One of the first Prolog implementations made available by LPA was micro-PROLOG[2] which ran on popular 8-bit home computers such as the Sinclair ZX Spectrum[3] and Apple II.
One of the first use cases of Prolog was in the legal area namely, the British Nationality Act.
Lance Elliot wrote: "The British Nationality Act was passed in 1981 and shortly thereafter was used as a means of showcasing the efficacy of using Artificial Intelligence (AI) techniques and technologies, doing so to explore how the at-the-time newly enacted statutory law might be encoded into a computerized logic-based formalization. A now oft-cited research paper entitled “The British Nationality Act as a Logic Program” was published in 1986 in the prestigious Communication of the ACM and subsequently became a hallmark for subsequent work in AI and the law." [4]
You can access this much heralded research paper here.[5]
The 8-bit micro-PROLOG interpreter was soon followed by micro-PROLOG Professional one of the first Prolog implementations for the 'new' 16-bit IBM Pcs running MS-DOS. micro-PROLOG Professional could access all of the 640K memory available under MS-DOS and therefore manage much larger programs
In 1985, LPA released LPA MacProlog which ran on the MacPlus and Mac II computers which could access up to 4 Mb memory. MacProlog was later licensed to Quintus for re-distribution in the USA.
In 1989, LPA started work on a new 32-bit Prolog compiler which could use DOS-extender technology to access up to 4GB memory.
This became the basis for LPA Prolog for Windows, aka WIN-PROLOG, which was then released for Windows 3.0 in 1990.
As well as continuing with Prolog compiler technology development, LPA has a track record of creating innovative associated tools and products to address specific challenges and opportunities.
Flex Expert System toolkit
In 1989, in response to the rise of interest in Expert Ssytems and the emergence of products such as Crystal, GoldWorks, NExpert, LPA developed the Flex expert system toolkit, which incorporated frame-based reasoning with inheritance, rule-based programming and data-driven procedures. Flex has its own English-like Knowledge Specification Language (KSL) which means that knowledge and rules are defined in an easy-to-read and understand way.[7]
LPA supported Flex on Windows, DOS and Macintosh PCs, as an add-on toolkit to its various LPA Prolog systems and eanbled LPA to enter the then quick vibrant Expert Systems rules-market.
Flex was quickly established as the leading Prolog-based expert system toolkit and was licensed to other Prolog providors on other hardware platforms including Telecomputing Plc to supplement Top One on IBM and ICL mainframes.[8]
Other implementations included Quintec-Flex, Quintus Flex, Poplog Flex and BIM Flex which were all running on Unix and/or Vax/VMS platforms.
POPLOG-Flex was used to build BRAND EVALUATOR - an expert system to assist brand specialists in evaluating the worth of branded products[9]
Quintec-Flex was used to build a hybrid system for the non-linear dynamic analysis/design of coupled shear walls [10]
Flex was adopted by the Open University as part of its course T396, "Artificial intelligence for technology"[11] which was designed by Prof Adrian Hopgood. Some of the teaching material is now available on his AI tookit website.
Flex was also used by David A Ferrucci and Selmer Bringsjord in their storytelling machine, BRUTUS.[12]
PVG
In 1992, LPA helped set up the Prolog Vendors Group,[13] a not-for-profit organization whose aim was to help promote Prolog by making people aware of its usage in industry.
Business Integrity Ltd and Contract Express
Between 1996 and 1998, based on work co-funded through a DTI Smart award, LPA developed ScaffoldIT,[14][15] a tool for building dynamic documents and intelligent web sites. This technology, built using the LPA Prolog engine and associated ProWeb Server, was able to generate complex, personalised documents such as insurance policy schedules, legal contracts, and complex sales proposals, over the Web.
In 1999/2000, LPA helped set up Business Integrity Ltd, as a Joint Venture with Tarlo-Lyons, to bring the above document assembly technology to market. This product eventually became Contract Express. Contract Express became very popular amongst large law firms and was sold worldwide for both internal and external use.
Partners and GCs liked Contract Express because lawyers were able to quickly and accurately automate and update their legal templates in Word without requiring IT specialists to convert them into programs.
As a result of the commercial success of Contract Express, BIL was acquired by Thomson Reuters in 2015.[16]
The very early days of BIL are described by Clive Spenser here.[17]
VisiRule
In 2004, LPA launched VisiRule [18] a graphical tool for developing knowledge-based and decision support systems. VisiRule was described in IEEE Potentials in 2007 (see Drawing on your knowledge with VisiRule):
VisiRule has been used in various sectors, to build legal expert systems, machine diagnostic programs, medical and financial advice systems, etc.[citation needed]
In 2023, VisiRule was listed as one of the 5 best decision support software for large enterprises in 2024.[19]
Customers
For many years, LPA has worked closely with Valdis Krebs, an American-Latvian researcher, author, and consultant in the field of social and organizational network analysis. Valdis is the founder and chief scientist of Orgnet, and the creator of the popular Inflow [20] software package.
