Barney Pell (born March 18, 1968) is an American entrepreneur, angel investor and computer scientist. He was co-founder and CEO of Powerset, a pioneering natural language search startup, search strategist and architect for Microsoft's Bing search engine, a pioneer in the field of general game playing in artificial intelligence, and the architect of the first intelligent agent to fly onboard and control a spacecraft. He was co-founder, Vice Chairman and Chief Strategy Officer of Moon Express; co-founder and chairman of LocoMobi; and Associate Founder of Singularity University. == Career == === Education === Pell received his Bachelor of Science degree in symbolic systems from Stanford University in 1989, where he graduated Phi Beta Kappa and was a National Merit Scholar. Pell earned a PhD in computer science from Cambridge University in 1993, supervised by Stephen Pulman, where he was a Marshall Scholar. === Research === Pell's research is focused on basic problems in the study of intelligence, computer game playing, machine learning, natural language processing, autonomous robotics, and web search. Barney Pell has published over 30 technical papers on topics related to information retrieval, knowledge management, machine learning, artificial intelligence, and scheduling systems. In computer game playing and machine learning, he was a pioneer in the field of General Game Playing, and created programs to generate the rules of chess-like games and programs to play individual games directly from the rules without human assistance. He also did early work on machine learning in the game of Go and on an architecture for pragmatic reasoning for bidding in the game of Bridge. In natural language processing, he was a scientist in the Artificial Intelligence Center at SRI International, where he worked on the Core Language Engine. Barney Pell was the Technical Area Manager of the Collaborative and Assistant Systems area within the Computational Sciences Division (now the Intelligent Systems Division) at NASA Ames Research Center, where he oversaw a staff of 80 scientists working on information retrieval, search, knowledge management, machine learning, semantic technology, human centered systems, collaboration technology, adaptive user interfaces, human robot interaction, and other areas of artificial intelligence. From 1993 to 1998, Barney Pell worked as a Principal Investigator and Senior Computer Scientist at NASA Ames, where he conducted advanced research and development of autonomous control software for NASA's deep space missions. He was the Architect for the Deep Space One Remote Agent Experiment and the Project Lead for the Executive component of the Remote Agent Experiment, the first intelligent agent to fly onboard and control a spacecraft. === Business === Pell is an entrepreneur who has founded or co-founded several business ventures, including Powerset, Moon Express, and LocoMobi. He was the founder and CEO of Powerset, a San Francisco startup company that built a search engine based on natural language processing technology originally developed at XEROX PARC. On May 11, 2008, the company unveiled a tool for searching a fixed subset of Wikipedia using conversational phrases rather than keywords. On July 1, 2008, Microsoft signed an agreement to acquire Powerset for an estimated $100 million. Powerset became a part of Microsoft's search engine, Bing. From 2008 until August 2011, Pell served as Partner, Search Strategist, and Evangelist for Microsoft's search engine, Bing and as Head of Bing's Local and Mobile Search teams. Prior to joining Powerset, Pell was an Entrepreneur-in-Residence at Mayfield Fund, a venture capital firm in Silicon Valley. Pell is also a founder of Moon Express, Inc., a U.S. company awarded a $10M commercial lunar contract by NASA and a competitor in the Google Lunar X PRIZE. Pell was also co-founder and chairman of LocoMobi, Inc., a U.S. company developing mobile, software and hardware technology solutions for the parking industry. LocoMobi was winner of the Tie50 Award in 2014. Pell is also an associate founder of Singularity University and a Machine Learning Fellow at the Creative Destruction Lab at the Rotman School of Management From 1998 to 2000, Pell served as chief strategist and vice president of business development at StockMaster.com (acquired by Red Herring in March, 2000). From 2000 to 2002, Pell was Chief Strategist and Vice President of Business Development for Whizbang Labs. Pell has been an angel investor and advisor to numerous startup companies, including Pulse.io (acquired by Google), Aardvark (acquired by Google), Appjet (acquired by Google), Jibe Mobile (acquired by Google), Movity (acquired by Trulia), QuestBridge, BrandYourself, CrowdFlower (acquired by Appen), and LinkedIn. === Views and predictions === Pell has expressed views and predictions regarding technological advancements in coming years. He believes that humans will soon have "brain-machine interfaces that will let people interact with each other as if they had 'hangouts' in their mind." Pell predicts these interfaces to become available within 20 to 30 years. Pell also predicts advancements in bodily augmentation, such as "even-better-than-human prosthetics and high-quality tissue engineering within 10 years." Pell believes that with advancements in space exploration technology the moon will soon be a commercially viable resource for material such as platinum and water. == Awards and recognition == In 1986, Pell was awarded a National Merit Scholarship. In 1989, Pell was awarded a Marshall Scholarship. In 1989, Pell was elected Phi Beta Kappa. In 1997, Pell was part of the team award a NASA Software of the Year Award for the Deep Space 1 Remote Agent.
