippocampus” is developed, creating “permanent changes in the brain” such as the expansion of the posterior hippocampal region relative to the average population. This change is achieved through memorisation and navigation of complicated routes and maps of London's urban area, leading to a rigid pattern of cognitive chunks that results in resistance to sudden modifications, as well as

8064-648: The common sense knowledge problem ). Margaret Masterman believed that it was meaning and not grammar that was the key to understanding languages, and that thesauri and not dictionaries should be the basis of computational language structure. Modern deep learning techniques for NLP include word embedding (representing words, typically as vectors encoding their meaning), transformers (a deep learning architecture using an attention mechanism), and others. In 2019, generative pre-trained transformer (or "GPT") language models began to generate coherent text, and by 2023, these models were able to get human-level scores on

8176-481: The control nodes (chunk nodes) at the higher level would still be able to facilitate access to the lower-level nodes. Chunks in motor learning are identified by pauses between successive actions in Terrace (2001). It is also suggested that during the sequence performance stage (after learning), participants download list items as chunks during pauses. He also argued for an operational definition of chunks suggesting

8288-433: The decline of the skill level of the older players as a consequence of reaching and passing their peak, and explicit comparison to a younger age group was complicated due to “prior learning and past experiences”, also referred to as “crystallised intelligence”. Prior to de Groot and Simon's theories and implementation, the standard paradigm for experimentation in chess play and chess research typically consists of illustrating

8400-402: The demands they are presented with at any given moment during the performance. Chunking and memory in chess revisited Previous research has shown that chunking is an effective tool for enhancing memory capacity due to the nature of grouping individual pieces into larger, more meaningful groups that are easier to remember. Chunking is a popular tool for people who play chess, specifically

8512-409: The development of “practised habits”. In the face of unfamiliar circumstances, the individual may depend on existing patterns and strategies despite if the knowledge may not be applicable. The plasticity of the information processing centre in the brain leads to potential “blind spots” when faced with situations that require visualisation external of preexisting patterns. The chess domain has long been

8624-401: The emphasis is now on long-term memory rather than only on short-term memory . A chunk can then be defined as "a collection of elements having strong associations with one another, but weak associations with elements within other chunks". The emphasis of chunking on long-term memory is supported by the idea that chunking only exists in long-term memory, but it assists with reintegration, which

8736-640: The experiment, his digit span had grown to 80 numbers. A later description of the research in The Brain-Targeted Teaching Model for 21st Century Schools states that SF later expanded his strategy by incorporating ages and years, but his chunks were always familiar, which allowed him to recall them more easily. Someone who does not have knowledge in the expert domain (e.g. being familiar with mile/marathon times) would have difficulty chunking with race times and ultimately be unable to memorize as many numbers using this method. The idea that

8848-429: The external world, short-term and long-term memory stores, in particular visual and verbal memory storage, and the individual's mechanisms with problem-solving. Chunks in CHREST are referenced in short-term memory while being held in long-term memory , often recognised through neural categorial perception involving discrimination. In much similarity to EPAM , chunks in cognition learning in long-term memory are acquired as

8960-419: The field of cognitive and computer sciences and the use of drosophila in genetic sciences research have been drawn up as chess has notably been identified as a “representative measure” of cognition and intelligence in both humans and computers. Common applications and simulations of the CHREST theory have been carried out extensively in the past within the context of chess play. The methodology involves allowing

9072-399: The following example: While recalling a mobile phone number such as 9849523450, we might break this into 98 495 234 50. Thus, instead of remembering 10 separate digits that are beyond the putative "seven plus-or-minus two" memory span, we are remembering four groups of numbers. An entire chunk can also be remembered simply by storing the beginnings of a chunk in the working memory, resulting in

9184-422: The information is grouped, are meant to improve short-term retention of the material, thus bypassing the limited capacity of working memory and allowing the working memory to be more efficient. A chunk is a collection of basic units that are strongly associated with one another, and have been grouped together and stored in a person's memory. These chunks can be retrieved easily due to their coherent grouping. It

9296-420: The information. Compressibility refers to making information more compact and condensed. The material is transformed from something complex to something more simplified. Thus, compressibility relates to chunking due to the predictability factor. As for the second factor, the sequence of the information can impact what is being discovered. So the order, along with the process of compressing the material, may increase

9408-440: The intelligence of existing computer agents. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal sentiment analysis , wherein AI classifies the affects displayed by a videotaped subject. A machine with artificial general intelligence should be able to solve a wide variety of problems with breadth and versatility similar to human intelligence . AI research uses

9520-537: The late 1980s and 1990s, methods were developed for dealing with uncertain or incomplete information, employing concepts from probability and economics . Many of these algorithms are insufficient for solving large reasoning problems because they experience a "combinatorial explosion": They become exponentially slower as the problems grow. Even humans rarely use the step-by-step deduction that early AI research could model. They solve most of their problems using fast, intuitive judgments. Accurate and efficient reasoning

