AI Coding Laptop

AI Coding Laptop — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Expectation propagation

    Expectation propagation

    Expectation propagation (EP) is a technique in Bayesian machine learning. EP finds approximations to a probability distribution. It uses an iterative approach that uses the factorization structure of the target distribution. It differs from other Bayesian approximation approaches such as variational Bayesian methods. More specifically, suppose we wish to approximate an intractable probability distribution p ( x ) {\displaystyle p(\mathbf {x} )} with a tractable distribution q ( x ) {\displaystyle q(\mathbf {x} )} . Expectation propagation achieves this approximation by minimizing the Kullback–Leibler divergence K L ( p | | q ) {\displaystyle \mathrm {KL} (p||q)} . Variational Bayesian methods minimize K L ( q | | p ) {\displaystyle \mathrm {KL} (q||p)} instead. If q ( x ) {\displaystyle q(\mathbf {x} )} is a Gaussian N ( x | μ , Σ ) {\displaystyle {\mathcal {N}}(\mathbf {x} |\mu ,\Sigma )} , then K L ( p | | q ) {\displaystyle \mathrm {KL} (p||q)} is minimized with μ {\displaystyle \mu } and Σ {\displaystyle \Sigma } being equal to the mean of p ( x ) {\displaystyle p(\mathbf {x} )} and the covariance of p ( x ) {\displaystyle p(\mathbf {x} )} , respectively; this is called moment matching. == Applications == Expectation propagation via moment matching plays a vital role in approximation for indicator functions that appear when deriving the message passing equations for TrueSkill.

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  • Model

    Model

    A model is an informative representation of an object, person, or system. The term originally denoted the plans of a building in 16th-century English, and derived via French and Italian ultimately from Latin modulus, 'a measure'. Models can be divided into physical models (e.g. a ship model) and abstract models (e.g. a set of mathematical equations describing the workings of the atmosphere for the purpose of weather forecasting). Abstract or conceptual models are central to philosophy of science. In scholarly research and applied science, a model should not be confused with a theory: while a model seeks only to represent reality with the purpose of better understanding or predicting the world, a theory is more ambitious in that it claims to be an explanation of reality. == Types of model == === Model in specific contexts === As a noun, model has specific meanings in certain fields, derived from its original meaning of "structural design or layout": Model (art), a person posing for an artist, e.g. a 15th-century criminal representing the biblical Judas in Leonardo da Vinci's painting The Last Supper Model (person), a person who serves as a template for others to copy, as in a role model, often in the context of advertising commercial products; e.g. the first fashion model, Marie Vernet Worth in 1853, wife of designer Charles Frederick Worth. Model (product), a particular design of a product as displayed in a catalogue or show room (e.g. Ford Model T, an early car model) Model (organism) a non-human species that is studied to understand biological phenomena in other organisms, e.g. a guinea pig starved of vitamin C to study scurvy, an experiment that would be immoral to conduct on a person Model (mimicry), a species that is mimicked by another species Model (logic), a structure (a set of items, such as natural numbers 1, 2, 3,..., along with mathematical operations such as addition and multiplication, and relations, such as < {\displaystyle <} ) that satisfies a given system of axioms (basic truisms), i.e. that satisfies the statements of a given theory Model (CGI), a mathematical representation of any surface of an object in three dimensions via specialized software Model (MVC), the information-representing internal component of a software, as distinct from its user interface === Physical model === A physical model (most commonly referred to simply as a model but in this context distinguished from a conceptual model) is a smaller or larger physical representation of an object, person or system. The object being modelled may be small (e.g., an atom) or large (e.g., the Solar System) or life-size (e.g., a fashion model displaying clothes for similarly-built potential customers). The geometry of the model and the object it represents are often similar in the sense that one is a rescaling of the other. However, in many cases the similarity is only approximate or even intentionally distorted. Sometimes the distortion is systematic, e.g., a fixed scale horizontally and a larger fixed scale vertically when modelling topography to enhance a region's mountains. An architectural model permits visualization of internal relationships within the structure or external relationships of the structure to the environment. Another use is as a toy. Instrumented physical models are an effective way of investigating fluid flows for engineering design. Physical models are often coupled with computational fluid dynamics models to optimize the design of equipment and processes. This includes external flow such as around buildings, vehicles, people, or hydraulic structures. Wind tunnel and water tunnel testing is often used for these design efforts. Instrumented physical models can also examine internal flows, for the design of ductwork systems, pollution control equipment, food processing machines, and mixing vessels. Transparent flow models are used in this case to observe the detailed flow phenomenon. These models are scaled in terms of both geometry and important forces, for example, using Froude number or Reynolds number scaling (see Similitude). In the pre-computer era, the UK economy was modelled with the hydraulic model MONIAC, to predict for example the effect of tax rises on employment. === Conceptual model === A conceptual model is a theoretical representation of a system, e.g. a set of mathematical equations attempting to describe the workings of the atmosphere for the purpose of weather forecasting. It consists of concepts used to help understand or simulate a subject the model represents. Abstract or conceptual models are central to philosophy of science, as almost every scientific theory effectively embeds some kind of model of the physical or human sphere. In some sense, a physical model "is always the reification of some conceptual model; the conceptual model is conceived ahead as the blueprint of the physical one", which is then constructed as conceived. Thus, the term refers to models that are formed after a conceptualization or generalization process. === Examples === Conceptual model (computer science), an agreed representation of entities and their relationships, to assist in developing software Economic model, a theoretical construct representing economic processes Language model, a probabilistic model of a natural language, used for speech recognition, language generation, and information retrieval Large language models are artificial neural networks used for generative artificial intelligence (AI), e.g. ChatGPT Mathematical model, a description of a system using mathematical concepts and language Statistical model, a mathematical model that usually specifies the relationship between one or more random variables and other non-random variables Model (CGI), a mathematical representation of any surface of an object in three dimensions via specialized software Medical model, a proposed "set of procedures in which all doctors are trained" Mental model, in psychology, an internal representation of external reality Model (logic), a set along with a collection of finitary operations, and relations that are defined on it, satisfying a given collection of axioms Model (MVC), information-representing component of a software, distinct from the user interface (the "view"), both linked by the "controller" component, in the context of the model–view–controller software design Model act, a law drafted centrally to be disseminated and proposed for enactment in multiple independent legislatures Standard model (disambiguation) == Properties of models, according to general model theory == According to Herbert Stachowiak, a model is characterized by at least three properties: 1. Mapping A model always is a model of something—it is an image or representation of some natural or artificial, existing or imagined original, where this original itself could be a model. 2. Reduction In general, a model will not include all attributes that describe the original but only those that appear relevant to the model's creator or user. 3. Pragmatism A model does not relate unambiguously to its original. It is intended to work as a replacement for the original a) for certain subjects (for whom?) b) within a certain time range (when?) c) restricted to certain conceptual or physical actions (what for?). For example, a street map is a model of the actual streets in a city (mapping), showing the course of the streets while leaving out, say, traffic signs and road markings (reduction), made for pedestrians and vehicle drivers for the purpose of finding one's way in the city (pragmatism). Additional properties have been proposed, like extension and distortion as well as validity. The American philosopher Michael Weisberg differentiates between concrete and mathematical models and proposes computer simulations (computational models) as their own class of models. == Uses of models == According to Bruce Edmonds, there are at least 5 general uses for models: Prediction: reliably anticipating unknown data, including data within the domain of the training data (interpolation), and outside the domain (extrapolation) Explanation: establishing plausible chains of causality by proposing mechanisms that can explain patterns seen in data Theoretical exposition: discovering or proposing new hypotheses, or refuting existing hypotheses about the behaviour of the system being modelled Description: representing important aspects of the system being modelled Illustration: communicating an idea or explanation

