Semiautomaton

In mathematics and theoretical computer science, a semiautomaton is a deterministic finite automaton having inputs but no output. It consists of a set Q of states, a set Σ called the input alphabet, and a function T: Q × Σ → Q called the transition function. Associated with any semiautomaton is a monoid called the characteristic monoid, input monoid, transition monoid or transition system of the semiautomaton, which acts on the set of states Q. This may be viewed either as an action of the free monoid of strings in the input alphabet Σ, or as the induced transformation semigroup of Q. In older books like Clifford and Preston (1967) semigroup actions are called "operands". In category theory, semiautomata essentially are functors. == Transformation semigroups and monoid acts == A transformation semigroup or transformation monoid is a pair ( M , Q ) {\displaystyle (M,Q)} consisting of a set Q (often called the "set of states") and a semigroup or monoid M of functions, or "transformations", mapping Q to itself. They are functions in the sense that every element m of M is a map m : Q → Q {\displaystyle m\colon Q\to Q} . If s and t are two functions of the transformation semigroup, their semigroup product is defined as their function composition ( s t ) ( q ) = ( s ∘ t ) ( q ) = s ( t ( q ) ) {\displaystyle (st)(q)=(s\circ t)(q)=s(t(q))} . Some authors regard "semigroup" and "monoid" as synonyms. Here a semigroup need not have an identity element; a monoid is a semigroup with an identity element (also called "unit"). Since the notion of functions acting on a set always includes the notion of an identity function, which when applied to the set does nothing, a transformation semigroup can be made into a monoid by adding the identity function. === M-acts === Let M be a monoid and Q be a non-empty set. If there exists a multiplicative operation μ : Q × M → Q {\displaystyle \mu \colon Q\times M\to Q} ( q , m ) ↦ q m = μ ( q , m ) {\displaystyle (q,m)\mapsto qm=\mu (q,m)} which satisfies the properties q 1 = q {\displaystyle q1=q} for 1 the unit of the monoid, and q ( s t ) = ( q s ) t {\displaystyle q(st)=(qs)t} for all q ∈ Q {\displaystyle q\in Q} and s , t ∈ M {\displaystyle s,t\in M} , then the triple ( Q , M , μ ) {\displaystyle (Q,M,\mu )} is called a right M-act or simply a right act. In long-hand, μ {\displaystyle \mu } is the right multiplication of elements of Q by elements of M. The right act is often written as Q M {\displaystyle Q_{M}} . A left act is defined similarly, with μ : M × Q → Q {\displaystyle \mu \colon M\times Q\to Q} ( m , q ) ↦ m q = μ ( m , q ) {\displaystyle (m,q)\mapsto mq=\mu (m,q)} and is often denoted as M Q {\displaystyle \,_{M}Q} . An M-act is closely related to a transformation monoid. However the elements of M need not be functions per se, they are just elements of some monoid. Therefore, one must demand that the action of μ {\displaystyle \mu } be consistent with multiplication in the monoid (i.e. μ ( q , s t ) = μ ( μ ( q , s ) , t ) {\displaystyle \mu (q,st)=\mu (\mu (q,s),t)} ), as, in general, this might not hold for some arbitrary μ {\displaystyle \mu } , in the way that it does for function composition. Once one makes this demand, it is completely safe to drop all parenthesis, as the monoid product and the action of the monoid on the set are completely associative. In particular, this allows elements of the monoid to be represented as strings of letters, in the computer-science sense of the word "string". This abstraction then allows one to talk about string operations in general, and eventually leads to the concept of formal languages as being composed of strings of letters. Another difference between an M-act and a transformation monoid is that for an M-act Q, two distinct elements of the monoid may determine the same transformation of Q. If we demand that this does not happen, then an M-act is essentially the same as a transformation monoid. === M-homomorphism === For two M-acts Q M {\displaystyle Q_{M}} and B M {\displaystyle B_{M}} sharing the same monoid M {\displaystyle M} , an M-homomorphism f : Q M → B M {\displaystyle f\colon Q_{M}\to B_{M}} is a map f : Q → B {\displaystyle