End-to-end encryption (E2EE) is a method of implementing a secure communication system where only the sender and intended recipient can read the messages. No one else, including the system provider, telecom providers, Internet providers or malicious actors, can access the cryptographic keys needed to read or send messages. End-to-end encryption prevents data from being read or secretly modified, except by the sender and intended recipients. In many applications, messages are relayed from a sender to some recipients by a service provider. In an E2EE-enabled service, messages are encrypted on the sender's device such that no third party, including the service provider, has the means to decrypt them. The recipients retrieve encrypted messages and decrypt them independently on their own devices. Since third parties cannot decrypt the data being communicated or stored, services with E2EE are better at protecting user data from data breaches and espionage. Computer security experts, digital freedom organizations, and human rights activists advocate for the use of E2EE due to its security and privacy benefits, including its ability to resist mass surveillance. Popular messaging apps like WhatsApp, iMessage, Facebook Messenger, and Signal use end-to-end encryption for chat messages, with some also supporting E2EE of voice and video calls. As of May 2025, WhatsApp is the most widely used E2EE messaging service, with over 3 billion users. Meanwhile, Signal with an estimated 70 million users, is regarded as the current gold standard in secure messaging by cryptographers, protestors, and journalists. Since end-to-end encrypted services cannot offer decrypted messages in response to government requests, the proliferation of E2EE has been met with controversy. Around the world, governments, law enforcement agencies, and child protection groups have expressed concerns over its impact on criminal investigations. As of 2025, some governments have successfully passed legislation targeting E2EE, such as Australia's Telecommunications and Other Legislation Amendment Act (2018) and the Online Safety Act (2023) in the UK. Other attempts at restricting E2EE include the EARN IT Act in the US and the Child Sexual Abuse Regulation in the EU.[1] Nevertheless, some government bodies such as the UK's Information Commissioner's Office and the US's Cybersecurity and Infrastructure Security Agency (CISA) have argued for the use of E2EE, with Jeff Greene of the CISA advising that "encryption is your friend" following the discovery of the Salt Typhoon espionage campaign in 2024. == Definitions == End-to-end encryption is a means of ensuring the security of communications in applications like secure messaging. Under E2EE, messages are encrypted on the sender's device such that they can be decoded only by the final recipient's device. In many non-E2EE messaging systems, including email and many chat platforms, messages pass through intermediaries and are stored by a third party service provider, from which they are retrieved by the recipient. Even if messages are encrypted, they are only encrypted 'in transit', and are thus accessible by the service provider. Server-side disk encryption is also distinct from E2EE because it does not prevent the service provider from viewing the information, as they have the encryption keys and can simply decrypt it. The term "end-to-end encryption" originally only meant that the communication is never decrypted during its transport from the sender to the receiver. For example, around 2003, E2EE was proposed as an additional layer of encryption for GSM or TETRA, in addition to the existing radio encryption protecting the communication between the mobile device and the network infrastructure. This has been standardized by SFPG for TETRA. Note that in TETRA, the keys are generated by a Key Management Centre (KMC) or a Key Management Facility (KMF), not by the communicating users. Later, around 2014, the meaning of "end-to-end encryption" started to evolve when WhatsApp encrypted a portion of its network, requiring that not only the communication stays encrypted during transport, but also that the provider of the communication service is not able to decrypt the communications—maliciously or when requested by law enforcement agencies. Similarly, messages must be undecryptable in transit by attackers through man-in-the-middle attacks. This new meaning is now the widely accepted one. == Motivations == The lack of end-to-end encryption can allow service providers to easily provide search and other features, or to scan for illegal and unacceptable content. However, it also means that content can be read by anyone who has access to the data stored by the service provider, by design or via a backdoor. This can be a concern in many cases where privacy is important, such as in governmental and military communications, financial transactions, and when sensitive information such as health and biometric data are sent. If this content were shared without E2EE, a malicious actor or adversarial government could obtain it through unauthorized access or subpoenas targeted at the service provider. E2EE alone does not guarantee privacy or security. For example, the data may be held unencrypted on the user's own device or accessed through their own app if their credentials are compromised. == Modern implementations == === Messaging === In May 2026, Meta ended support for end-to-end encryption (E2EE) on Instagram, reversing a previous commitment to expand the technology across its messaging services. The company justified the move as a