Rhetorical structure theory
Rhetorical structure theory (RST) is a theory of text organization that describes relations that hold between parts of text. It was originally developed by William Mann, Sandra Thompson, Christian M. I. M. Matthiessen and others at the University of Southern California's Information Sciences Institute (ISI) and defined in a 1988 paper. The theory was developed as part of studies of computer-based text generation. Natural language processing researchers later began using RST in automatic summarization and other applications. It explains coherence by postulating a hierarchical, connected structure of texts, which are labeled using a small, predefined inventory of relation types - for example, one part of a text may provide an elaboration on another part, provide background or specify a cause for another. In the 2000s, following the release of the first large-scale dataset implementing the theory, the RST Discourse Treebank (RST-DT), Daniel Marcu demonstrated the feasibility of practical applications of RST to discourse parsing and summarization at ISI. Originally limited to written text, subsequent work in the 2010s expanded RST to spoken language analysis, and the framework has been applied to a variety of languages including Farsi, German, Mandarin Chinese, Russian and Spanish. Following the introduction of Transformers, LLMs have been applied to automatic RST parsing, with results approaching human performance on parsing text in English. == Rhetorical relations == Rhetorical relations, also called coherence or discourse relations, are paratactic (coordinate) or hypotactic (subordinate) relations that hold across two or more text spans. The logical arrangement of relations in a text contributes to its coherence by connecting different propositions in a relational structure. RST using rhetorical relations provides a systematic way for an analyst to analyze the underlying intention of a text. The analysis is usually built by reading the text and constructing a tree using the relations. The following example is a title and summary, appearing at the top of an article in Scientific American magazine (adapted from Ramachandran and Anstis, 1986). The original text, broken into numbered units, is: [Title:] The Perception of Apparent Motion [Abstract:] When the motion of an intermittently seen object is ambiguous the visual system resolves confusion by applying some tricks that reflect a built-in knowledge of properties of the physical world. In the figure, the numbers 1-5 show the corresponding units from the text above. Unit 5 provides an "elaboration" on unit 4, and therefore constitutes a less prominent satellite of unit 4, which acts as a nucleus for the relation. Units 4-5 form a relation "Means", explaining the means by which the visual system resolves confusion. Unit 3 is the Central Discourse Unit (CDU) of the text, since all units point to it directly or indirectly. Similarly units 1 and 2 form "preparation" and "circumstance" relations relative to their nuclei. Groups of units which serve as a satellite or nucleus together are called complex discourse units, and always span a set of adjacent EDUs. == Nuclearity in discourse == RST establishes two different types of units. Nuclei are considered as the most important parts of text whereas satellites contribute to the nuclei and are secondary. Nucleus contains basic information and satellite contains additional information about nucleus. The satellite is often incomprehensible without nucleus, whereas a text where satellites have been deleted can be understood to a certain extent. == Hierarchy in the analysis == RST relations are applied recursively in a text, until all units in that text are constituents in an RST relation. The result of such analyses is that RST structure are typically represented as trees, with one top level relation that encompasses other relations at lower levels. == Why RST? == From linguistic point of view, RST proposes a different view of text organization than most linguistic theories. RST points to a tight relation between relations and coherence in text From a computational point of view, it provides a characterization of text relations that has been implemented in different systems and for applications as text generation and summarization. == In design rationale == Computer scientists Ana Cristina Bicharra Garcia and Clarisse Sieckenius de Souz have used RST as the basis of a design rationale system called ADD+. In ADD+, RST is used as the basis for the rhetorical organization of a knowledge base, in a way comparable to other knowledge representation systems such as issue-based information system (IBIS). Similarly, RST has been used in representation schemes for argumentation.