9632-406: The load on adding items into working memory. Chunking allows more items to be encoded into working memory with more available to transfer into long-term memory. Chekaf, Cowan, and Mathy (2016) looked at how immediate memory relates to the formation of chunks. In the immediate memory, they came up with a two-factor theory of the formation of chunks. These factors are compressibility and the order of

9744-550: The long-term memory recovering the remainder of the chunk. A modality effect is present in chunking. That is, the mechanism used to convey the list of items to the individual affects how much "chunking" occurs. Experimentally, it has been found that auditory presentation results in a larger amount of grouping in the responses of individuals than visual presentation does. Previous literature, such as George Miller's The Magical Number Seven, Plus or Minus Two: Some Limits on our Capacity for Processing Information (1956) has shown that

9856-585: The main limiting factor of visual short-term memory being restricted. The algorithm takes into account the typical time spent when simulating a specific action, such as mentally calculating each position, and “increments the internal clock of the algorithm by the amount of time used”. As such, the parameters set out, such as the time constraint, result in time-restricted problems to be simulated to an extent, limited by “available and simulated resources”. Additionally, extensive research conducted by Woollett and Maguire revealed that through acquiring expertise, such as in

9968-445: The memory process of chunking: chunk, memory code, decode and recode. The chunk, as mentioned prior, is a sequence of to-be-remembered information that can be composed of adjacent terms. These items or information sets are to be stored in the same memory code. The process of recoding is where one learns the code for a chunk, and decoding is when the code is translated into the information that it represents. The phenomenon of chunking as

10080-457: The most difficult problems in knowledge representation are the breadth of commonsense knowledge (the set of atomic facts that the average person knows is enormous); and the sub-symbolic form of most commonsense knowledge (much of what people know is not represented as "facts" or "statements" that they could express verbally). There is also the difficulty of knowledge acquisition , the problem of obtaining knowledge for AI applications. An "agent"

10192-405: The other hand. Classifiers are functions that use pattern matching to determine the closest match. They can be fine-tuned based on chosen examples using supervised learning . Each pattern (also called an " observation ") is labeled with a certain predefined class. All the observations combined with their class labels are known as a data set . When a new observation is received, that observation

10304-471: The priors that can be used to interpret a degraded representation in STM (short-term memory)". In Bayesian inference, priors refer to the initial beliefs regarding the relative frequency of an event occurring instead of other plausible events occurring. When one who holds the initial beliefs receives more information, one will determine the likelihood of each of the plausible events that could happen and thus predict

10416-501: The probability of recall of information is greater when the chunking strategy is used. As stated above, the grouping of the responses occurs as individuals place them into categories according to their inter-relatedness based on semantic and perceptual properties. Lindley (1966) showed that since the groups produced have meaning to the participant, this strategy makes it easier for an individual to recall and maintain information in memory during studies and testing. Therefore, when "chunking"

10528-411: The probability that a particular action will change the state in a particular way and a reward function that supplies the utility of each state and the cost of each action. A policy associates a decision with each possible state. The policy could be calculated (e.g., by iteration ), be heuristic , or it can be learned. Game theory describes the rational behavior of multiple interacting agents and

10640-547: The probability that chunking occurs. These two factors interact with one another and matter in the concept of chunking. Chekaf, Cowan, and Mathy (2016) gave an example where the material "1,2,3,4” can be compressed to "numbers one through four." However, if the material was presented as "1,3,2,4” you cannot compress it because the order in which it is presented is different. Therefore, compressibility and order play an important role in chunking. Artificial intelligence Artificial intelligence ( AI ), in its broadest sense,

10752-468: The process of how an expert is formed, it is possible to find general mechanisms for learning that can be implemented into classrooms. Chunking is a method of learning that can be applied in a number of contexts and is not limited to learning verbal material. Karl Lashley , in his classic paper on serial order , argued that the sequential responses that appear to be organized in a linear and flat fashion concealed an underlying hierarchical structure. This

10864-533: The research conducted by Dirlam (1972), a mathematical analysis was conducted to see what the efficient chunk size is. We are familiar with the size range that chunking holds, but Dirlam (1972) wanted to discover the most efficient chunk size. The mathematical findings have discovered that four or three items in each chunk is the most optimal. The word chunking comes from a famous 1956 paper by George A. Miller , " The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information ". At

10976-486: The same level, the skill level of older players equalled that of younger players in strategy-based tasks that required the player to select the best play within a time constraint, where older players outpaced younger players. The legitimate interpretation of Charness’ experiment is refuted by Retschitzki et al., who identify key issues in Charness’ methodology that leads to an inaccurate conclusion. Retschitzki et al. suggest