measure to mitigate fraudulent activity and facilitate the detection of harmful content. The decision highlighted a conflict between digital privacy and online safety; while child protection organizations supported the change to better identify predatory behavior, privacy advocates argued that removing E2EE compromises user security. As of 2025, messaging apps like Signal and WhatsApp are designed to exclusively use end-to-end encryption. Both Signal and WhatsApp use the Signal Protocol. Other messaging apps and protocols that support end-to-end encryption include Facebook Messenger, iMessage, Telegram, Matrix, and Keybase. Although Telegram supports end-to-end encryption, it has been criticized for not enabling it by default, instead supporting E2EE through opt-in "secret chats". As of 2020, Telegram did not support E2EE for group chats and no E2EE on its desktop clients. In 2022, after controversy over the use of Facebook Messenger messages in an abortion lawsuit in Nebraska, Facebook added support for end-to-end encryption in the Messenger app. Writing for Wired, technologist Albert Fox Cahn criticized Messenger's approach to end-to-end encryption, which required the user to opt into E2EE for each conversation and split the message thread into two chats which were easy for users to confuse. In December 2023, Facebook announced plans to enable end-to-end encryption by default despite pressure from British law enforcement agencies. As of 2016, many server-based communications systems did not include end-to-end encryption. These systems can only guarantee the protection of communications between clients and servers, meaning that users have to trust the third parties who are running the servers with the sensitive content. End-to-end encryption is regarded as safer because it reduces the number of parties who might be able to interfere or break the encryption. In the case of instant messaging, users may use a third-party client or plugin to implement an end-to-end encryption scheme over an otherwise non-E2EE protocol. === Audio and video conferencing === Signal and WhatsApp use end-to-end encryption for audio and video calls. Since 2020, Signal has also supported end-to-encrypted video calls. In 2024, Discord added end-to-end encryption for audio and video calls, voice channels, and certain live streams. However, they had no plans to implement E2EE for messages. In 2020, after acquiring Keybase, Zoom announced end-to-end encryption would be limited to paid accounts. Following criticism from human rights advocates, Zoom extended the feature to all users with accounts. In 2021, Zoom settled an $85M class action lawsuit over past misrepresentation about end-to-end encryption. The FTC confirmed Zoom previously retained access to meeting keys. === Other uses === Some encrypted backup and file sharing services provide client-side encryption. Nextcloud and MEGA, offer end-to-end encryption of shared files. The term "end-to-end encryption" is sometimes incorrectly used to describe client-side encryption. Some non-E2EE systems, such as Lavabit and Hushmail, have described themselves as offering "end-to-end" encryption when they did not. == Law enforcement and regulation == In 2022, Facebook Messenger came under scrutiny because the messages between a mother and daughter in Nebraska were used to seek criminal charges in an abortion-rel
Sentence embedding
In natural language processing, a sentence embedding is a representation of a sentence as a vector of numbers which encodes meaningful semantic information. State of the art embeddings are based on the learned hidden layer representation of dedicated sentence transformer models. BERT pioneered an approach involving the use of a dedicated [CLS] token prepended to the beginning of each sentence inputted into the model; the final hidden state vector of this token encodes information about the sentence and can be fine-tuned for use in sentence classification tasks. In practice however, BERT's sentence embedding with the [CLS] token achieves poor performance, often worse than simply averaging non-contextual word embeddings. SBERT later achieved superior sentence embedding performance by fine tuning BERT's [CLS] token embeddings through the usage of a siamese neural network architecture on the SNLI dataset. Other approaches are loosely based on the idea of distributional semantics applied to sentences. Skip-Thought trains an encoder-decoder structure for the task of neighboring sentences predictions; this has been shown to achieve worse performance than approaches such as InferSent or SBERT. An alternative direction is to aggregate word embeddings, such as those returned by Word2vec, into sentence embeddings. The most straightforward approach is to simply compute the average of word vectors, known as continuous bag-of-words (CBOW). However, more elaborate solutions based on word vector quantization have also been proposed. One such approach is the vector of locally aggregated word embeddings (VLAWE), which demonstrated performance improvements in downstream text classification tasks. == Applications == In recent years, sentence embedding has seen a growing level of interest due to its applications in natural language queryable knowledge bases through the usage of vector indexing for semantic search. LangChain for instance utilizes sentence transformers for purposes of indexing documents. In particular, an indexing is generated by generating embeddings for chunks of documents and storing (document chunk, embedding) tuples. Then given a query in natural language, the embedding for