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  • Instantaneously trained neural networks

    Instantaneously trained neural networks

    Instantaneously trained neural networks are feedforward artificial neural networks that create a new hidden neuron node for each novel training sample. The weights to this hidden neuron separate out not only this training sample but others that are near it, thus providing generalization. This separation is done using the nearest hyperplane that can be written down instantaneously. In the two most important implementations the neighborhood of generalization either varies with the training sample (CC1 network) or remains constant (CC4 network). These networks use unary coding for an effective representation of the data sets. This type of network was first proposed in a 1993 paper of Subhash Kak. Since then, instantaneously trained neural networks have been proposed as models of short term learning and used in web search, and financial time series prediction applications. They have also been used in instant classification of documents and for deep learning and data mining. As in other neural networks, their normal use is as software, but they have also been implemented in hardware using FPGAs and by optical implementation. == CC4 network == In the CC4 network, which is a three-stage network, the number of input nodes is one more than the size of the training vector, with the extra node serving as the biasing node whose input is always 1. For binary input vectors, the weights from the input nodes to the hidden neuron (say of index j) corresponding to the trained vector is given by the following formula: w i j = { − 1 , for x i = 0 + 1 , for x i = 1 r − s + 1 , for i = n + 1 {\displaystyle w_{ij}={\begin{cases}-1,&{\mbox{for }}x_{i}=0\\+1,&{\mbox{for }}x_{i}=1\\r-s+1,&{\mbox{for }}i=n+1\end{cases}}} where r {\displaystyle r} is the radius of generalization and s {\displaystyle s} is the Hamming weight (the number of 1s) of the binary sequence. From the hidden layer to the output layer the weights are 1 or -1 depending on whether the vector belongs to a given output class or not. The neurons in the hidden and output layers output 1 if the weighted sum to the input is 0 or positive and 0, if the weighted sum to the input is negative: y = { 1 if ∑ x i ≥ 0 0 if ∑ x i < 0 {\displaystyle y=\left\{{\begin{matrix}1&{\mbox{if }}\sum x_{i}\geq 0\\0&{\mbox{if }}\sum x_{i}<0\end{matrix}}\right.} == Other networks == The CC4 network has also been modified to include non-binary input with varying radii of generalization so that it effectively provides a CC1 implementation. In feedback networks the Willshaw network as well as the Hopfield network are able to learn instantaneously.

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  • Anthropic–United States Department of Defense dispute