f\colon Q\to B} such that f ( q m ) = f ( q ) m {\displaystyle f(qm)=f(q)m} for all q ∈ Q M {\displaystyle q\in Q_{M}} and m ∈ M {\displaystyle m\in M} . The set of all M-homomorphisms is commonly written as H o m ( Q M , B M ) {\displaystyle \mathrm {Hom} (Q_{M},B_{M})} or H o m M ( Q , B ) {\displaystyle \mathrm {Hom} _{M}(Q,B)} . The M-acts and M-homomorphisms together form a category called M-Act. == Semiautomata == A semiautomaton is a triple ( Q , Σ , T ) {\displaystyle (Q,\Sigma ,T)} where Σ {\displaystyle \Sigma } is a non-empty set, called the input alphabet, Q is a non-empty set, called the set of states, and T is the transition function T : Q × Σ → Q . {\displaystyle T\colon Q\times \Sigma \to Q.} When the set of states Q is a finite set—it need not be—, a semiautomaton may be thought of as a deterministic finite automaton ( Q , Σ , T , q 0 , A ) {\displaystyle (Q,\Sigma ,T,q_{0},A)} , but without the initial state q 0 {\displaystyle q_{0}} or set of accept states A. Alternately, it is a finite-state machine that has no output, and only an input. Any semiautomaton induces an act of a monoid in the following way. Let Σ ∗ {\displaystyle \Sigma ^{}} be the free monoid generated by the alphabet Σ {\displaystyle \Sigma } (so that the superscript is understood to be the Kleene star); it is the set of all finite-length strings composed of the letters in Σ {\displaystyle \Sigma } . For every word w in Σ ∗ {\displaystyle \Sigma ^{}} , let T w : Q → Q {\displaystyle T_{w}\colon Q\to Q} be the function, defined recursively, as follows, for all q in Q: If w = ε {\displaystyle w=\varepsilon } , then T ε ( q ) = q {\displaystyle T_{\varepsilon }(q)=q} , so that the empty word ε {\displaystyle \varepsilon } does not change the state. If w = σ {\displaystyle w=\sigma } is a letter in Σ {\displaystyle \Sigma } , then T σ ( q ) = T ( q , σ ) {\displaystyle T_{\sigma }(q)=T(q,\sigma )} . If w = σ v {\displaystyle w=\sigma v} for σ ∈ Σ {\displaystyle \sigma \in \Sigma } and v ∈ Σ ∗ {\displaystyle v\in \Sigma ^{}} , then T w ( q ) = T v ( T σ ( q ) ) {\displaystyle T_{w}(q)=T_{v}(T_{\sigma }(q))} . Let M ( Q , Σ , T ) {\displaystyle M(Q,\Sigma ,T)} be the set M ( Q , Σ , T ) = { T w | w ∈ Σ ∗ } . {\displaystyle M(Q,\Sigma ,T)=\{T_{w}\vert w\in \Sigma ^{}\}.} The set M ( Q , Σ , T ) {\displaystyle M(Q,\Sigma ,T)} is closed under function composition; that is, for all v , w ∈ Σ ∗ {\displaystyle v,w\in \Sigma ^{}} , one has T w ∘ T v = T v w {\displaystyle T_{w}\circ T_{v}=T_{vw}} . It also contains T ε {\displaystyle T_{\varepsilon }} , which is the identity function on Q. Since function composition is associative, the set M ( Q , Σ , T ) {\displaystyle M(Q,\Sigma ,T)} is a monoid: it is called the input monoid, characteristic monoid, characteristic semigroup or transition monoid of the semiautomaton ( Q , Σ , T ) {\displaystyle (Q,\Sigma ,T)} . == Properties == If the set of states Q is finite, then the transition functions are commonly represented as state transition tables. The structure of all possible transitions driven by strings in the free monoid has a graphical depiction as a de Bruijn graph. The set of states Q need not be finite, or even countable. As an example, semiautomata underpin the concept of quantum finite automata. There, the set of states Q are given by the complex projective space C P n {\displaystyle \mathbb {C} P^{n}} , and individual states are referred to as n-state qubits. State transitions are given by unitary n×n matrices. The input alphabet Σ {\displaystyle \Sigma } remains finite, and other typical concerns of automata theory remain in play. Thus, the quantum semiautomaton may be simply defined as the triple ( C P n , Σ , { U σ 1 , U σ 2 , … , U σ p } ) {\displaystyle (\mathbb {C} P^{n},\Sigma ,\{U_{\sigma _{1}},U_{\sigma _{2}},\dotsc ,U_{\sigma _{p}}\})} when the alphabet Σ {\displaystyle \Sigma } has p letters, so that there is one unitary matrix U σ {\displaystyle U_{\sigma }} for each letter σ ∈ Σ {\displaystyle \sigma \in \Sigma } . Stated in this way, the quantum semiautomaton has many geometrical generalizations. Thus, for example, one may take a Riemannian symmetric space in place of C P n {\displaystyle \mathbb {C} P^{n}} , and selections from its group of isometries as transition functions. The syntactic monoid of a regular language is isomorphic to the transition monoid of the minimal automaton accepting the language. == Literature == A. H. Clifford and G. B. Preston, The Algebraic Theory of Semigroups. American Mathematical Society, volume 2 (1967), ISBN 978-0-8218-0272-4. F. Gecseg and I. Peak, Algebraic Theory of Automata (1972), Akademiai Kiado, Budapest. W. M. L. Holcombe, Algebraic Automata Theory (1982), Cambridge University Press J. M. Howie, Automata and Languages, (1991), Cla