Query understanding
Query understanding is the process of inferring the intent of a search engine user by extracting semantic meaning from the searcher’s keywords. Query understanding methods generally take place before the search engine retrieves and ranks results. It is related to natural language processing but specifically focused on the understanding of search queries. == Methods == === Stemming and lemmatization === Many languages inflect words to reflect their role in the utterance they appear in. The variation between various forms of a word is likely to be of little importance for the relatively coarse-grained model of meaning involved in a retrieval system, and for this reason the task of conflating the various forms of a word is a potentially useful technique to increase recall of a retrieval system. Stemming algorithms, also known as stemmers, typically use a collection of simple rules to remove suffixes intended to model the language’s inflection rules. For some languages, there are simple lemmatisation methods to reduce a word in query to its lemma or root form or its stem; for others, this operation involves non-trivial string processing and may require recognizing the word's part of speech or referencing a lexical database. The effectiveness of stemming and lemmatization varies across languages. === Query Segmentation === Query segmentation is a key component of query understanding, aiming to divide a query into meaningful segments. Traditional approaches, such as the bag-of-words model, treat individual words as independent units, which can limit interpretative accuracy. For languages like Chinese, where words are not separated by spaces, segmentation is essential, as individual characters often lack standalone meaning. Even in English, the BOW model may not capture the full meaning, as certain phrases—such as "New York"—carry significance as a whole rather than as isolated terms. By identifying phrases or entities within queries, query segmentation enhances interpretation, enabling search engines to apply proximity and ordering constraints, ultimately improving search accuracy and user satisfaction. === Entity recognition === Entity recognition is the process of locating and classifying entities within a text string. Named-entity recognition specifically focuses on named entities, such as names of people, places, and organizations. In addition, entity recognition includes identifying concepts in queries that may be represented by multi-word phrases. Entity recognition systems typically use grammar-based linguistic techniques or statistical machine learning models. === Query rewriting === Query rewriting is the process of automatically reformulating a search query to more accurately capture its intent. Query expansion adds additional query terms, such as synonyms, in order to retrieve more documents and thereby increase recall. Query relaxation removes query terms to reduce the requirements for a document to match the query, thereby also increasing recall. Other forms of query rewriting, such as automatically converting consecutive query terms into phrases and restricting query terms to specific fields, aim to increase precision. === Spelling Correction === Automatic spelling correction is a critical feature of modern search engines, designed to address common spelling errors in user queries. Such errors are especially frequent as users often search for unfamiliar topics. By correcting misspelled queries, search engines enhance their understanding of user intent, thereby improving the relevance and quality of search results and overall user experience.