    Anthropic–United States Department of Defense dispute

    Since January 2026, the United States Department of Defense has conflicted with the artificial intelligence company Anthropic over the use of its products for military purposes and mass domestic surveillance. == Background == === Artificial intelligence in the U.S. military === The United States Department of Defense began developing lethal autonomous weapons as early as the Reagan administration. The Department of Defense established a policy on the use of artificial intelligence in 2012, Directive 3000.09. Efforts to utilize artificial intelligence intensified under the term of secretary Ash Carter. The Department of Defense's use of artificial intelligence for Project Maven prompted concerns within Google in 2018, leading to protests and mass resignations. === Anthropic in the second Trump administration === In Donald Trump's second presidency, Anthropic publicly disagreed with the administration's policies and initiatives. In January 2025, Anthropic chief executive Dario Amodei criticized the artificial intelligence investment project Stargate as "chaotic" and opposed Trump's rescission of president Joe Biden's Executive Order on Artificial Intelligence, but noted that Anthropic had held discussions with Trump officials about artificial intelligence policy. Amid discussions over the One Big Beautiful Bill Act, Anthropic privately lobbied for Congress to vote against a bill preventing states from regulating artificial intelligence and expressed opposition to an artificial intelligence agreement signed among Gulf states in Trump's visit to the Middle East in May. According to Semafor, Trump officials chastised Anthropic's hiring of several officials involved in the Biden administration, including Elizabeth Kelly, the former director of the Artificial Intelligence Safety Institute; Tarun Chhabra, the coordinator for technology and national security in the National Security Council; and Ben Buchanan, Biden's advisor for artificial intelligence. The following month, Amodei wrote an op-ed in The New York Times describing the artificial intelligence regulation bill, then tied to the One Big Beautiful Bill Act, as "far too blunt an instrument". Prior to the dispute, the Trump administration had integrated Anthropic's services. By November 2024, Anthropic had already partnered with Palantir and Amazon Web Services, companies that offered services with FedRAMP authorization. In the Biden administration, Anthropic had reached an agreement with the AI Safety Institute and had participated in a nuclear information safety evaluation. The Department of Homeland Security authorized its workers to use commercial artificial intelligence systems, including Anthropic's Claude, until May 2025. Through its interoperability with Palantir, a company heavily involved in data analysis and analytics at the Department of Defense, Anthropic's technology achieved relatively widespread usage in the U.S. military. The following month, Anthropic announced that it would allow national security customers to use Claude Gov. Anthropic's orthogonal usage policy to the surveillance systems implemented at the Federal Bureau of Investigation, the Secret Service, and Immigration and Customs Enforcement led to a conflict between Anthropic and the Trump administration by September. That month, Amodei criticized Trump's approach to export restrictions on semiconductors. Anthropic's strategy has mirrored Amodei's views towards Trump; in a Facebook post ahead of the 2024 presidential election, Amodei urged his associates to vote for vice president Kamala Harris over Trump, describing him as a "feudal warlord". As the Trump administration targeted law firms, Amodei cut ties with the firms Skadden, Arps, Slate, Meagher & Flom and Latham & Watkins, which reached agreements with the Trump administration to avoid punishment. David Sacks, Trump's advisor for artificial intelligence and cryptocurrency, said on All-In (2020–present) that Anthropic was among several "AI doomers" that support regulation he saw as overly restrictive. According to The Wall Street Journal, officials close to Sacks examined whether Anthropic's Claude was a "woke AI"; in July, Trump signed an executive order "Preventing Woke AI in the Federal Government ". Sacks viewed Amodei's decision to attend the World Economic Forum over Trump's second inauguration; his hiring of Biden officials; and Anthropic's association with the philanthropic initiative Open Philanthropy as evidence that Anthropic would not support Trump's agenda. In October 2025, Sacks stated that Anthropic was "running a sophisticated regulatory capture strategy based on fear-mongering." That month, Amodei published a blog post rebuffing "inaccurate claims" from the Trump administration on Anthropic's policies, intensifying the dispute. Amodei's statement included views explicitly espoused by vice president JD Vance. In December, Amodei met with Trump officials and several senators in an effort to improve Anthropic's relationship with the Trump administration. == Dispute == In December 2025, secretary of defense Pete Hegseth announced GenAI.mil, an artificial intelligence platform for the Department of Defense. The department initially contracted Google Gemini for the platform, then OpenAI's ChatGPT. The following month, Hegseth announced that the Department of Defense would additionally contract xAI's Grok for use in the military, decrying "woke AI." In January 2026, Semafor reported that the Department of Defense had conflicted with Anthropic over its policies on lethal military force and that Hegseth's comment on woke AI was a reference to Anthropic. According to Reuters, Anthropic representatives opposed the use of the company's products for surveillance or to develop lethal autonomous weapons. The dispute between Anthropic and the Department of Defense resulted in the termination of a contract worth an estimated US$200 million. In February 2026, Emil Michael, the under secretary of defense for research and engineering, stated that the Department of Defense would expand access to commercial artificial intelligence systems, including Anthropic's Claude, to unclassified and classified domains. That month, Axios reported that the Department of Defense had used Claude in the United States intervention in Venezuela. Anthropic told Axios that it would reassess its partnership with the Department of Defense after the revelations. After Anthropic refused to agree to allow the Department of Defense to use Claude for "all lawful purposes," the department threatened to cancel its contracts with the company. Hegseth additionally moved to label Anthropic a "supply chain risk," which would have forced military contractors to cut ties with Anthropic. A federal judge blocked this designation, describing it as punitive. Michael told reporters that Anthropic should "cross the Rubicon" and allow the Department of Defense to dictate the terms of how its technology is used. The position of the Department of Defense, and its tactics during the dispute, were widely criticized on grounds including violating the principles of rule-of-law, market independence and national security. == Impact == The dispute caused 1789 Capital, a venture capital firm associated with Donald Trump Jr., to abandon an investment in Anthropic worth hundreds of millions of dollars. Following the government's actions against Anthropic, OpenAI "rushed", hours before the US started the 2026 Iran war, to get a deal without the constraints that Anthropic had sought. == Lawsuits == In March 2026, Judge Rita F. Lin granted a preliminary injunction against the government. Lin wrote: The Department of War’s records show that it designated Anthropic as a supply chain risk because of its “hostile manner through the press.” Punishing Anthropic for bringing public scrutiny to the government’s contracting position is classic illegal First Amendment retaliation. (...) At bottom, Anthropic has shown that these broad punitive measures were likely unlawful and that it is suffering irreparable harm from them. Numerous amici have also described wide-ranging harm to the public interest, including the chilling of open discussion about important topics in AI safety. In April 2026, the Court of Appeals for the D.C. Circuit in a per curiam order denied Anthropic's motion to lift the designation. The April order is not final. The court's order said lifting the designation "would force the United States military to prolong its dealings with an unwanted vendor of critical AI services in the middle of a significant ongoing military conflict". According to Wired, "Several experts in government contracting and corporate rights" said "Anthropic has a strong case against the government, but the courts sometimes refuse to overrule the White House on matters related to national security."

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  • Client-side persistent data

    Client-side persistent data

    Client-side persistent data or CSPD is a term used in computing for storing data required by web applications to complete internet tasks on the client-side as needed rather than exclusively on the server. As a framework it is one solution to the needs of Occasionally connected computing or OCC. A major challenge for HTTP as a stateless protocol has been asynchronous tasks. The AJAX pattern using XMLHttpRequest was first introduced by Microsoft in the context of the Outlook e-mail product. The first CSPD were the 'cookies' introduced by the Netscape Navigator. ActiveX components which have entries in the Windows registry can also be viewed as a form of client-side persistence.