11088-430: The span is about nine and, although it drops to about five with monosyllabic English words, the difference is far less than the hypothesis of constant information would require (see also, memory span ). The span of immediate memory seems to be almost independent of the number of bits per chunk, at least over the range that has been examined to date." Miller acknowledged that "we are not very definite about what constitutes

11200-556: The specific event that will occur. Chunks in long-term memory are involved in forming the priors, and they assist with determining the likelihood and prediction of the recall of information in short-term memory. For example, if an acronym and its full meaning already exist in long-term memory, the recall of information regarding that acronym will be easier in short-term memory. Chase and Simon in 1973 and later Gobet, Retschitzki, and de Voogt in 2004 showed that chunking could explain several phenomena linked to expertise in chess. Following

11312-809: The task as a large whole. The study suggests that chunking a larger task into a smaller more manageable task can produce a better outcome. The research also found that completing the task in a coherent order rather than swapping from one task to another can also produce a better outcome. Chunking is used in adults in different ways which can include low-level perceptual features, category membership, semantic relatedness, and statistical co-occurrences between items. Although due to recent studies we are starting to realize that infants also use chunking. They also use different types of knowledges to help them with chunking like conceptual knowledge, spatiotemporal cue knowledge, and knowledge of their social domain. There have been studies that use different chunking models like PARSER and

11424-471: The technology . The general problem of simulating (or creating) intelligence has been broken into subproblems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention and cover the scope of AI research. Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions . By

11536-422: The template. Simulations are carried out by allowing the model to acquire knowledge by receiving stimuli representative of the domain under study. For example, during the learning phase of the chess simulations, the program incrementally acquires chunks and templates by scanning a large database of positions taken from master-level games. This makes it possible to create networks of various sizes, and so to simulate

11648-451: The training data with the expected answers, and comes in two main varieties: classification (where the program must learn to predict what category the input belongs in) and regression (where the program must deduce a numeric function based on numeric input). In reinforcement learning , the agent is rewarded for good responses and punished for bad ones. The agent learns to choose responses that are classified as "good". Transfer learning

11760-420: The use of particular tools. The traditional goals of AI research include reasoning , knowledge representation , planning , learning , natural language processing , perception, and support for robotics . General intelligence —the ability to complete any task performable by a human on an at least equal level—is among the field's long-term goals. To reach these goals, AI researchers have adapted and integrated

11872-435: The works of Dennis Holding, there remains a scarcity of models that further detail memory use in chess, with the exemption of MAPP developed by Chase and Simon, later implemented by Simon and Gilmartin. Chunking (psychology) In cognitive psychology , chunking is a process by which small individual pieces of a set of information are bound together to create a meaningful whole later on in memory. The chunks, by which

11984-436: Was associated with a reliable prediction of the chunking model regarding learning, but the absence of the cue was associated with increased sensitivity to the strength of transitional probabilities. Their findings suggest that the chunking model can only explain certain aspects of learning, specifically language acquisition.   Norris conducted a study in 2020 of chunking and short-term memory recollection, finding that when

12096-578: Was done to see how chunking can improve working memory when it came to symbolic sequences and gating mechanisms. This was done by having 25 participants learn 16 sequences through trial and error. The target was presented alongside a distractor and participants were to identify the target by using right or left buttons on a computer mouse. The final analysis was done on only 19 participants. The results showed that chunking does improve symbolic sequence performance through decreasing cognitive load and real-time strategy. Chunking has proved to be effective in reducing

12208-599: Was done to see how chunking could be beneficial to patients who had Alzheimer's disease. This study was based on how chunking was used to improve working memory in normal young people. Working memory is impaired in the early stages of Alzheimer's disease which affects the ability to do everyday tasks. It also affects executive control of working memory. It was found that participants who had mild Alzheimer's disease were able to use working memory strategies to enhance verbal and spatial working memory performance. It has been long thought that chunking can improve working memory. A study

12320-421: Was due to an increase in his short-term memory capacity. However, they rejected this hypothesis when they found that his short-memory capacity was always the same, considering that he "chunked" only three to four digits at once. Furthermore, he never rehearsed more than six digits at once nor rehearsed more than four groups in a supergroup. Lastly, if his short-term memory capacity increased, then he would have shown

12432-406: Was that they may parse the words using small chunks that correspond to the made-up language. Subsequent studies have supported that when learning involves statistical probabilities (e.g., transitional probabilities in language), it may be better explained via chunking models. Franco and Destrebecqz (2012) further studied chunking in language acquisition and found that the presentation of a temporal cue

12544-402: Was then demonstrated in motor control by Rosenbaum et al. in 1983. Thus sequences can consist of sub-sequences and these can, in turn, consist of sub-sub-sequences. Hierarchical representations of sequences have an advantage over linear representations: They combine efficient local action at low hierarchical levels while maintaining the guidance of an overall structure. While the representation of

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