Cinema 4D

Cinema 4D is a 3D software suite developed by the German company Maxon. == Overview == As of R21, only a single version of Cinema 4D is available. It replaces all previous variants, including BodyPaint 3D, and includes all features of the past 'Studio' variant. With R21, all binaries were unified. There is no technical difference between commercial, educational, or demo versions. The difference is now only in licensing. 2014 saw the release of Cinema 4D Lite, which came packaged with Adobe After Effects Creative Cloud 2014. "Lite" acts as an introductory version, with many features withheld. This is part of a partnership between the two companies, where a Maxon-produced plug-in, called Cineware, allows any variant to create a seamless workflow with After Effects. The "Lite" variant is dependent on After Effects CC, needing the latter application running to launch, and is only sold as a package component included with After Effects CC through Adobe. Initially, Cinema 4D was developed for Amiga computers in the early 1990s, and the first three versions of the program were available exclusively for that platform. With v4, however, Maxon began to develop the application for Windows and Macintosh computers as well, citing the wish to reach a wider audience and the growing instability of the Amiga market following Commodore's bankruptcy. It was also released for BeOS. On Linux, Cinema 4D is available as a commandline rendering version. == Modules and older variants == From R12 to R20, Cinema 4D was available in four variants. A core Cinema 4D 'Prime' application, a 'Broadcast' version with additional motion-graphics features, 'Visualize,' which adds functions for architectural design and 'Studio,' which includes all modules. From Release 8 until Release 11.5, Cinema 4D had a modular approach to the application, with the ability to expand upon the core application with various modules. This ended with Release 12, though the functionality of these modules remains in the different flavors of Cinema 4D (Prime, Broadcast, Visualize, Studio) The old modules were: Advanced Render (global illumination/HDRI, caustics, ambient occlusion and sky simulation) BodyPaint 3D (direct painting on UVW meshes; now included in the core. In essence Cinema 4D Core/Prime and the BodyPaint 3D products are identical. The only difference between the two is the splash screen that is shown at startup and the default user interface.) Dynamics (for simulating soft body and rigid body dynamics) Hair (simulates hair, fur, grass, etc.) MOCCA (character animation and cloth simulation) MoGraph (Motion Graphics procedural modelling and animation toolset) NET Render (to render animations over a TCP/IP network in render farms) PyroCluster (simulation of smoke and fire effects) Prime (the core application) Broadcast (adds MoGraph2) Visualize (adds Virtual Walkthrough, Advanced Render, Sky, Sketch and Toon, data exchange, camera matching) Studio (the complete package) == Version history == == Use in industry == A number of films and related works have been modeled and rendered in Cinema 4D, including: == Cinebench == Cinebench is a cross-platform test suite which tests a computer's hardware capabilities. It can be used as a test for Cinema 4D's 3D modeling, animation, motion graphic and rendering performance on multiple CPU cores. The program "target[s] a certain niche and [is] better suited for high-end desktop and workstation platforms". Cinebench is commonly used to demonstrate hardware capabilities at tech shows to show a CPU performance, especially by tech YouTubers and review sites.