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  • Parents & Kids Safe AI Coalition

    Parents & Kids Safe AI Coalition

    The Parents & Kids Safe AI Coalition is a political action committee that advocates for regulation of artificial intelligence on child safety. As of April 2026, the group is funded solely by the artificial intelligence company OpenAI, which pledged $10 million to the effort. == History == In October 2025, California Gov. Gavin Newsom vetoed Assembly Bill 1064. Sponsored by Common Sense Media, the bill would have introduced stronger child safety protections for AI chatbots. The following month, Common Sense Media founder Jim Steyer filed a ballot initiative intended to restore the "guardrails" lost in the veto. In response, OpenAI introduced a competing initiative. In January 2026, Common Sense Media and OpenAI announced that they would be working together on a compromise ballot initiative, the Parents & Kids Safe AI Act. Reporting indicated that initial outreach emails to child safety organizations failed to disclose OpenAI's involvement. Several advocacy groups signed an open letter claiming the initiative would shield AI companies from liability and undermine age verification, among other concerns. After Common Sense Media met with opposing groups in February, the ballot initiative was put on hold and the organizations involved sought to negotiate with the Legislature instead. The Parents & Kids Safe AI Coalition was founded to support this effort. In March 2026, the group reached out to some of the same groups contacted earlier, asking them to endorse its list of policy priorities. Again, some organizations reported being unaware of OpenAI's level of involvement. At least two groups withdrew from the coalition after learning about the financial ties. The priorities themselves were described as "vague but fairly uncontroversial" by The San Francisco Standard.

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  • JaCoP (solver)

    JaCoP (solver)

    JaCoP is a constraint solver for constraint satisfaction problems. It is written in Java and it is provided as a Java library. JaCoP has an interface to the MiniZinc and AMPL modeling languages. Its main focus is on ease of use, modeling power, as well as efficiency. It has a large collection of global constraints implemented to facilitate problem modeling. JaCoP is actively developed since year 2001. Krzysztof Kuchcinski and Radoslaw Szymanek are the core developers of this Java library. There are number of people who have contributed to JaCoP development in addition to core developers. JaCoP development has been influenced by more than 20 research articles from Constraint Programming community. It has been used as a tool in more than 30 research articles. There are many different examples provided so it is easier to learn how to use JaCoP. The JaCoP project contains a wrapper for the Scala programming language, and a wrapper for Clojure is maintained as a separate project CloCoP.

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  • Type–token distinction

    Type–token distinction

    The type–token distinction is the difference between a type of objects (analogous to a class) and the individual tokens of that type (analogous to instances). Since each type may be instantiated by multiple tokens, there are generally more tokens than types of an object. For example, the sentence "A rose is a rose is a rose" contains three word types: three word tokens of the type a, two word tokens of the type is, and three word tokens of the type rose. The distinction is important in disciplines such as logic, linguistics, metalogic, typography, and computer programming. == Overview == The type–token distinction separates types (abstract descriptive concepts) from tokens (objects that instantiate concepts). For example, in the sentence "the bicycle is becoming more popular" the word bicycle represents the abstract concept of bicycles and this abstract concept is a type, whereas in the sentence "the bicycle is in the garage", it represents a particular object and this particular object is a token. Similarly, the word type 'letter' uses only four letter types: L, E, T and R. Nevertheless, it uses both E and T twice. One can say that the word type 'letter' has six letter tokens, with two tokens each of the letter types E and T. Whenever a word type is inscribed, the number of letter tokens created equals the number of letter occurrences in the word type. Some logicians consider a word type to be the class of its tokens. Other logicians counter that the word type has a permanence and constancy not found in the class of its tokens. The type remains the same while the class of its tokens is continually gaining new members and losing old members. == Typography == In typography, the type–token distinction is used to determine the presence of a text printed by movable type: The defining criteria which a typographic print has to fulfill is that of the type identity of the various letter forms which make up the printed text. In other words: each letter form which appears in the text has to be shown as a particular instance ("token") of one and the same type which contains a reverse image of the printed letter. == Charles Sanders Peirce == The distinctions between using words as types or tokens were first made by American logician and philosopher Charles Sanders Peirce in 1906 using terminology that he established. Peirce's type–token distinction applies to words, sentences, paragraphs and so on: to anything in a universe of discourse of character-string theory, or concatenation theory. Peirce's original words are the following: A common mode of estimating the amount of matter in a ... printed book is to count the number of words. There will ordinarily be about twenty 'thes' on a page, and, of course, they count as twenty words. In another sense of the word 'word,' however, there is but one word 'the' in the English language; and it is impossible that this word should lie visibly on a page, or be heard in any voice .... Such a ... Form, I propose to term a Type. A Single ... Object ... such as this or that word on a single line of a single page of a single copy of a book, I will venture to call a Token. .... In order that a Type may be used, it has to be embodied in a Token which shall be a sign of the Type, and thereby of the object the Type signifies.

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  • Comparison gallery of image scaling algorithms

    Comparison gallery of image scaling algorithms

    This gallery shows the results of numerous image scaling algorithms. == Scaling methods == An image size can be changed in several ways. Consider resizing a 160x160 pixel photo to the following 40x40 pixel thumbnail and then scaling the thumbnail to a 160x160 pixel image. Also consider doubling the size of the following image containing text. == Examples of enlarged images == Below are examples of various images enlarged 4x using each scaling algorithm.