the query can be generated. A top k similarity search algorithm is then used between the query embedding and the document chunk embeddings to retrieve the most relevant document chunks as context information for question answering tasks. This approach is also known formally as retrieval-augmented generation. Though not as predominant as BERTScore, sentence embeddings are commonly used for sentence similarity evaluation which sees common use for the task of optimizing a Large language model's generation parameters is often performed via comparing candidate sentences against reference sentences. By using the cosine-similarity of the sentence embeddings of candidate and reference sentences as the evaluation function, a grid-search algorithm can be utilized to automate hyperparameter optimization. == Evaluation == A way of testing sentence encodings is to apply them on Sentences Involving Compositional Knowledge (SICK) corpus for both entailment (SICK-E) and relatedness (SICK-R). In the best results are obtained using a BiLSTM network trained on the Stanford Natural Language Inference (SNLI) Corpus. The Pearson correlation coefficient for SICK-R is 0.885 and the result for SICK-E is 86.3. A slight improvement over previous scores is presented in: SICK-R: 0.888 and SICK-E: 87.8 using a concatenation of bidirectional Gated recurrent unit.
Lighthill report
Artificial Intelligence: A General Survey, commonly known as the Lighthill report, is a scholarly article by James Lighthill, published in Artificial Intelligence: a paper symposium in 1973. It was compiled by Lighthill for the British Science Research Council as an evaluation of academic research in the field of artificial intelligence (AI). The report gave a very pessimistic prognosis for many core aspects of research in this field, stating that "In no part of the field have the discoveries made so far produced the major impact that was then promised". It "formed the basis for the decision by the British government to end support for AI research in most British universities", contributing to an AI winter in the United Kingdom. == Publication history == It was commissioned by the SRC in 1972 for Lighthill to "make a personal review of the subject [of AI]". Lighthill completed the report in July. The SRC discussed the report in September, and decided to publish it, together with some alternative points of view by Stuart Sutherland, Roger Needham, Christopher Longuet-Higgins, and Donald Michie. The SRC's decision to invite the report was partly a reaction to high levels of discord within the University of Edinburgh's Department of Artificial Intelligence, one of the earliest and biggest centres for AI research in the UK. On May 9, 1973, Lighthill debated several leading AI researchers (Donald Michie, John McCarthy, Richard Gregory) at the Royal Institution in London concerning the report. == Content == While the report was supportive of research into the simulation of neurophysiological and psychological processes, it was "highly critical of basic research in foundational areas such as robotics and language processing". The report stated that AI researchers had failed to address the issue of combinatorial explosion when solving problems within real-world domains. That is, the report states that whilst AI techniques may have worked within the scope of small problem domains, the techniques would not scale up well to solve more realistic problems. The report represents a pessimistic view of AI that began after early excitement in the field. The report divides AI research into three categories: Advanced Automation ("A"): applications of AI, such as optical character recognition, mechanical component design and manufacture, missile perception and guidance, etc. Computer-based Central Nervous System research ("C"): building computational models of human brains (neurobiology) and behavior (psychology). Bridge, or Building Robots ("B"): research that combines categories A and C. This category is intentionally vague. Projects in category A had had some success, but only in restricted domains where a large quantity of detailed knowledge was used in designing the program. This was disappointing to researchers who hoped for generic methods. Due to the issue of the combinatorial explosion, the amount of detailed knowledge required by the program quickly grew too large to be entered by hand, thus restricting projects to restricted domains. Projects in category C had had some measure of success. Artificial neural networks were successfully used to model neurobiological data. SHRDLU demonstrated that human use of language, even in fine details, depends on the semantics or knowledge, and is not purely syntactical. This was influential in psycholinguistics. Attempts to extend SHRDLU to larger domains of discourse was considered impractical, again due to the issue of the combinatorial explosion. Projects in category B were held to be failures. One important project, that of "programming and building a robot that would mimic human ability in a combination of eye-hand co-ordination and common-sense problem solving", was considered entirely disappointing. Similarly, chess playing programs were no better than human amateurs. Due to the combinatorial explosion, the run-time of general algorithms quickly grew impractical, requiring detailed problem-specific heuristics. The report stated that it was expected that within the next 25 years, category A would simply become applied technologies engineering, C would integrate with psychology and neurobiology, while category B would be abandoned.