Niki.ai
Niki was an artificial intelligence company headquartered in Bangalore, Karnataka. It was founded in May 2015 by IIT Kharagpur graduates Sachin Jaiswal, Keshav Prawasi, Shishir Modi, and Nitin Babel. The Niki android app was launched for a limited beta in June 2015, then released for public during YourStory's TechSparks 2015, and is a Tech30 company. The company raised an undisclosed amount in seed funding from Unilazer Ventures, a Mumbai-based VC firm founded by Ronnie Screwvala, in October 2015. This was followed by another seed funding round by Ratan Tata in May 2016. The company then raised US$2 million in Series A round of funding from SAP.iO, existing investors and some US and German-based investors, among others. Niki.ai shut down in October 2021 as per media reports. Website not working. == Product == The product is an artificial intelligence-powered chatbot which works as an intelligent personal assistant, named Niki. Leveraging natural language processing and machine learning, Niki presents a chat-based natural language user interface to the users where they can interact with Niki in their natural language. Niki understands how users chat in India, deciphers the words, in the context of product/services that they would like to purchase, and comes up with apt recommendations. Initially, it was only available on the Android platform as a mobile app. The company has expanded its operations to the Facebook Messenger and Apple iOS platforms. The company aims to soon be present on more messaging platforms like Slack and WhatsApp. The company currently provides 20+ services to over 2 million consumers, covering a wide spectrum ranging from utility services like mobile recharge, bill payments, travel services like cabs, buses, hotels and entertainment services like movies and events. Services such as flights and healthcare are also planned. == Partnerships == In September 2017, Infosys Finacle joined with Niki.ai to provide chat-based service to banking customers. In August 2017, Niki partnered with LazyPay to enable a 'buy now, pay later' feature for its users.
Natural language understanding
Natural language understanding (NLU) or natural language interpretation (NLI) is a subset of natural language processing in artificial intelligence that deals with machine reading comprehension. NLU has been considered an AI-hard problem. There is considerable commercial interest in the field because of its application to automated reasoning, machine translation, question answering, news-gathering, text categorization, voice-activation, archiving, and large-scale content analysis. == History == The program STUDENT, written in 1964 by Daniel Bobrow for his PhD dissertation at MIT, is one of the earliest known attempts at NLU by a computer. Eight years after John McCarthy coined the term artificial intelligence, Bobrow's dissertation (titled Natural Language Input for a Computer Problem Solving System) showed how a computer could understand simple natural language input to solve algebra word problems. A year later, in 1965, Joseph Weizenbaum at MIT wrote ELIZA, an interactive program that carried on a dialogue in English on any topic, the most popular being psychotherapy. ELIZA worked by simple parsing and substitution of key words into canned phrases and Weizenbaum sidestepped the problem of giving the program a database of real-world knowledge or a rich lexicon. Yet ELIZA gained surprising popularity as a toy project and can be seen as a very early precursor to current commercial systems such as those used by Ask.com. In 1969, Roger Schank at Stanford University introduced the conceptual dependency theory for NLU. This model, partially influenced by the work of Sydney Lamb, was extensively used by Schank's students at Yale University, such as Robert Wilensky, Wendy Lehnert, and Janet Kolodner. In 1970, William A. Woods introduced the augmented transition network (ATN) to represent natural language input. Instead of phrase structure rules ATNs used an equivalent set of finite-state automata that were called recursively. ATNs and their more general format called "generalized ATNs" continued to be used for a number of years. In 1971, Terry Winograd finished writing SHRDLU for his PhD thesis at MIT. SHRDLU could understand simple English sentences in a restricted world of children's blocks to direct a robotic arm to move items. The successful demonstration of SHRDLU provided significant momentum for continued research in the field. Winograd continued to be a major influence in the field with the publication of his book Language as a Cognitive Process. At Stanford, Winograd would later advise Larry Page, who co-founded Google. In the 1970s and 1980s, the natural language processing group at SRI International continued research and development in the field. A number of commercial efforts based on the research were undertaken, e.g., in 1982 Gary Hendrix formed Symantec Corporation originally as a company for developing a natural language interface for database queries on personal computers. However, with the advent of mouse-driven graphical user interfaces, Symantec changed direction. A number of other commercial efforts were started around the same time, e.g., Larry R. Harris at the Artificial Intelligence