Logico-linguistic modeling

Logico-linguistic modeling is a method for building knowledge-based systems with a learning capability using conceptual models from soft systems methodology, modal predicate logic, and logic programming languages such as Prolog. == Overview == Logico-linguistic modeling is a six-stage method developed primarily for building knowledge-based systems (KBS), but it also has application in manual decision support systems and information source analysis. Logico-linguistic models have a superficial similarity to John F. Sowa's conceptual graphs; both use bubble style diagrams, both are concerned with concepts, both can be expressed in logic and both can be used in artificial intelligence. However, logico-linguistic models are very different in both logical form and in their method of construction. Logico-linguistic modeling was developed in order to solve theoretical problems found in the soft systems method for information system design. The main thrust of the research into has been to show how soft systems methodology (SSM), a method of systems analysis, can be extended into artificial intelligence. == Background == SSM employs three modeling devices i.e. rich pictures, root definitions, and conceptual models of human activity systems. The root definitions and conceptual models are built by stakeholders themselves in an iterative debate organized by a facilitator. The strengths of this method lie, firstly, in its flexibility, the fact that it can address any problem situation, and, secondly, in the fact that the solution belongs to the people in the organization and is not imposed by an outside analyst. Information requirements analysis (IRA) took the basic SSM method a stage further and showed how the conceptual models could be developed into a detailed information system design. IRA calls for the addition of two modeling devices: "Information Categories", which show the required information inputs and outputs from the activities identified in an expanded conceptual model; and the "Maltese Cross", a matrix which shows the inputs and outputs from the information categories and shows where new information processing procedures are required. A completed Maltese Cross is sufficient for the detailed design of a transaction processing system. The initial impetus to the development of logico-linguistic modeling was a concern with the theoretical problem of how an information system can have a connection to the physical world. This is a problem in both IRA and more established methods (such as SSADM) because none base their information system design on models of the physical world. IRA designs are based on a notional conceptual model and SSADM is based on models of the movement of documents. The solution to these problems provided a formula that was not limited to the design of transaction processing systems but could be used for the design of KBS with learning capability. == The six stages of logico-linguistic modeling == The logico-linguistic modeling method comprises six stages. === 1. Systems analysis === In the first stage logico-linguistic modeling uses SSM for systems analysis. This stage seeks to structure the problem in the client organization by identifying stakeholders, modelling organizational objectives and discussing possible solutions. At this stage it not assumed that a KBS will be a solution and logico-linguistic modeling often produces solutions that do not require a computerized KBS. Expert systems tend to capture the expertise, of individuals in different organizations, on the same topic. By contrast a KBS, produced by logico-linguistic modeling, seeks to capture the expertise of individuals in the same organization on different topics. The emphasis is on the elicitation of organizational or group knowledge rather than individual experts. In logico-linguistic modeling the stakeholders become the experts. The end point of this stage is an SSM style conceptual models such as figure 1. === 2. Language creation === According to the theory behind logico-linguistic modeling the SSM conceptual model building process is a Wittgensteinian language-game in which the stakeholders build a language to describe the problem situation. The logico-linguistic model expresses this language as a set of definitions, see figure 2. === 3. Knowledge elicitation === After the model of the language has been built putative knowledge about the real world can be added by the stakeholders. Traditional SSM conceptual models contain only one logical connective (a necessary condition). In order to represent causal sequences, "sufficient conditions" and "necessary and sufficient conditions" are also required. In logico-linguistic modeling this deficiency is remedied by two addition types of connective. The outcome of stage three is an empirical model, see figure 3. === 4. Knowledge representation === Modal predicate logic (a combination of modal logic and predicate logic) is used as the formal method of knowledge representation. The connectives from the language model are logically true (indicated by the "L" modal operator) and connective added at the knowledge elicitation stage are possibility true (indicated by the "M" modal operator). Before proceeding to stage 5, the models are expressed in logical formulae. === 