LPA Prolog and Flex were used to create Allergenius, an expert system for the interpretation of allergen microarray results. Rules representing the knowledge base (KB) were derived from the literature and specialized databases. The input data included the patient's ID and disease(s), the results of either a skin prick test or specific IgE assays and ISAC results. The output was a medical report.[21]
Related Research Articles
In artificial intelligence, an expert system is a computer system emulating the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural code. The first expert systems were created in the 1970s and then proliferated in the 1980s. Expert systems were among the first truly successful forms of artificial intelligence (AI) software. An expert system is divided into two subsystems: the inference engine and the knowledge base. The knowledge base represents facts and rules. The inference engine applies the rules to the known facts to deduce new facts. Inference engines can also include explanation and debugging abilities.
Knowledge representation and reasoning is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems, and represent knowledge in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning, such as the application of rules or the relations of sets and subsets.
Logic programming is a programming, database and knowledge-representation and reasoning paradigm which is based on formal logic. A program, database or knowledge base in a logic programming language is a set of sentences in logical form, expressing facts and rules about some problem domain. Major logic programming language families include Prolog, Answer Set Programming (ASP) and Datalog. In all of these languages, rules are written in the form of clauses:
Prolog is a logic programming language that has its origins in artificial intelligence and computational linguistics.
Planner is a programming language designed by Carl Hewitt at MIT, and first published in 1969. First, subsets such as Micro-Planner and Pico-Planner were implemented, and then essentially the whole language was implemented as Popler by Julian Davies at the University of Edinburgh in the POP-2 programming language. Derivations such as QA4, Conniver, QLISP and Ether were important tools in artificial intelligence research in the 1970s, which influenced commercial developments such as Knowledge Engineering Environment (KEE) and Automated Reasoning Tool (ART).
In artificial intelligence, symbolic artificial intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. Symbolic AI used tools such as logic programming, production rules, semantic nets and frames, and it developed applications such as knowledge-based systems, symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems.
The Fifth Generation Computer Systems was a 10-year initiative begun in 1982 by Japan's Ministry of International Trade and Industry (MITI) to create computers using massively parallel computing and logic programming. It aimed to create an "epoch-making computer" with supercomputer-like performance and to provide a platform for future developments in artificial intelligence. FGCS was ahead of its time, and its excessive ambitions led to commercial failure. However, on a theoretical level, the project was a strong stimulus for the development of concurrent logic programming.
In the field of artificial intelligence, an inference engine is a component of an intelligent system that applies logical rules to the knowledge base to deduce new information. The first inference engines were components of expert systems. The typical expert system consisted of a knowledge base and an inference engine. The knowledge base stored facts about the world. The inference engine applied logical rules to the knowledge base and deduced new knowledge. This process would iterate as each new fact in the knowledge base could trigger additional rules in the inference engine. Inference engines work primarily in one of two modes either special rule or facts: forward chaining and backward chaining. Forward chaining starts with the known facts and asserts new facts. Backward chaining starts with goals, and works backward to determine what facts must be asserted so that the goals can be achieved.
Logic in computer science covers the overlap between the field of logic and that of computer science. The topic can essentially be divided into three main areas:
Legal informatics is an area within information science.
Knowledge-based engineering (KBE) is the application of knowledge-based systems technology to the domain of manufacturing design and production. The design process is inherently a knowledge-intensive activity, so a great deal of the emphasis for KBE is on the use of knowledge-based technology to support computer-aided design (CAD) however knowledge-based techniques can be applied to the entire product lifecycle.
A deductive language is a computer programming language in which the program is a collection of predicates ('facts') and rules that connect them. Such a language is used to create knowledge based systems or expert systems which can deduce answers to problem sets by applying the rules to the facts they have been given. An example of a deductive language is Prolog, or its database-query cousin, Datalog.
Prolog++ is an object-oriented toolkit for the Prolog logic programming language. It allows classes and class hierarchies to be created within Prolog programs.
Natural-language user interface is a type of computer human interface where linguistic phenomena such as verbs, phrases and clauses act as UI controls for creating, selecting and modifying data in software applications.
Knowledge-based configuration, also referred to as product configuration or product customization, is an activity of customising a product to meet the needs of a particular customer. The product in question may consist of mechanical parts, services, and software. Knowledge-based configuration is a major application area for artificial intelligence (AI), and it is based on modelling of the configurations in a manner that allows the utilisation of AI techniques for searching for a valid configuration to meet the needs of a particular customer.
In information technology a reasoning system is a software system that generates conclusions from available knowledge using logical techniques such as deduction and induction. Reasoning systems play an important role in the implementation of artificial intelligence and knowledge-based systems.
A legal expert system is a domain-specific expert system that uses artificial intelligence to emulate the decision-making abilities of a human expert in the field of law. Legal expert systems employ a rule base or knowledge base and an inference engine to accumulate, reference and produce expert knowledge on specific subjects within the legal domain.
This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.
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