METR
Model Evaluation and Threat Research (METR) (MEE-tər), is a nonprofit research institute, based in Berkeley, California, that evaluates frontier AI models' capabilities to carry out long-horizon, agentic tasks that some researchers argue could pose catastrophic risks to society. METR has worked with leading AI companies to conduct pre-deployment model evaluations and contribute to system cards, including OpenAI's o3, o4-mini, GPT-4o and GPT-4.5, and Anthropic's Claude models. METR's CEO and founder is Beth Barnes, a former alignment researcher at OpenAI who left in 2022 to form ARC Evals, the evaluation division of Paul Christiano's Alignment Research Center. In December 2023, ARC Evals was spun off into an independent 501(c)(3) nonprofit and renamed METR. == Research == A substantial amount of METR's research is focused on evaluating the capabilities of AI systems to conduct research and development of AI systems themselves, including RE-Bench, a benchmark designed to test whether AIs can "solve research engineering tasks and accelerate AI R&D". === Doubling time estimates === In March 2025, METR published a paper noting that the length of software engineering tasks that the leading AI model could complete had a doubling time of around 7 months between 2019 and 2024. In January 2026, METR released a new version of their time horizon estimates model (Time Horizon 1.1). According to the updated model, the rate of progress of AI capabilities has increased since 2023, with a post-2023 doubling time estimated at 130.8 days (4.3 months). Progress is thus estimated to be 20% more rapid. === Time horizon measurements === METR releases a "task-completion time horizon" for analysed AI models. This measures the "task duration (measured by human expert completion time) at which an AI agent is predicted to succeed with a given level of reliability." The metric is reported in two variants: the 50%-time horizon, which gives the task duration at which an AI model is estimated to succeed 50% of the time, and the 80%-time horizon, which gives the task duration at which an AI model is estimated to succeed 80% of the time. METR has published two versions of the underlying model: Time Horizon 1.0 and Time Horizon 1.1, the latter introduced in January 2026. As of 9 May 2026, the best-performing model is Claude Mythos, with a 50%-time horizon of likely at least 16 hours and an 80%-time horizon of 3 hours and 6 minutes. METR notes that "[m]easurements above 16 [hours] are unreliable with [their] current task suite". The following table provides time horizon estimates ordered by each model's release date:
User modeling
User modeling is the subdivision of human–computer interaction which describes the process of building up and modifying a conceptual understanding of the user. The main goal of user modeling is customization and adaptation of systems to the user's specific needs. The system needs to "say the 'right' thing at the 'right' time in the 'right' way". To do so it needs an internal representation of the user. Another common purpose is modeling specific kinds of users, including modeling of their skills and declarative knowledge, for use in automatic software-tests. User-models can thus serve as a cheaper alternative to user testing but should not replace user testing. == Background == A user model is the collection and categorization of personal data associated with a specific user. A user model is a (data) structure that is used to capture certain characteristics about an individual user, and a user profile is the actual representation in a given user model. The process of obtaining the user profile is called user modeling. Therefore, it is the basis for any adaptive changes to the system's behavior. Which data is included in the model depends on the purpose of the application. It can include personal information such as users' names and ages, their interests, their skills and knowledge, their goals and plans, their preferences and their dislikes or data about their behavior and their interactions with the system. There are different design patterns for user models, though often a mixture of them is used. Static user models Static user models are the most basic kinds of user models. Once the main data is gathered they are normally not changed again, they are static. Shifts in users' preferences are not registered and no learning algorithms are used to alter the model. Dynamic user models Dynamic user models allow a more up to date representation of users. Changes in their interests, their learning progress or interactions with the system are noticed and influence the user models. The models can thus be updated and take the current needs and goals of the users into account. Stereotype based user models Stereotype based user models are based on demographic statistics. Based on the gathered information users are classified into common stereotypes. The system then adapts to this stereotype. The application therefore can make assumptions about a user even though there might be no data about that specific area, because demographic studies have shown that other users in this stereotype have the same characteristics. Thus, stereotype