5. Computer code === Formulae in predicate logic translate easily into the Prolog artificial intelligence language. The modality is expressed by two different types of Prolog rules. Rules taken from the language creation stage of model building process are treated as incorrigible. While rules from the knowledge elicitation stage are marked as hypothetical rules. The system is not confined to decision support but has a built in learning capability. === 6. Verification === A knowledge based system built using this method verifies itself. Verification takes place when the KBS is used by the clients. It is an ongoing process that continues throughout the life of the system. If the stakeholder beliefs about the real world are mistaken this will be brought out by the addition of Prolog facts that conflict with the hypothetical rules. It operates in accordance to the classic principle of falsifiability found in the philosophy of science == Applications == === Knowledge-based computer systems === Logico-linguistic modeling has been used to produce fully operational computerized knowledge based systems, such as one for the management of diabetes patients in a hospital out-patients department. === Manual decision support === In other projects the need to move into Prolog was considered unnecessary because the printed logico-linguistic models provided an easy-to-use guide to decision making. For example, a system for mortgage loan approval === Information source analysis === In some cases a KBS could not be built because the organization did not have all the knowledge needed to support all their activities. In these cases logico-linguistic modeling showed shortcomings in the supply of information and where more was needed. For example, a planning department in a telecoms company == Criticism == While logico-linguistic modeling overcomes the problems found in SSM's transition from conceptual model to computer code, it does so at the expense of increased stakeholder constructed model complexity. The benefits of this complexity are questionable and this modeling method may be much harder to use than other methods. This contention has been exemplified by subsequent research. An attempt by researchers to model buying decisions across twelve companies using logico-linguistic modeling required simplification of the models and removal of the modal elements.

Pattern language

A pattern language is an organized and coherent set of patterns, each of which describes a problem and the core of a solution that can be used in many ways within a specific field of expertise. The term was coined by architect Christopher Alexander and popularized by his 1977 book A Pattern Language. A pattern language can also be an attempt to express the deeper wisdom of what brings aliveness within a particular field of human endeavor, through a set of interconnected patterns. Aliveness is one placeholder term for "the quality that has no name": a sense of wholeness, spirit, or grace, that while of varying form, is precise and empirically verifiable. Alexander claims that ordinary people can use this design approach to successfully solve very large, complex design problems. == What is a pattern? == When a designer designs something – whether a house, computer program, or lamp – they must make many decisions about how to solve problems. A single problem is documented with its typical place (the syntax), and use (the grammar) with the most common and recognized good solution seen in the wild, like the examples seen in dictionaries. Each such entry is a single design pattern. Each pattern has a name, a descriptive entry, and some cross-references, much like a dictionary entry. A documented pattern should explain why that solution is good in the pattern's contexts. Elemental or universal patterns such as "door" or "partnership" are versatile ideals of design, either as found in experience or for use as components in practice, explicitly described as holistic resolutions of the forces in recurrent contexts and circumstances, whether in architecture, medicine, software development or governance, etc. Patterns might be invented or found and studied, such as the naturally occurring patterns of design that characterize human environments. Like all languages, a pattern language has vocabulary, syntax, and grammar – but a pattern language applies to some complex activity other than communication. In pattern languages for design, the parts break down in this way: The language description – the vocabulary – is a collection of named, described solutions to problems in a field of interest. These are called design patterns. So, for example, the language for architecture describes items like: settlements, buildings, rooms, windows, latches, etc. Each solution includes syntax, a description that shows where the solution fits in a larger, more comprehensive or more abstract design. This automatically links the solution into a web of other needed solutions. For example, rooms have ways to get light, and ways to get people in and out. The solution includes grammar that describes how the solution solves a problem or produces a benefit. So, if the benefit is unneeded, the solution is not used. Perhaps that part of the design can be left empty to save money or other resources; if people do