Over the last 50 years, the Lisp programming language has become the dominant computer language in the American Artificial Intelligence community. John McCarthy developed Lisp, at MIT in the late 1950's, to help computers cope with such problems as symblic differentiation and integration of algebraic expressions. The first Lisp interpreter was implemented at MIT. Lisp is the second oldest computer language (Fortran is the oldest).
John McCarthy built the heart of Lisp language design around Alonzo Church's Lambda Calculus which formalized the theoretical ideas at the forefront of research into computation and reasoning. Alonzo Church was born on June 14, 1903 in Washington, D.C. and died Friday, August 11, 1995 in Hudson, Ohio at the age of 92. He was buried in Princeton Cemetery. Church was professor of mathematics at Princeton University from 1929 to 1967 when he became professor of mathematics and philosophy at UCLA. His work has been greatly influential in the fields of mathematical logic, recursion theory, and theoretical computer science. At the time of his death, Church was widely regarded as the greatest living logician in the world. His most well-remembered contributions are the following: Church's Theorem (1936), showing that arithmetic is undecidable; Church's Thesis, conjecturing that effective computation is equivalent to the notion of a "recursive" function; The Lambda Calculus.
Lisp is understood as the model of a functional programming language today. There are people who believe that there once was a clean "pure" language design in the functional direction which was compromised by AI-programmers in search of efficiency. This view does not take into account, that around the end of the fifties, nobody, including McCarthy himself, seriously based his programming on the concept of mathematical function. It is quite certain that McCarthy for a long time associated programming with the design of stepwise executed algorithms.
It was McCarthy who, as the first, seemed to have developed the idea of using funtional terms (in the form of "function calls" or "subroutine calls") for every partial step of a program. This idea emerged more as a stylistic decision, proved to be sound and became the basis for a proper way of programming - functional progamming (or, as I prefer to call it, function-oriented programming). We should mention here that McCarthy at the same time conceived the idea of logic-oriented programming, that is, the idea of using logical formulae to express goals that a program should try to establish and of using the prover as programming language interpreter. It is an important fact that McCarthy as mathematician was familiar with some formal mathematical languages but did not have a deep, intimate understanding of all their details. McCarthy himself has stressed this fact. His aim was to use the mathematical formalismus as languages and not as calculi. This is the root of the historical fact that he never took the Lambda-Calculus conversion rules as a sound basis for Lisp implementation. We have to bear this in mind if we follow now the sequence of events that led to Lisp. It is due to McCarthy's work that functional programming is a usable way of programming today. The main practice of this programming style, done with Lisp, still shows his personal mark.
It seems that McCarthy had a feeling for the importance of a programming language for work in artificial intelligence already before 1955. In any case, the famous proposal for the Darmouth Summer Research Project on Artificial Intelligence -- dated the 31st of August 1955 - contains a research program for McCarthy which is devoted to this question: "During next year and during the Summer Research Project on Artificial Intelligence, I propose to study the relation of language to intelligence ..."
McCarthy compared (formal) logical and natural languages in this proposal for his personal research program. His conclusion was: " It therefore seems to be desirable to attempt to construct an artificial language which a computer can be programmed to use on problems and self-reference. It should correspond to English in the sense that short English statements about the given subject matter should have short correspondents in the language and so should short arguments or conjectural arguments." His goal: " I hope to try to formulate language having these properties and in addition to contain the notions of physical object, event, etc., with the hope that using this language it will be possible to program a machine to learn to play games well and do other tasks."
Lisp is a productive development and delivery language for building expert systems, providing a complete environment for the construction of rule based expert systems.
Conventional programming languages, such as FORTRAN and C, are designed and optimized for the procedural manipulation of data (such as numbers and arrays). Humans, however, often solve complex problems using very abstract, symbolic approaches which are not well suited for implementation in conventional languages. Although abstract information can be modeled in these languages, considerable programming effort is required to transform the information to a format usable with procedural programming paradigms.
One of the results of research in the area of artificial intelligence has been the development of techniques which allow the modeling of information at higher levels of abstraction. These techniques are embodied in languages or tools which allow programs to be built that closely resemble human logic in their implementation and are therefore easier to develop and maintain. These programs, which emulate human expertise in well defined problem domains, are called expert systems. The Lisp language has greatly reduced the effort and cost involved in developing an expert system.