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  • Reason maintenance

    Reason maintenance

    Reason maintenance is a knowledge representation approach to efficient handling of inferred information that is explicitly stored. Reason maintenance distinguishes between base facts, which can be defeated, and derived facts. As such it differs from belief revision which, in its basic form, assumes that all facts are equally important. Reason maintenance was originally developed as a technique for implementing problem solvers. It encompasses a variety of techniques that share a common architecture: two components—a reasoner and a reason maintenance system—communicate with each other via an interface. The reasoner uses the reason maintenance system to record its inferences and justifications of ("reasons" for) the inferences. The reasoner also informs the reason maintenance system which are the currently valid base facts (assumptions). The reason maintenance system uses the information to compute the truth value of the stored derived facts and to restore consistency if an inconsistency is derived. == Truth maintenance system == A truth maintenance system, or TMS, is a knowledge representation method for representing both beliefs and their dependencies and an algorithm called the "truth maintenance algorithm" that manipulates and maintains the dependencies. The name truth maintenance is due to the ability of these systems to restore consistency. A truth maintenance system maintains consistency between old believed knowledge and current believed knowledge in the knowledge base (KB) through revision. If the current believed statements contradict the knowledge in the KB, then the KB is updated with the new knowledge. It may happen that the same data will again be believed, and the previous knowledge will be required in the KB. If the previous data are not present, but may be required for new inference. But if the previous knowledge was in the KB, then no retracing of the same knowledge is needed. The use of TMS avoids such retracing; it keeps track of the contradictory data with the help of a dependency record. This record reflects the retractions and additions which makes the inference engine (IE) aware of its current belief set. == Algorithm == Each statement having at least one valid justification is made a part of the current belief set. When a contradiction is found, the statement(s) responsible for the contradiction are identified and the records are appropriately updated. This process is called dependency-directed backtracking. The TMS algorithm maintains the records in the form of a dependency network. Each node in the network is an entry in the KB (a premise, antecedent, or inference rule etc.) Each arc of the network represent the inference steps through which the node was derived. A premise is a fundamental belief which is assumed to be true. They do not need justifications. The set of premises are the basis from which justifications for all other nodes will be derived. == Justification == There are two types of justification for a node. They are: Support list [SL] Conditional proof (CP) == Examples == Many kinds of truth maintenance systems exist. Two major types are single-context and multi-context truth maintenance. In single context systems, consistency is maintained among all facts in memory (KB) and relates to the notion of consistency found in classical logic. Multi-context systems support paraconsistency by allowing consistency to be relevant to a subset of facts in memory, a context, according to the history of logical inference. This is achieved by tagging each fact or deduction with its logical history. Multi-agent truth maintenance systems perform truth maintenance across multiple memories, often located on different machines. de Kleer's assumption-based truth maintenance system (ATMS, 1986) was utilized in systems based upon KEE on the Lisp Machine. The first multi-agent TMS was created by Mason and Johnson. It was a multi-context system. Bridgeland and Huhns created the first single-context multi-agent system.

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  • Partial-order planning

    Partial-order planning

    Partial-order planning is an approach to automated planning that maintains a partial ordering between actions and only commits ordering between actions when forced to, that is, ordering of actions is partial. Also this planning doesn't specify which action will come out first when two actions are processed. By contrast, total-order planning maintains a total ordering between all actions at every stage of planning. Given a problem in which some sequence of actions is needed to achieve a goal, a partial-order plan specifies all actions that must be taken, but specifies an ordering between actions only where needed. Consider the following situation: a person must travel from the start to the end of an obstacle course. The course is composed of a bridge, a see-saw, and a swing-set. The bridge must be traversed before the see-saw and swing-set are reachable. Once reachable, the see-saw and swing-set can be traversed in any order, after which the end is reachable. In a partial-order plan, ordering between these obstacles is specified only when needed. The bridge must be traversed first. Second, either the see-saw or swing-set can be traversed. Third, the remaining obstacle can be traversed. Then the end can be traversed. Partial-order planning relies upon the principle of least commitment for its efficiency. == Partial-order plan == A partial-order plan or partial plan is a plan which specifies all actions that must be taken, but only specifies the order between actions when needed. It is the result of a partial-order planner. A partial-order plan consists of four components: A set of actions (also known as operators). A partial order for the actions. It specifies the conditions about the order of some actions. A set of causal links. It specifies which actions meet which preconditions of other actions. Alternatively, a set of bindings between the variables in actions. A set of open preconditions. It specifies which preconditions are not fulfilled by any action in the partial-order plan. To keep the possible orders of the actions as open as possible, the set of order conditions and causal links must be as small as possible. A plan is a solution if the set of open preconditions is empty. A linearization of a partial order plan is a total order plan derived from the particular partial order plan; in other words, both order plans consist of the same actions, with the order in the linearization being a linear extension of the partial order in the original partial order plan. === Example === For example, a plan for baking a cake might start: go to the store get eggs; get flour; get milk pay for all goods go to the kitchen This is a partial plan because the order for finding eggs, flour and milk is not specified, the agent can wander around the store reactively accumulating all the items on its shopping list until the list is complete. == Partial-order planner == A partial-order planner is an algorithm or program which will construct a partial-order plan and search for a solution. The input is the problem description, consisting of descriptions of the initial state, the goal and possible actions. The problem can be interpreted as a search problem where the set of possible partial-order plans is the search space. The initial state would be the plan with the open preconditions equal to the goal conditions. The final state would be any plan with no open preconditions, i.e. a solution. The initial state is the starting conditions, and can be thought of as the preconditions to the task at hand. For a task of setting the table, the initial state could be a clear table. The goal is simply the final action that needs to be accomplished, for example setting the table. The operators of the algorithm are the actions by which the task is accomplished. For this example there may be two operators: lay (tablecloth), and place (glasses, plates, and silverware). === Plan space === The plan space of the algorithm is constrained between its start and finish. The algorithm starts, producing the initial state and finishes when all parts of the goal have been achieved. In the setting a table example, two types of actions exist that must be addressed: the put-out and lay operators. Four unsolved operators also exist: Action 1, lay-tablecloth, Action 2, Put-out (plates), Action 3, Put-out (silverware), and Action 4, Put-out (glasses). However, a threat arises if Action 2, 3, or 4 comes before Action 1. This threat is that the precondition to the start of the algorithm will be unsatisfied as the table will no longer be clear. Thus, constraints exist that must be added to the algorithm that force Actions 2, 3, and 4 to come after Action 1. Once these steps are completed, the algorithm will finish and the goal will have been completed. === Threats === As seen in the algorithm presented above, partial-order planning can encounter certain threats, meaning orderings that threaten to break connected actions, thus potentially destroying the entire plan. There are two ways to resolve threats: Promotion Demotion Promotion orders the possible threat after the connection it threatens. Demotion orders the possible threat before the connection it threatens. Partial-order planning algorithms are known for being both sound and complete, with sound being defined as the total ordering of the algorithm, and complete being defined as the capability to find a solution, given that a solution does in fact exist. == Partial-order vs. total-order planning == Partial-order planning is the opposite of total-order planning, in which actions are sequenced all at once and for the entirety of the task at hand. The question arises when one has two competing processes, which one is better? Anthony Barret and Daniel Weld have argued in their 1993 book, that partial-order planning is superior to total-order planning, as it is faster and thus more efficient. They tested this theory using Korf’s taxonomy of subgoal collections, in which they found that partial-order planning performs better because it produces more trivial serializability than total-order planning. Trivial serializability facilitates a planner’s ability to perform quickly when dealing with goals that contain subgoals. Planners perform more slowly when dealing with laboriously serializable or nonserializable subgoals. The determining factor that makes a subgoal trivially or laboriously serializable is the search space of different plans. They found that partial-order planning is more adept at finding the quickest path, and is therefore the more efficient of these two main types of planning. == The Sussman anomaly == Partial-order plans are known to easily and optimally solve the Sussman anomaly. Using this type of incremental planning system solves this problem quickly and efficiently. This was a result of partial-order planning that solidified its place as an efficient planning system. == Disadvantages to partial-order planning == One drawback of this type of planning system is that it requires a lot more computational power for each node. This higher per-node cost occurs because the algorithm for partial-order planning is more complex than others. This has important artificial intelligence implications. When coding a robot to do a certain task, the creator needs to take into account how much energy is needed. Though a partial-order plan may be quicker it may not be worth the energy cost for the robot. The creator must be aware of and weigh these two options to build an efficient robot.