not need to wait to enter a room, a simple doorway can replace a waiting room. In the language description, grammar and syntax cross index (often with a literal alphabetic index of pattern names) to other named solutions, so the designer can quickly think from one solution to related, needed solutions, and document them in a logical way. In Christopher Alexander's book A Pattern Language, the patterns are in decreasing order by size, with a separate alphabetic index. The web of relationships in the index of the language provides many paths through the design process. This simplifies the design work because designers can start the process from any part of the problem they understand and work toward the unknown parts. At the same time, if the pattern language has worked well for many projects, there is reason to believe that even a designer who does not completely understand the design problem at first will complete the design process, and the result will be usable. For example, skiers coming inside must shed snow and store equipment. The messy snow and boot cleaners should stay outside. The equipment needs care, so the racks should be inside. == Many patterns form a language == Just as words must have grammatical and semantic relationships to each other in order to make a spoken language useful, design patterns must be related to each other in position and utility order to form a pattern language. Christopher Alexander's work describes a process of decomposition, in which the designer has a problem (perhaps a commercial assignment), selects a solution, then discovers new, smaller problems resulting from the larger solution. Occasionally, the smaller problems have no solution, and a different larger solution must be selected. Eventually all of the remaining design problems are small enough or routine enough to be solved by improvisation by the builders, and the "design" is done. The actual organizational structure (hierarchical, iterative, etc.) is left to the discretion of the designer, depending on the problem. This explicitly lets a designer explore a design, starting from some small part. When this happens, it's common for a designer to realize that the problem is actually part of a larger solution. At this point, the design almost always becomes a better design. In the language, therefore, each pattern has to indicate its relationships to other patterns and to the language as a whole. This gives the designer using the language a great deal of guidance about the related problems that must be solved. The most difficult part of having an outside expert apply a pattern language is in fact to get a reliable, complete list of the problems to be solved. Of course, the people most familiar with the problems are the people that need a design. So, Alexander famously advocated on-site improvisation by concerned, empowered users, as a powerful way to form very workable large-scale initial solutions, maximizing the utility of a design, and minimizing the design rework. The desire to empower users of architecture was, in fact, what led Alexander to undertake a pattern language project for architecture in the first place. == Design problems in a context == An important aspect of design patterns is to identify and document the key ideas that make a good system different from a poor system (that may be a house, a computer program or an object of daily use), and to assist in the design of future systems. The idea expressed in a pattern should be general enough to be applied in very different systems within its context, but still specific enough to give constructive guidance. The range of situations in which the problems and solutions addressed in a pattern apply is called its context. An important part in each pattern is to describe this context. Examples can further illustrate how the pattern applies to very different situation. For instance, Alexander's pattern "A PLACE TO WAIT" addresses bus stops in the same way as waiting rooms in a surgery, while still proposing helpful and constructive solutions. The "Gang-of-Four" book Design Patterns by Gamma et al. proposes solutions that are independent of the programming language, and the program's application domain. Still, the problems and solutions described in a pattern can vary in their level of abstraction and generality on the one side, and specificity on the other side. In the end this depends on the author's preferences. However, even a very abstract pattern will usually contain examples that are, by nature, absolutely concrete and specific. Patterns can also vary in how far they are proven in the real world. Alexander gives each pattern a rating by zero, one or two stars, indicating how well they are proven in real-world examples. It is generally claimed that all patterns need at least some existing real-world examples. It is, however, conceivable to document yet unimplemented ideas in a pattern-like format. The patterns in Alexander's book also vary in their level of scale – some describing how to build a town or neighbourhood, others dealing with individual buildings and the interior of rooms. Alexander sees the low-scale artifacts as constructive elements of the large-scale world, so they can be connected to a hierarchic network. === Balancing of forces === A pattern must characterize the problems that it is meant to solve, the context or situation where these problems arise, and the conditions under which the proposed solutions can be recommended. Often these problems arise from a conflict of different interests or "forces". A pattern emerges as a dialogue that will then help to balance the forces and finally make a decision. For instance, there could be a pattern suggesting a wireless telephone. The forces would be the need to communicate, and the need to get other things done at the same time (cooking, inspecting the bookshelf). A very specific pattern would be just "WIRELESS TELEPHONE". More general patterns would be "WIRELESS DEVICE" or "SECONDARY ACTIVITY", suggesting that a secondary activity (such as talking on t