Rule-based programming is one of the most commonly used techniques for developing expert systems. In this programming paradigm, rules are used to represent heuristics, or rules of thumb, which specify a set of actions to be performed for a given situation. A rule is composed of an if portion and a then portion. The if portion of a rule is a series of patterns which specify the facts (or data) which cause the rule to be applicable. The process of matching facts to patterns is called pattern matching. The expert system tool provides a mechanism, called the inference engine, which automatically matches facts against patterns and determines which rules are applicable. The if portion of a rule can actually be thought of as the whenever portion of a rule since pattern matching always occurs whenever changes are made to facts. The then portion of a rule is the set of actions to be executed when the rule is applicable. The actions of applicable rules are executed when the inference engine is instructed to begin execution. The inference engine selects a rule and then the actions of the selected rule are executed (which may affect the list of applicable rules by adding or removing facts). The inference engine then selects another rule and executes its actions. This process continues until no applicable rules remain.
Artificial intelligence (AI) has been one of the most controversial domains of inquiry in computer science since it was first proposed in the 1950s. Defined as the part of computer science concerned with designing systems that exhibit the characteristics associated with human intelligence--understanding language, learning, reasoning, solving problems, and so on (Barr and Feigenbaum, 1981)--the field has attracted researchers because of its ambitious goals and enormous underlying intellectual challenges. The field has been controversial because of its social, ethical, and philosophical implications. Such controversy has affected the funding environment for AI and the objectives of many research programs.
AI research is conducted by a range of scientists and technologists with varying perspectives, interests, and motivations. Scientists tend to be interested in understanding the underlying basis of intelligence and cognition, some with an emphasis on unraveling the mysteries of human thought and others examining intelligence more broadly. Engineering-oriented researchers, by contrast, are interested in building systems that behave intelligently. Some attempt to build systems using techniques analogous to those used by humans, whereas others apply a range of techniques adopted from fields such as information theory, electrical engineering, statistics, and pattern recognition. Those in the latter category often do not necessarily consider themselves AI researchers, but rather fall into a broader category of researchers interested in machine intelligence.
The concept of AI originated in the private sector, but the growth of the field, both intellectually and in the size of the research community, has depended largely on public investments. Public monies have been invested in a range of AI programs, from fundamental, long-term research into cognition to shorter-term efforts to develop operational systems. Most of the federal support has come from the Defense Advanced Research Projects Agency (DARPA, known during certain periods as ARPA) and other units of the Department of Defense (DOD). Other funding agencies have included the National Institutes of Health, National Science Foundation, and National Aeronautics and Space Administration (NASA), which have pursued AI applications of particular relevance to their missions--health care, scientific research, and space exploration.
The origins of AI research are intimately linked with two landmark papers on chess playing by machine. They were written in 1950 by Claude E. Shannon, a mathematician at Bell Laboratories who is widely acknowledged as a principal creator of information theory. In the late 1930s, while still a graduate student, he developed a method for symbolic analysis of switching systems and networks (Shannon, 1938), which provided scientists and engineers with much-improved analytical and conceptual tools. After working at Bell Labs for half a decade, Shannon published a paper on information theory (Shannon, 1948). Shortly thereafter, he published two articles outlining the construction or programming of a computer for playing chess (Shannon, 1950a,b).
Shannon's work inspired a young mathematician, John McCarthy, who, while a research instructor in mathematics at Princeton University, joined Shannon in 1952 in organizing a conference on automata studies, largely to promote symbolic modeling and work on the theory of machine intelligence. A year later, Shannon arranged for McCarthy and another future pioneer in AI, Marvin Minsky, then a graduate student in mathematics at Princeton and a participant in the 1952 conference, to work with him at Bell Laboratories during 1953.
By 1955, McCarthy believed that the theory of machine intelligence was sufficiently advanced, and that related work involved such a critical mass of researchers, that rapid progress could be promoted by a concentrated summer seminar at Dartmouth University, where he was then an assistant professor of mathematics. He approached the Rockefeller Foundation's Warren Weaver, also a mathematician and a promoter of cutting-edge science, as well as Shannon's collaborator on information theory. Weaver and his colleague Robert S. Morison, director for Biological and Medical Research, were initially skeptical (Weaver, 1955). Morison pushed McCarthy and Shannon to widen the range of participants and made other suggestions. McCarthy and Shannon responded with a widened proposal that heeded much of Morison's advice. They brought in Minsky and a well-known industrial researcher, Nathaniel Rochester of IBM, as co-principal investigators for the proposal, submitted in September 1955. In the proposal, the four researchers declared that the summer study was "to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it."