Corporation and Roger Schank and his students at Cognitive Systems Corp. In 1983, Michael Dyer developed the BORIS system at Yale which bore similarities to the work of Roger Schank and W. G. Lehnert. The third millennium saw the introduction of systems using machine learning for text classification, such as the IBM Watson. However, experts debate how much "understanding" such systems demonstrate: e.g., according to John Searle, Watson did not even understand the questions. John Ball, cognitive scientist and inventor of the Patom Theory, supports this assessment. Natural language processing has made inroads for applications to support human productivity in service and e-commerce, but this has largely been made possible by narrowing the scope of the application. There are thousands of ways to request something in a human language that still defies conventional natural language processing. According to Wibe Wagemans, "To have a meaningful conversation with machines is only possible when we match every word to the correct meaning based on the meanings of the other words in the sentence – just like a 3-year-old does without guesswork." == Scope and context == The umbrella term "natural language understanding" can be applied to a diverse set of computer applications, ranging from small, relatively simple tasks such as short commands issued to robots, to highly complex endeavors such as the full comprehension of newspaper articles or poetry passages. Many real-world applications fall between the two extremes, for instance text classification for the automatic analysis of emails and their routing to a suitable department in a corporation does not require an in-depth understanding of the text, but needs to deal with a much larger vocabulary and more diverse syntax than the management of simple queries to database tables with fixed schemata. Throughout the years various attempts at processing natural language or English-like sentences presented to computers have taken place at varying degrees of complexity. Some attempts have not resulted in systems with deep understanding, but have helped overall system usability. For example, Wayne Ratliff originally developed the Vulcan program with an English-like syntax to mimic the English speaking computer in Star Trek. Vulcan later became the dBase system whose easy-to-use syntax effectively launched the personal computer database industry. Systems with an easy-to-use or English-like syntax are, however, quite distinct from systems that use a rich lexicon and include an internal representation (often as first order logic) of the semantics of natural language sentences. Hence the breadth and depth of "understanding" aimed at by a system determine both the complexity of the system (and the implied challenges) and the types of applications it can deal with. The "breadth" of a system is measured by the sizes of its vocabulary and grammar. The "depth" is measured by the degree to which its understanding approximates that of a fluent native speaker. At the narrowest and shallowest, English-like command interpreters require minimal complexity, but have a small range of applications. Narrow but deep systems explore and model mechanisms of understanding, but they still have limited application. Systems that attempt to understand the contents of a document such as a news release beyond simple keyword matching and to judge its suitability for a user are broader and require significant complexity, but they are still somewhat shallow. Systems that are both very broad and very deep are beyond the current state of the art. == Components and architecture == Regardless of the approach used, most NLU systems share some common components. The system needs a lexicon of the language and a parser and grammar rules to break sentences into an internal representation. The construction of a rich lexicon with a suitable ontology requires significant effort, e.g., the Wordnet lexicon required many person-years of effort. The system also needs theory from semantics to guide the comprehension. The interpretation capabilities of a language-understanding system depend on the semantic theory it uses. Competing semantic theories of language have specific trade-offs in their suitability as the basis of computer-automated semantic interpretation. These range from naive semantics or stochastic semantic analysis to the use of pragmatics to derive meaning from context. Semantic parsers convert natural-language texts into formal meaning representations. Advanced applications of NLU also attempt to incorporate logical inference within their framework. This is generally achieved by mapping the derived meaning into a set of assertions in predicate logic, then using logical deduction to arrive at conclusions. Therefore, systems based on functional languages such as Lisp need to include a subsystem to represent logical assertions, while logic-oriented systems such as those using the language Prolog generally rely on an extension of the built-in logical representation framework. The management of context in NLU can present special challenges. A large variety of examples and counter examples have resulted in multiple approaches to the formal modeling of context, each with specific strengths and weaknesses.