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  • Embodied cognition

    Embodied cognition

    Embodied cognition represents a diverse group of theories which investigate how cognition is shaped by the bodily state and capacities of the organism. These embodied factors include the motor system, the perceptual system, bodily interactions with the environment (situatedness), and the assumptions about the world that shape the functional structure of the brain and body of the organism. Embodied cognition suggests that these elements are essential to a wide spectrum of cognitive functions, such as perception biases, memory recall, comprehension and high-level mental constructs (such as meaning attribution and categories) and performance on various cognitive tasks (reasoning or judgment). The embodied mind thesis challenges other theories, such as cognitivism, computationalism, and Cartesian dualism. It is closely related to the extended mind thesis, situated cognition, and enactivism. The modern version depends on understandings drawn from up-to-date research in psychology, linguistics, cognitive science, dynamical systems, artificial intelligence, robotics, animal cognition, plant cognition, and neurobiology. == Theory == Proponents of the embodied cognition thesis emphasize the active and significant role the body plays in the shaping of cognition and in the understanding of an agent's mind and cognitive capacities. In philosophy, embodied cognition holds that an agent's cognition, rather than being the product of mere (innate) abstract representations of the world, is strongly influenced by aspects of an agent's body beyond the brain itself. An embodied model of cognition opposes the disembodied Cartesian model, according to which all mental phenomena are non-physical and, therefore, not influenced by the body. With this opposition the embodiment thesis intends to reintroduce an agent's bodily experiences into any account of cognition. It is a rather broad thesis and encompasses both weak and strong variants of embodiment. In an attempt to reconcile cognitive science with human experience, the enactive approach to cognition defines "embodiment" as follows: By using the term embodied we mean to highlight two points: first that cognition depends upon the kinds of experience that come from having a body with various sensorimotor capacities, and second, that these individual sensorimotor capacities are themselves embedded in a more encompassing biological, psychological and cultural context. This double sense attributed to the embodiment thesis emphasizes the many aspects of cognition that researchers in different fields—such as philosophy, cognitive science, artificial intelligence, psychology, and neuroscience—are involved with. This general characterization of embodiment faces some difficulties: a consequence of this emphasis on the body, experience, culture, context, and the cognitive mechanisms of an agent in the world is that often distinct views and approaches to embodied cognition overlap. The theses of extended cognition and situated cognition, for example, are usually intertwined and not always carefully separated. And since each of the aspects of the embodiment thesis is endorsed to different degrees, embodied cognition should be better seen "as a research program rather than a well-defined unified theory". Some authors explain the embodiment thesis by arguing that cognition depends on an agent's body and its interactions with a determined environment. From this perspective, cognition in real biological systems is not an end in itself; it is constrained by the system's goals and capacities. Such constraints do not mean cognition is set by adaptive behavior (or autopoiesis) alone, but instead that cognition requires "some kind of information processing... the transformation or communication of incoming information". The acquiring of such information involves the agent's "exploration and modification of the environment". It would be a mistake, however, to suppose that cognition consists simply of building maximally accurate representations of input information...the gaining of knowledge is a stepping stone to achieving the more immediate goal of guiding behavior in response to the system's changing surroundings. Another approach to understanding embodied cognition comes from a narrower characterization of the embodiment thesis. The following narrower view of embodiment avoids any compromises to external sources other than the body and allows differentiating between embodied cognition, extended cognition, and situated cognition. Thus, the embodiment thesis can be specified as follows: Many features of cognition are embodied in that they are deeply dependent upon characteristics of the physical body of an agent, such that the agent's beyond-the-brain body plays a significant causal role, or a physically constitutive role, in that agent's cognitive processing. This thesis points out the core idea that an agent's body plays a significant role in shaping different features of cognition, such as perception, attention, memory, reasoning—among others. Likewise, these features of cognition depend on the kind of body an agent has. The thesis omits direct mention of some aspects of the "more encompassing biological, psychological and cultural context" included in the enactive definition, making it possible to separate embodied cognition, extended cognition, and situated cognition. In contrast to the embodiment thesis, the extended mind thesis limits cognitive processing neither to the brain nor even to the body, it extends it outward into the agent's world. Situated cognition emphasizes that this extension is not just a matter of including resources outside the head but stressing the role of probing and changing interactions with the agent's world. Cognition is situated in that it is inherently dependent upon the cultural and social contexts within which it takes place. This conceptual reframing of cognition as an activity influenced by the body has had significant implications. For instance, the view of cognition inherited by most contemporary cognitive neuroscience is internalist in nature. An agent's behavior along with its capacity to maintain (accurate) representations of the surrounding environment were considered as the product of "powerful brains that can maintain the world models and devise plans". From this perspective, cognizing was conceived as something that an isolated brain did. In contrast, accepting the role the body plays during cognitive processes allows us to account for a more encompassing view of cognition. This shift in perspective within neuroscience suggests that successful behavior in real-world scenarios demands the integration of several sensorimotor and cognitive (as well as affective) capacities of an agent. Thus, cognition emerges in the relationship between an agent and the affordances provided by the environment rather than in the brain alone. In 2002, a collection of positive characterizations summarizing what the embodiment thesis entails for cognition were offered. Professor of Cognitive Psychology Margaret Wilson argues that the general outlook of embodied cognition "displays an interesting co-variation of multiple observations and houses a number of different claims: (1) cognition is situated; (2) cognition is time-pressured; (3) we off-load cognitive work onto the environment; (4) the environment is part of the cognitive system; (5) cognition is for action; (6) offline cognition is bodily-based". According to Wilson, the first three and the fifth claim appear to be at least partially true, while the fourth claim is deeply problematic in that all things that have an impact on the elements of a system are not necessarily considered part of the system. The sixth claim has received the least attention in the literature on embodied cognition, yet it might be the most significant of the six claims as it shows how certain human cognitive capabilities, that previously were thought to be highly abstract, now appear to be leaning towards an embodied approach for their explanation. Wilson also describes at least five main (abstract) categories that combine both sensory and motor skills (or sensorimotor functions). The first three are working memory, episodic memory, and implicit memory; the fourth is mental imagery, and finally, the fifth concerns reasoning and problem solving. == History == The theory of embodied cognition, along with the multiple aspects it comprises, can be regarded as the imminent result of an intellectual skepticism towards the flourishment of the disembodied theory of mind put forth by René Descartes in the 17th century. According to Cartesian dualism, the mind is entirely distinct from the body and can be successfully explained and understood without reference to the body or to its processes. Research has been done to identify the set of ideas that would establish what could be considered as the early stages of embodied cognition around inquiries regarding the mind-body-soul rel