Lisp has been an important programming language in AI research, and its history demonstrates the more general benefits resulting from the efforts of AI researchers to tackle exceptionally difficult problems. As with other developments in AI, Lisp demonstrates how, in addressing problems in the representation and computational treatment of knowledge, AI researchers often stretched the limits of computing technology and were forced to invent new techniques that found their way into mainstream application.
Early AI researchers interested in logical reasoning and problem solving needed tools to represent logical formulas, proofs, plans, and computations on such objects. Existing programming techniques were very awkward for this purpose, inspiring the development of specialized programming languages, such as list-processing languages. List structures provide a simple and universal encoding of the expressions that arise in symbolic logic, formal language theory, and their applications to the formalization of reasoning and natural language understanding. Among early list-processing languages (the name is based on that phrase), Lisp was the most effective tool for representing both symbolic expressions and manipulations of them. It was also an object of study in itself. Lisp can readily operate on other Lisp programs that are represented as list structures, and it thus can be used for symbolic reasoning on programs. Lisp is also notable because it is based on ideas of mathematical logic that are of great importance in the study of computability and formal systems.
Lisp was successful in niche commercial applications. For instance, Lisp is the scripting language in AutoCAD, the widely used computer-aided design (CAD) program from AutoDesk. But it had much broader implications for other languages. Effective implementation of Lisp demanded some form of automatic memory management. Thus, Lisp had critical influence far beyond AI in the theory and design of programming languages, including all functional programming languages as well as object-oriented languages such as Simula-67, SmallTalk, and, most notably, Java. This is not just a happy accident, but rather a consequence of the conceptual breakthroughs arising from the effort to develop computational models of reasoning. Other examples include frame-based knowledge representations, which strongly influenced the development of object-oriented programming and object databases; rule-based and logic-programming language ideas, which found practical applications in expert systems, databases, and optimization techniques; and CAD representations for reasoning with uncertainty, which have found their way into manufacturing control, medical and equipment diagnosis, and human-computer interfaces.
Over the years, the Lisp programming language flourished in the academic community. In keeping with the rich intellectual tradition of Lisp, a number of experimental Lisp dialects emerged. When industry began using the language, the search for a common standard Lisp dialect began. Computer scientist Guy Steele, together with a group of other interested computer scientists, developed the specifications for a commercial standard Lisp dialect called Common Lisp.
Common Lisp has long been the leading language for software research and advanced development projects. Its ability to tackle the biggest problems is unmatched.
Common Lisp is rich in data types, supported by a high-level language model and garbage collection. In Common Lisp, all data are represented as objects. There are no out-of-language errors. This model encourages a high-level view of programs and an exploratory programming process that make Lisp programmers among the most productive in the world.
Common Lisp has grown and evolved over time, acquiring features and supporting paradigms as they've entered the world of computer science. It is now supported by an ANSI standard (ANSI X3.226:1994). This standard includes the Common Lisp Object System (CLOS); features like multimethods and dynamic class redefinition make CLOS among the most advanced object systems in the world.
Among the most important features of Common Lisp are:
The Scheme dialect of Lisp came into existence as a toy Lisp interpreter that Guy Steele and Gerald Sussman wrote in 1975 to study some aspects of Carl Hewitt's theory of actors, an object-oriented computational paradigm. Following the tradition of the AI languages "Planner" and "Conniver", Steele and Sussman named their language "Schemer"; the name was later truncated to "Scheme" to fit the 6-character filename limitation of the ITS operating system. This early Scheme language centered around a rather compact set of concepts and was composed of only a small number of language constructs and primitive data types; this "minimalistic" design approach has continued to influence the development of Scheme until today.
During the late 1970s, Scheme has been used at the MIT AI Lab as the basis for a number of papers exploring semantic concepts of programming languages. A first Scheme compiler (Rabbit) was written by Steele in 1978, and Scheme began to be used outside MIT during that period, mainly as the framework for experimentation by theoreticians (first at the University of Indiana, where similar research activities were going on). As Scheme is much smaller than most Lisp dialects and can be put to work on top of Lisp systems easily, various Scheme implementations emerged during the early 1980s (at the Universities of Yale and Indiana, and at MIT, among other sites). Also, several commercial implementations of Scheme were made by existing and newly founded companies in the following years, among them Chez Scheme, MacScheme for the Apple Macintosh, and PC Scheme by Texas Instruments.
Scheme rapidly became popular not only among groups interested in the theoretical and mathematical aspects of programming languages, but also as a tool for research and as the basis for computer science curricula at universities and colleges; it has even found use as a vehicle for teaching liberal arts students about programming. The now classic book Structure and Interpretation of Computer Programs (often referred to as SICP) by Harold Abelson and Gerald Sussman has significantly contributed to the popularity of Scheme in larger circles; the book is based on the curriculum of an undergraduate computer science course at MIT for which Scheme was adopted.