Systems development life cycle
The systems development life cycle (SDLC) describes the typical phases and progression between phases during the development of a computer-based system. These phases progress from inception to retirement. At base, there is just one life cycle, but the taxonomy used to describe it may vary; the cycle may be classified into different numbers of phases and various names may be used for those phases. The SDLC is analogous to the life cycle of a living organism from its birth to its death. In particular, the SDLC varies by system in much the same way that each living organism has a unique path through its life. The SDLC does not prescribe how engineers should go about their work to move the system through its life cycle. Prescriptive techniques are referred to using various terms such as methodology, model, framework, and formal process. Other terms are used for the same concept as SDLC, including software development life cycle (also SDLC), application development life cycle (ADLC), and system design life cycle (also SDLC). These other terms focus on a different scope of development and are associated with different prescriptive techniques, but are about the same essential life cycle. The term "life cycle" is often written without a space, as "lifecycle", with the former more popular in the past and in non-engineering contexts. The acronym SDLC was coined when the longer form was more popular and has remained associated with the expansion, even though the shorter form is popular in engineering. Also, SDLC is relatively unique as opposed to the TLA SDL, which is highly overloaded. == Phases == Depending on the source, the SDLC is described as having different phases and using different terms. Even so, there are common aspects. The following attempts to describe notable phases using notable terminology. The phases are somewhat ordered by the natural sequence of development, although they can be overlapping and iterative. === Conceptualization === During conceptualization (a.k.a. conceptual design, system investigation, feasibility), options and priorities are considered. A feasibility study can determine whether the development effort is worthwhile via activities such as understanding user needs, cost estimation, benefit analysis, and resource analysis. A study should address operational, financial, technical, human factors, and legal/political concerns. === Requirements analysis === Requirements analysis (a.k.a. preliminary design) involves understanding the problem and determining what is needed. Often this involves engaging users to define the requirements and recording them in a document known as a requirements specification. === Design === During the design phase (a.k.a. detail design), a solution is planned. The plan can include relatively high-level information such as describing the major components of the system. The plan can include relatively low-level information such as describing functions, screen layout, business rules, and process flow. The design phase is informed by the requirements of the system. The design must satisfy each requirement. The design may be recorded in textual documents as well as functional hierarchy diagrams, example screen images, business rules, process diagrams, pseudo-code, and data models. === Construction === During construction (a.k.a. implementation, production), the system is realized. Based on the design, hardware and software components are created and integrated. This phase includes testing sub-components, components and the integration of some components, but typically does not include testing at the complete system level. This phase may include the development of training materials, including user manuals and help files. === Acceptance === The acceptance phase (a.k.a. system testing) is about testing the complete system to ensure that it meets customer expectations (requirements). === Deployment === The deployment phase (a.k.a. implementation) involves the logistics of delivery to the customer. Some systems are deployed as a single instance (i.e. in the cloud), and deployment may be ad hoc and manual. Some systems are built in quantity and are associated with manufacturing process and commissioning. This phase may include training users to use the system. It may include transitioning future development to support staff. === Maintenance === During the maintenance phase (a.k.a. operation, utilization, support) development is largely inactive, although this phase does include customer support for resolving user issues and recording suggestions for improvement. Fixes and enhancements are handled by returning to the first phase, conceptualization. For minor changes, the cycle may be significantly abbreviated compared to initial development. === Decommission === Decommission (a.k.a. disposition, retirement, phase-out) is when the system is removed from use, i.e., when it reaches end-of-life. == Practices == === Management and control === SDLC phase objectives are described in this section with key deliverables, a description of recommended tasks, and a summary of related control objectives for effective management. It is critical for the project manager to establish and monitor control objectives while executing projects. Control objectives are clear statements of the desired result or purpose and should be defined and monitored throughout a project. Control objectives can be grouped into major categories (domains), and relate to the SDLC phases as shown in the figure. To manage and control a substantial SDLC initiative, a work breakdown structure (WBS) captures and schedules the work. The WBS and