Versata

Versata is a privately held software company, one of several business units under the ESW Capital umbrella. Versata acquires underperforming or financially struggling enterprise software companies, integrates them into their portfolio, and makes operational changes to improve the viability and performance of the companies. == History == === Early years (1991–2000) === This company was founded in 1991 with the name Image Innovations; Naren Bakshi was co-founder and president, Kevin Fletcher Tweedy was vice president of technology, and they sold a development tool set named Image Application WorkBench that worked with Plexus Software's imaging platform. In 1997, the company name changed to Vision Software. They sold a small suite of software: Vision Builder for accelerated coding; and Vision StoryBoard Pro for creating software documentation. In 1998, their flagship product was a Java development tool named Vision JADE. In January 2000, the company changed names again, this time to Versata, and their e-business automation system, Versata Logic Suite, had three components: Versata Logic Server to host business rules written in Java, Versata Studio for developing the business rules, and Versata Connectors for connecting the logic server to IBM database servers. === Public company (2000–2006) === They went public in March 2000 during the dot-com bubble, raising about $94 million and reaching a market capitalization of over $2.5 billion despite reporting just $13 million in revenue and a $21 million loss in the prior year. In November 2000, Versata expanded into the business workflow area with the acquisition of Verve, Inc. and its workflow management system by the same name. From early 2001 through mid-2003, Versata's revenues were in quarter-over-quarter decline until Alan Baratz took over as CEO. Five consecutive quarters of growth followed until early 2005, when revenues once again took a downward plunge. In mid-2005, the company was notified by NASDAQ that it no longer met NASDAQ's requirements for continued listing, related to maintenance of a minimum amount of shareholder's equity, market value, or net income. In July 2005, Versata was delisted from NASDAQ and publicly traded on the OTC (also known as the Pink Sheets). == Versata, a business unit of ESW Capital == In January 2006, Austin-based Trilogy, Inc. acquired the company and took it private. Trilogy then proceeded to merge portions of Trilogy, specifically, Trilogy Technology Group, into Versata and began acquiring further companies, reorganizing dramatically and offshoring most technical positions to its office in Bangalore, India. From 2006 to 2008, Versata continued to make acquisitions mostly in US. Most of the employees in the acquired companies were laid -off with the majority work being offshored to its India office in Bangalore. In early 2009, Versata made another major overhaul of its business model when it asked all its employees in India to work as contractors through oDesk for a gDev which is an entity incorporated by Trilogy to manage its outsourcing activities. The only employees left in Versata were the ones in US. == Acquisitions == a Corizon was acquired by Metatomix, while Metatomix was part of Versata. b Infopia was acquired by Everest Software, while Everest Software was part of Versata. c Symphony Commerce was acquired by Quantum Retail, while Quantum Retail was part of Versata. == Legal disputes == === Patent infringement and "poison pill" lawsuits with Selectica === The legal disputes with Selectica began in 2004 (before Trilogy acquired Versata in January 2006) and lasted until 2010. While there were many suits and counter-suits, they largely centered around three issues: 2004–2006: Patent infringement in configure, price, and quote (CPQ) software 2005–2007: Patent infringement in contract lifecycle management (CLM) software 2008–2010: The "poison pill" lawsuit In 2004, Selectica and Trilogy had competing CPQ software: Selectica sold Solutions Advisor and Deal Optimization, while Trilogy sold Selling Chain. In April of that year, Trilogy Software sued Selectica for patent infringement. In 2005, before the court ruling, Trilogy made several offers to buy Selectica, but the board rejected them. In January 2006, the court ordered Selectica to pay Trilogy $7.5 million in damages. Four days after the January 2006 judgment in the first lawsuit, Trilogy announced its acquisition of Versata for an undisclosed amount. In 2005, Selectica had acquired the Determine CLM software platform, which