based user models mainly rely on statistics and do not take into account that personal attributes might not match the stereotype. However, they allow predictions about a user even if there is rather little information about him or her. Highly adaptive user models Highly adaptive user models try to represent one particular user and therefore allow a very high adaptivity of the system. In contrast to stereotype based user models they do not rely on demographic statistics but aim to find a specific solution for each user. Although users can take great benefit from this high adaptivity, this kind of model needs to gather a lot of information first. == Data gathering == Information about users can be gathered in several ways. There are three main methods: Asking for specific facts while (first) interacting with the system Mostly this kind of data gathering is linked with the registration process. While registering users are asked for specific facts, their likes and dislikes and their needs. Often the given answers can be altered afterwards. Learning users' preferences by observing and interpreting their interactions with the system In this case users are not asked directly for their personal data and preferences, but this information is derived from their behavior while interacting with the system. The ways they choose to accomplish a tasks, the combination of things they takes interest in, these observations allow inferences about a specific user. The application dynamically learns from observing these interactions. Different machine learning algorithms may be used to accomplish this task. A hybrid approach which asks for explicit feedback and alters the user model by adaptive learning This approach is a mixture of the ones above. Users have to answer specific questions and give explicit feedback. Furthermore, their interactions with the system are observed and the derived information are used to automatically adjust the user models. Though the first method is a good way to quickly collect main data it lacks the ability to automatically adapt to shifts in users' interests. It depends on the users' readiness to give information and it is unlikely that they are going to edit their answers once the registration process is finished. Therefore, there is a high likelihood that the user models are not up to date. However, this first method allows the users to have full control over the collected data about them. It is their decision which information they are willing to provide. This possibility is missing in the second method. Adaptive changes in a system that learns users' preferences and needs only by interpreting their behavior might appear a bit opaque to the users, because they cannot fully understand and reconstruct why the system behaves the way it does. Moreover, the system is forced to collect a certain amount of data before it is able to predict the users' needs with the required accuracy. Therefore, it takes a certain learning time before a user can benefit from adaptive changes. However, afterwards these automatically adjusted user models allow a quite accurate adaptivity of the system. The hybrid approach tries to combine the advantages of both methods. Through collecting data by directly asking its users it gathers a first stock of information which can be used for adaptive changes. By learning from the users' interactions it can adjust the user models and reach more accuracy. Yet, the designer of the system has to decide, which of these information should have which amount of influence and what to do with learned data that contradicts some of the information given by a user. == System adaptation == Once a system has gathered information about a user it can evaluate that data by preset analytical algorithm and then start to adapt to the user's needs. These adaptations may concern every aspect of the system's behavior and depend on the system's purpose. Information and functions can be presented according to the user's interests, knowledge or goals by displaying only relevant features, hiding information the user does not need, making proposals what to do next and so on. One has to distinguish between adaptive and adaptable systems. In an adaptable system the user can manually change the system's appearance, behavior or functionality by actively selecting the corresponding options. Afterwards the system will stick to these choices. In an adaptive system a dynamic adaption to the user is automatically performed by the system itself, based on the built user model. Thus, an adaptive system needs ways to interpret information about the user in order to make these adaptations. One way to accomplish this task is implementing rule-based filtering. In this case a set of IF... THEN... rules is established that covers the knowledge base of the system. The IF-conditions can check for specific user-information and if they match the THEN-branch is performed which is responsible for the adaptive changes. Another approach is based on collaborative filtering. In this case information about a user is compared to that of other users of the same systems. Thus, if characteristics of the current user match those of another, the system can