all programmatic material should be kept in the "project description" section of the project notebook. The project manager chooses a WBS format that best describes the project. The diagram shows that coverage spans numerous phases of the SDLC, but the associated MCD (Management Control Domains) shows mappings to SDLC phases. For example, Analysis and Design is primarily performed as part of the Acquisition and Implementation Domain, and System Build and Prototype is primarily performed as part of delivery and support. === Work breakdown structured organization === The upper section of the WBS provides an overview of the project scope and timeline. It should also summarize the major phases and milestones. The middle section is based on the SDLC phases. WBS elements consist of milestones and tasks to be completed rather than activities to be undertaken, and have a deadline. Each task has a measurable output (e.g., an analysis document). A WBS task may rely on one or more activities (e.g., coding). Parts of the project needing support from contractors should have a statement of work (SOW). The development of an SOW does not occur during a specific phase of SDLC but is developed to include the work from the SDLC process that may be conducted by contractors. === Baselines === Baselines are established after four of the five phases of the SDLC, and are critical to the iterative nature of the model. Baselines become milestones. functional baseline: established after the conceptual design phase. allocated baseline: established after the preliminary design phase. product baseline: established after the detailed design and development phase. updated product baseline: established after the production construction phase. In the following diagram, these stages are divided into ten steps, from definition to creation and modification of IT work products:
Version space learning
Version space learning is a logical approach to machine learning, specifically binary classification. Version space learning algorithms search a predefined space of hypotheses, viewed as a set of logical sentences. Formally, the hypothesis space is a disjunction H 1 ∨ H 2 ∨ . . . ∨ H n {\displaystyle H_{1}\lor H_{2}\lor ...\lor H_{n}} (i.e., one or more of hypotheses 1 through n are true). A version space learning algorithm is presented with examples, which it will use to restrict its hypothesis space; for each example x, the hypotheses that are inconsistent with x are removed from the space. This iterative refining of the hypothesis space is called the candidate elimination algorithm, the hypothesis space maintained inside the algorithm, its version space. == The version space algorithm == In settings where there is a generality-ordering on hypotheses, it is possible to represent the version space by two sets of hypotheses: (1) the most specific consistent hypotheses, and (2) the most general consistent hypotheses, where "consistent" indicates agreement with observed data. The most specific hypotheses (i.e., the specific boundary SB) cover the observed positive training examples, and as little of the remaining feature space as possible. These hypotheses, if reduced any further, exclude a positive training example, and hence become inconsistent. These minimal hypotheses essentially constitute a (pessimistic) claim that the true concept is defined just by the positive data already observed: Thus, if a novel (never-before-seen) data point is observed, it should be assumed to be negative. (I.e., if data has not previously been ruled in, then it's ruled out.) The most general hypotheses (i.e., the general boundary GB) cover the observed positive training examples, but also cover as much of the remaining feature space without including any negative training examples. These, if enlarged any further, include a negative training example, and hence become inconsistent. These maximal hypotheses essentially constitute a (optimistic) claim that the true concept is defined just by the negative data already observed: Thus, if a novel (never-before-seen) data point is observed, it should be assumed to be positive. (I.e., if data has not previously been ruled out, then it's ruled in.) Thus, during learning, the version space (which itself is a set – possibly infinite – containing all consistent hypotheses) can be represented by just its lower and upper bounds (maximally general and maximally specific hypothesis sets), and learning operations can be performed just on these representative sets. After learning, classification can be performed on unseen examples by testing the hypothesis learned by the algorithm. If the example is consistent with multiple hypotheses, a majority vote rule can be applied. == Historical background == The notion of version spaces was introduced by Mitchell in the early 1980s as a framework for understanding the basic problem of supervised learning within the context of solution search. Although the basic "candidate elimination" search method that accompanies the version space framework is not a popular learning algorithm, there are some practical implementations that have been developed (e.g., Sverdlik & Reynolds 1992, Hong & Tsang 1997, Dubois & Quafafou 2002). A major drawback of version space learning is its inability to deal with noise: any pair of inconsistent examples can cause the version space to collapse, i.e., become empty, so that classification becomes impossible. One solution of this problem is proposed by Dubois and Quafafou that proposed the Rough Version Space, where rough sets based approximations are used to learn certain and possible hypothesis in the presence of inconsistent data.