included features that overlapped with some offered by Versata. In October 2006, Versata filed a second patent infringement lawsuit. The case was settled in 2007, with Selectica agreeing to pay Trilogy and Versata $10 million, plus up to $7.5 million in additional contingent payments. In 2008, Versata began acquiring Selectica stock. By December, Selectica's board amended its shareholder rights plan to adopt a "poison pill" with an unusually low trigger threshold: if any shareholder acquired more than 4.99% of company stock, their ownership would be diluted. The board explained that the move was meant to protect Selectica's net operating losses (NOLs), which were tax-deductible if the company returned to profitability. Under IRS Section 382, a significant change in stock ownership could cause those NOLs to be disqualified. Versata intentionally triggered the poison pill and also offered to sell back the stocks at a profit (greenmailing them), which prompted a legal dispute over whether Selectica's board had the authority to set such a low threshold and whether defending NOLs justified triggering shareholder dilution. The case ultimately reached the Delaware Supreme Court, which upheld the poison pill in October 2010, ruling in favor of Selectica. === Intellectual property lawsuit over joint development with Sun Microsystems === In 1998, Sun Microsystems hired Trilogy to help Sun's developers in California create a software configurator (later named the WC5 Configurator) that Sun's customers could use to modify products they wanted to buy, customizing products to have the features they wanted. Trilogy worked on the WC5 Configurator for several years, then Sun transferred the work to Oracle to finish. Trilogy believed that they owned the copyright to the work they'd done for Sun, and in 2006 after the merger with Versata they sued Sun for more than $100 million in damages. In April 2009, a jury ruled in favor of Sun and rejected Versata's claims. === Patent lawsuit and ruling on patents of abstract ideas with SAP === SAP developed Pricing Engine, a component in their enterprise resource planning (ERP) system. It competed with an older Trilogy product called Pricer, which was part of Trilogy's Selling Chain platform in the mid-1990s before they merged with Versata. In April 2007—the year after Trilogy acquired Versata—Versata filed a lawsuit against SAP for patent infringement. In August 2009, the jury agreed with Versata and awarded them $139 million. The court granted a new trial on damages and in September 2011, in the retrial, the jury awarded Versata $345 million. This then went to the US Court of Appeals, which in May 2013 affirmed the $345 million damages award, plus interest that had accumulated. In October 2014, Versata and SAP settled their litigation for an undisclosed amount of money. With the dispute between Versata and SAP settled, in June 2013 the Patent Trial and Appeal Board (PTAB) reviewed the validity of the patent itself, and issued a decision in a Covered Business Method (CBM) review, stating that the disputed items were abstract ideas and thus under the US patent law not patentable. In July 2015, the Federal Circuit agreed with PTAB's decision that the challenged items were not patentable. === Trade secrets and damages dispute with Internet Brands === Internet Brands was formerly known as CarsDirect and AutoData Solutions. Like Trilogy, they made software for automakers that helped customers compare vehicles online. In the late 1990s, Trilogy and Internet Brands tried to combine their products but failed to do so, and after a December 1999 lawsuit they made a settlement agreement in May 2001. In 2008, Versata sued Internet Brands claiming they had violated the settlement agreement by making presentations to potential clients stating they had a license from Versata to use and sell Versata technical solutions; and doing so had cost Versata business with Chrysler. Internet Brands' countersuit argued that Versata had misappropriated trade secrets and asked the jury to use Versata's business relationship with Toyota—including revenue from Toyota contracts—as a benchmark to calculate damages. The jury agreed and used that data to determine a $2 million damages award in favor of Internet Brands’ subsidiary, AutoData Solutions. Versata appealed the decision, and in January 2014 the court upheld the $2 million award to Internet Brands. === Patent challenges a

National Parking Platform

The National Parking Platform is a digital platform in the United Kingdom providing interoperability between car park operators, parking apps, and other service providers. It enables all parking apps that support the system: RingGo, JustPark, PayByPhone, Apcoa Connect, AppyParking, and Caura to work at all participating car parks. It has been rolled out in 13 local authorities so far. It was first developed by the Department for Transport starting in 2019, and since May 2025 is controlled by the British Parking Association on a not-for-profit basis. == Participating local authorities == Buckinghamshire Cheshire West and Chester Coventry City East Hertfordshire East Suffolk Liverpool City Manchester City Oxfordshire County Peterborough City Stevenage Sutton Walsall Welwyn Hatfield

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