make assumptions about the current user by presuming that he or she is likely to have similar characteristics in areas where the model of the current user is lacking data. Based on these assumption the system then can perform adaptive changes. == Usages == Adaptive hypermedia: In an adaptive hypermedia system the displayed content and the offered hyperlinks are chosen on basis of users' specific characteristics, taking their goals, interests, knowledge and abilities into account. Thus, an adaptive hypermedia system aims to reduce the "lost in hyperspace" syndrome by presenting only relevant information. Adaptive educational hypermedia: Being a subdivision of adaptive hypermedia the main focus of adaptive educational hypermedia lies on education, displaying content and hyperlinks corresponding to the user's knowledge on the field of study. Intelligent tutoring system: Unlike adaptive educational hypermedia systems intelligent tutoring systems are stand-alone systems. Their aim is to help students in a specific field of study. To do so, they build up a user model where they store information about abilities, knowledge and needs of the user. The system can now adapt to this user by presenting approp
Programming tool
A programming tool or software development tool is a computer program that is used to develop another computer program, usually by helping the developer manage computer files. For example, a programmer may use a tool called a source code editor to edit source code files, and then a compiler to convert the source code into machine code files. They may also use build tools that automatically package executable program and data files into shareable packages or install kits. A set of tools that are run one after another, with each tool feeding its output to the next one, is called a toolchain. An integrated development environment (IDE) integrates the function of several tools into a single program. Usually, an IDE provides a source code editor as well as other built-in or plug-in tools that help with compiling, debugging, and testing. Whether a program is considered a development tool can be subjective. Some programs, such as the GNU compiler collection, are used exclusively for software development while others, such as Notepad, are not meant specifically for development but are nevertheless often used for programming. == Categories == Notable categories of development tools: Assembler – Converts assembly language into machine code Bug tracking system – Software application that records software bugs Build automation – Building software via an unattended fashion Code review software – Activity where one or more people check a program's code Compiler – Software that translates code from one programming language to another Compiler-compiler – Program that generates parsers or compilers, a.k.a. parser generator Debugger – Software for debugging a computer program Decompiler – Program translating executable to source code Disassembler – Computer program to translate machine language into assembly language Documentation generator – Automation technology for creating software documentation Graphical user interface builder – Software development tool Linker – Program that combines intermediate build files into an executable file Loader – Loads executable files into memory and prepares them for execution by the CPU. Memory debugger – Software memory problem finder Minifier – Removal of unnecessary characters in code without changing its functionality Pretty-printer – Formatting to make code or markup easier to readPages displaying short descriptions of redirect targets Performance profiler – Measuring the time or resources used by a section of a computer program Static code analyzer – Analysis of computer programs without executing themPages displaying short descriptions of redirect targets Source code editor – Text editor specializing in software codePages displaying short descriptions of redirect targets Source code generation – Type of computer programmingPages displaying short descriptions of redirect targets Version control system – Stores and tracks versions of files
Rule-based system
In computer science, a rule-based system is a computer system in which domain-specific knowledge is represented in the form of rules and general-purpose reasoning is used to solve problems in the domain. Two different kinds of rule-based systems emerged within the field of artificial intelligence in the 1970s: Production systems, which use if-then rules to derive actions from conditions. Logic programming systems, which use conclusion if conditions rules to derive conclusions from conditions. The differences and relationships between these two kinds of rule-based system has been a major source of misunderstanding and confusion. Both kinds of rule-based systems use either forward or backward chaining, in contrast with imperative programs, which execute commands listed sequentially. However, logic programming systems have a logical interpretation, whereas production systems do not. == Production system rules == A classic example of a production rule-based system is the domain-specific expert system that uses rules to make deductions