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  • Anthrobotics

    Anthrobotics

    Anthrobotics is the science of developing and studying robots that are either entirely or in some way human-like. The term anthrobotics was originally coined by Mark Rosheim in a paper entitled "Design of An Omnidirectional Arm" presented at the IEEE International Conference on Robotics and Automation, May 13–18, 1990, pp. 2162–2167. Rosheim says he derived the term from "...Anthropomorphic and Robotics to distinguish the new generation of dexterous robots from its simple industrial robot forebears." The word gained wider recognition as a result of its use in the title of Rosheim's subsequent book Robot Evolution: The Development of Anthrobotics, which focussed on facsimiles of human physical and psychological skills and attributes. However, a wider definition of the term anthrobotics has been proposed, in which the meaning is derived from anthropology rather than anthropomorphic. This usage includes robots that respond to input in a human-like fashion, rather than simply mimicking human actions, thus theoretically being able to respond more flexibly or to adapt to unforeseen circumstances. This expanded definition also encompasses robots that are situated in social environments with the ability to respond to those environments appropriately, such as insect robots, robotic pets, and the like. Anthrobotics is now taught at some universities, encouraging students not only to design and build robots for environments beyond current industrial applications, but also to speculate on the future of robotics that are embedded in the world at large, as mobile phones and computers are today. In 2016 philosopher Luis de Miranda created the Anthrobotics Cluster at the University of Edinburgh "a platform of cross-disciplinary research that seeks to investigate some of the biggest questions that will need to be answered" on the relationship between humans, robots and intelligent systems and "a think tank on the social spread of robotics, and also how automation is part of the definition of what humans have always been". to explore the symbiotic relationship between humans and automated protocols.

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  • Business rule management system

    Business rule management system

    A BRMS or business rule management system is a software system used to define, deploy, execute, monitor and maintain the variety and complexity of decision logic that is used by operational systems within an organization or enterprise. This logic, also referred to as business rules, includes policies, requirements, and conditional statements that are used to determine the tactical actions that take place in applications and systems. == Overview == A BRMS includes, at minimum: A repository, allowing decision logic to be externalized from core application code Tools, allowing both technical developers and business experts to define and manage decision logic A runtime environment, allowing applications to invoke decision logic managed within the BRMS and execute it using a business rules engine The top benefits of a BRMS include: Reduced or removed reliance on IT departments for changes in live systems. Although, QA and Rules testing would still be needed in any enterprise system. Increased control over implemented decision logic for compliance and better business management including audit logs, impact simulation and edit controls. The ability to express decision logic with increased precision, using a business vocabulary syntax and graphical rule representations (decision tables, decision models, trees, scorecards and flows) Improved efficiency of processes through increased decision automation. Some disadvantages of the BRMS include: Extensive subject matter expertise can be required for vendor specific products. In addition to appropriate design practices (such as Decision Modeling), technical developers must know how to write rules and integrate software with existing systems Poor rule harvesting approaches can lead to long development cycles, though this can be mitigated with modern approaches like the Decision Model and Notation (DMN) standard. Integration with existing systems is still required and a BRMS may add additional security constraints. Reduced IT department reliance may never be a reality due to continued introduction to new business rule considerations or object model perturbations The coupling of a BRMS vendor application to the business application may be too tight to replace with another BRMS vendor application. This can lead to cost to benefits issues. The emergence of the DMN standard has mitigated this to some degree. Most BRMS vendors have evolved from rule engine vendors to provide business-usable software development lifecycle solutions, based on declarative definitions of business rules executed in their own rule engine. BRMSs are increasingly evolving into broader digital decisioning platforms that also incorporate decision intelligence and machine learning capabilities. However, some vendors come from a different approach (for example, they map decision trees or graphs to executable code). Rules in the repository are generally mapped to decision services that are naturally fully compliant with the latest SOA, Web Services, or other software architecture trends. == Related software approaches == In a BRMS, a representation of business rules maps to a software system for execution. A BRMS therefore relates to model-driven engineering, such as the model-driven architecture (MDA) of the Object Management Group (OMG). It is no coincidence that many of the related standards come under the OMG banner. A BRMS is a critical component for Enterprise Decision Management as it allows for the transparent and agile management of the decision-making logic required in systems developed using this approach. == Associated standards == The OMG Decision Model and Notation standard is designed to standardize elements of business rules development, specially decision table representations. There is also a standard for a Java Runtime API for rule engines JSR-94. OMG Business Motivation Model (BMM): A model of how strategies, processes, rules, etc. fit together for business modeling OMG SBVR: Targets business constraints as opposed to automating business behavior OMG Production Rule Representation (PRR): Represents rules for production rule systems that make up most BRMS' execution targets OMG Decision Model and Notation (DMN): Represents models of decisions, which are typically managed by a BRMS RuleML provides a family of rule mark-up languages that could be used in a BRMS and with W3C RIF it provides a family of related rule languages for rule interchange in the W3C Semantic Web stack Many standards, such as domain-specific languages, define their own representation of rules, requiring translations to generic rule engines or their own custom engines. Other domains, such as PMML, also define rules.