The Revised Report on Scheme that was written by Steele and Sussman in 1978 was the first in a series of language definitions called The Revised, ..., Revised Report on Scheme (the title is a running gag and is often abbreviated to R^nRS). The latest in this series of reports, the Revised, Revised, Revised, Revised Report on the Algorithmic Language Scheme (R^4RS), was published in 1991 and provides, among other things, a formal denotational semantics of the Scheme language. With the adoption of a new macro facility as a (semi-official) appendix to the R^4RS, Scheme became the first programming language with reliable, hygienic macros. This macro extension has been inspired by a pattern-directed syntactic substitution language invented by Eugen Kohlbecker in 1986 and the concept of syntactic closures.
In 1990 a slightly modified version of the R^3RS was adopted as an IEEE and ANSI standard; the relationship between this standard and the R^nRS is that the official standard corresponds to the R^n-1RS when the R^nRS is current.
The main impetus behind the evolvement of Scheme is an informally constituted group of Scheme authors who work on the revisions of the Scheme report at sporadic meetings and by electronic mail through an rrrs-authors mailing list. New features are added to the language only if an unanimous agreement can be reached within the group of Scheme authors. This rule ensures that the language remains relatively stable and small. In a way, Scheme can be regarded as the antithesis to Common Lisp: while many features have been added to Common Lisp as the result of a compromise or to satisfy one of the subgroups participating in the standardization process, a new feature is added to Scheme only if it is unanimously considered a Good Thing.
The AIS the Lisp language is a compiled dialect of Lisp, influenced by Scheme, with extensions for supporting the development of Agents. The AIS Lisp language is a high speed embedded programming language for the Agent Information Server database engine. Agents coded in AIS Lisp may be saved into and retrieved from AIS repositories as well as distributed across the Internet.
High volume data analysis agents (of the type popular in Agent Information Server applications) place a very high premium on fast execution speed. The high quantity of data and the complex nature of the more advanced analysis techniques, require the fastest possible agent execution speeds. As a programming language, Lisp has the reputation of delivering very fast development times plus very low engineering costs. We wanted Lisp's very fast development time; but, we also needed a Lisp which delivers ultra fast microchip-level execution speeds. AIS Lisp is a mixture of high level symbolic and list processing features together with microchip register to register operations for the fastest possible development and execution times.
Common Lisp and Scheme lambda values are function objects. In AIS Lisp, the lambda values are Agent objects. It is already the case that the Common Lisp let special form creates persistent variables associated with returned lambda objects, and it is already the case that the Scheme call-cc function creates persistent variables associated with returned lambda objects. In AIS Lisp, we have extended this trend to allow easy declaration of agent persistent variables (of both pvars and cvars scope) with the lambda, define, and defun notation. It also possible, in AIS Lisp, to easily declare microchip registers as variables. This allows ultra fast, register to register operations, with no further changes to the normal Lisp syntax.
The list of AIS syntactic enhancements to Lisp is actually rather modest. The new vars, pvars, cvars, assembler, and regs special forms support the easy declaration of temporary, persistent, additional persistent, and register variables within otherwise normal lambda notation. The new faces special forms supports the easy proclamation of the agent's goals, intentions, plans, and interface conventions within otherwise normal lambda notation. Reference to agent methods, and container object contents is made more convient with the addition to AIS Lisp of the standard dot operator and bracket operators now common in many other programming languages.
The lambda, defun, and defmacro special forms support the development of a user defined function library in AIS Lisp (for those applications where a functional programming paradigm is warranted). The defclass and defmethod special forms support the development of a user defined class library in AIS Lisp (for those applications where an object oriented paradigm is warranted). Finally, the defchild, defriend, and the deforphan special forms, support a wide range of possible agent to agent relationship for building complex agent communities (for those applications where an agent oriented paradigm is warranted).
The defvm special form supports the development of user defined virtual machines in AIS Lisp (for those applications where an alternative virtual machine is warranted).
The assembler virtual machine instruction special forms support the development of portable assembler programs in AIS Lisp. The addition of the register special forms, support the development of portable high volume data analysis algorithms which execute at microchip-level speeds across a wide range of host computers.
Finally, the regs special form supports the declaration of register variables, making it easy to specify microchip-level register to register operations within normal Lisp syntax. This makes AIS Lisp, in addition to its symbol and list processing features, a write-once-run-anywhere fast execution algorithmic language.