or choices. For example, an expert system might help a doctor choose the correct diagnosis based on a cluster of symptoms, or select tactical moves to play a game. Rule-based systems can be used to perform lexical analysis to compile or interpret computer programs, or in natural language processing. Rule-based programming attempts to derive execution instructions from a starting set of data and rules. This is a more indirect method than that employed by an imperative programming language, which lists execution steps sequentially. === Construction === A typical rule-based system has four basic components: A list of rules or rule base, which is a specific type of knowledge base. An inference engine or semantic reasoner, which infers information or takes action based on the interaction of input and the rule base. The interpreter executes a production system program by performing the following match-resolve-act cycle: Match: In this first phase, the condition sides of all productions are matched against the contents of working memory. As a result a set (the conflict set) is obtained, which consists of instantiations of all satisfied productions. An instantiation of a production is an ordered list of working memory elements that satisfies the condition side of the production. Conflict-resolution: In this second phase, one of the production instantiations in the conflict set is chosen for execution. If no productions are satisfied, the interpreter halts. Act: In this third phase, the actions of the production selected in the conflict-resolution phase are executed. These actions may change the contents of working memory. At the end of this phase, execution returns to the first phase. Temporary working memory, which is a database of facts. A user interface or other connection to the outside world through which input and output signals are received and sent. Whereas the matching phase of the inference engine has a logical interpretation, the conflict resolution and action phases do not. Instead, "their semantics is usually described as a series of applications of various state-changing operators, which often gets quite involved (depending on the choices made in deciding which ECA rules fire, when, and so forth), and they can hardly be regarded as declarative". == Logic programming rules == The logic programming family of computer systems includes the programming language Prolog, the database language Datalog and the knowledge representation and problem-solving language Answer Set Programming (ASP). In all of these languages, rules are written in the form of clauses: A :- B1, ..., Bn. and are read as declarative sentences in logical form: A if B1 and ... and Bn. In the simplest case of Horn clauses (or "definite" clauses), which are a subset of first-order logic, all of the A, B1, ..., Bn are atomic formulae. Although Horn clause logic programs are Turing complete, for many practical applications, it is useful to extend Horn clause programs by allowing negative conditions, implemented by negation as failure. Such extended logic programs have the knowledge representation capabilities of a non-monotonic logic. == Differences and relationships between production rules and logic programming rules == The most obvious difference between the two kinds of systems is that production rules are typically written in the forward direction, if A then B, and logic programming rules are typically written in the backward direction, B if A. In the case of logic programming rules, this difference is superficial and purely syntactic. It does not affect the semantics of the rules. Nor does it affect whether the rules are used to reason backwards, Prolog style, to reduce the goal B to the subgoals A, or whether they are used, Datalog style, to derive B from A. In the case of production rules, the forward direction of the syntax reflects the stimulus-response character of most production rules, with the stimulus A coming before the response B. Moreover, even in cases when the response is simply to draw a conclusion B from an assumption A, as in modus ponens, the match-resolve-act cycle is restricted to reasoning forwards from A to B. Reasoning backwards in a production system would require the use of an entirely different kind of inference engine. In his Introduction to Cognitive Science, Paul Thagard includes logic and rules as alternative approaches to modelling human thinking. He does not consider logic programs in general, but he considers Prolog to be, not a rule-based system, but "a programming language that uses logic representations and deductive techniques" (page 40). He argues that rules, which have the form IF condition THEN action, are "very similar" to logical conditionals, but they are simpler and have greater psychological plausibility (page 51). Among other differences between logic and rules, he argues that logic uses deduction, but rules use search (page 45) and can be used to reason either forward or backward (page 47). Sentences in logic "have to be interpreted as universally true", but rules can be defaults, which admit exceptions (page 44). He does not observe that all of these features of rules apply to logic programming systems.