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  • Moral Machine

    Moral Machine

    Moral Machine is an online platform, developed by Iyad Rahwan's Scalable Cooperation group at the Massachusetts Institute of Technology, that generates moral dilemmas and collects information on the decisions that people make between two destructive outcomes. The platform is the idea of Iyad Rahwan and social psychologists Azim Shariff and Jean-François Bonnefon, who conceived of the idea ahead of the publication of their article about the ethics of self-driving cars. The key contributors to building the platform were MIT Media Lab graduate students Edmond Awad and Sohan Dsouza. The presented scenarios are often variations of the trolley problem, and the information collected would be used for further research regarding the decisions that machine intelligence must make in the future. For example, as artificial intelligence plays an increasingly significant role in autonomous driving technology, research projects like Moral Machine help to find solutions for challenging life-and-death decisions that will face self-driving vehicles. Moral Machine was active from January 2016 to July 2020. The Moral Machine continues to be available on their website for people to experience. == The experiment == The Moral Machine was an ambitious project; it was the first attempt at using such an experimental design to test a large number of humans in over 200 countries worldwide. The study was approved by the Institute Review Board (IRB) at Massachusetts Institute of Technology (MIT). The setup of the experiment asks the viewer to make a decision on a single scenario in which a self-driving car is about to hit pedestrians. The user can decide to have the car either swerve to avoid hitting the pedestrians or keep going straight to preserve the lives it is transporting. Participants can complete as many scenarios as they want to, however the scenarios themselves are generated in groups of thirteen. Within this thirteen, a single scenario is entirely random while the other twelve are generated from a space in a database of 26 million different possibilities. They are chosen with two dilemmas focused on each of six dimensions of moral preferences: character gender, character age, character physical fitness, character social status, character species, and character number. The experiment setup remains the same throughout multiple scenarios but each scenario tests a different set of factors. Most notably, the characters involved in the scenario are different in each one. Characters may include ones such as: Stroller, girl, boy, pregnant, Male Doctor, Female Doctor, Female Athlete, Executive Female, Male Athlete, Executive Male, Large Woman, Large Man, homeless, old man, old woman, dog, criminal, and a cat. Through these different characters researchers were able to understand how a wide variety of people will judge scenarios based on those involved. == Analysis == The Moral Machine collected 40 million moral decisions from 4 million participants in 233 countries, analysis of which revealed trends within individual countries and humanity as a whole. It tested for nine factors: preference for sparing humans versus pets, passengers versus pedestrians, men versus women, young versus elderly, fit versus overweight, higher versus lower social status, jaywalkers versus law abiders, larger versus smaller groups, and inaction (i.e. staying on course) versus swerving. Globally, participants favored human lives over lives of animals like dogs and cats. They preferred to spare more lives if possible, and younger lives as opposed to older. Babies were most often spared with cats being the least spared. In terms of gender variations, people tended to spare men over women for doctors and the elderly. All countries generally shared the preference to spare pedestrians over passengers and law-abiders over criminals. Participants from less wealthy countries showed a higher tendency of sparing pedestrians who crossed illegally compared to those from more wealthy and developed countries. This is most likely due to their experience living in a society where individuals are more likely to deviate from rules due to less stringent enforcement of laws. Countries of higher economic inequality overwhelmingly prefer to save wealthier individuals over poorer ones. === Cultural differences === Researchers subdivided 130 countries with similar results into three ‘cultural clusters’. North America and European countries with significant Christian populations had a higher preference for inaction on the part of the driver and thus had less of a preference for sparing pedestrians as compared to other clusters. East Asian and Islamic countries, together constituting the second cluster, did not have as much preference to spare younger humans compared to the other two clusters and had a higher preference for sparing law-abiding humans. Latin America and Francophone countries had a higher preference for sparing women, the young, the fit, and those of higher status, but a lower preference for sparing humans over pets or other animals. Individualistic cultures tended to spare larger groups, and collectivist cultures had a stronger preference for sparing the lives of older people. For instance, China ranked far below the world average for preference to spare the younger over elderly, while the average respondent from the US exhibited a much higher tendency to save younger lives and larger groups. == Applications of the data == The findings from the moral machine can help decision makers when designing self-driving automotive systems. Designers must make sure that these vehicles are able to solve problems on the road that aligns with the moral values of humans around it. This is a challenge because of the complex nature of humans who may all make different decisions based on their personal values. However, by collecting a large amount of decisions from humans all over the world, researchers can begin to understand patterns in the context of a particular culture, community, and people. == Other features == The Moral Machine was deployed in June 2016. In October 2016, a feature was added that offered users the option to fill a survey about their demographics, political views, and religious beliefs. Between November 2016 and March 2017, the website was progressively translated into nine languages in addition to English (Arabic, Chinese, French, German, Japanese, Korean, Portuguese, Russian, and Spanish). Overall, the Moral Machine offers four different modes, with the focus being on the data-gathering feature of the website, called the Judge mode. This means that the Moral Machine, in addition to providing their own scenarios for users to judge, also invites users to create their own scenarios to be submitted and approved so that other people may also judge those scenarios. Data is also open sourced for anyone to explore via an interactive map that is featured on the Moral Machine website. == In the literature == Studies and research on the Moral Machine have taken a wide variety of approaches. However, theological examinations of the topic are still scarce where two bodies of work that examine such perspective currently exist in this regard: One is Buddhist while the other is Christian.

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