A short history of symbolic AI to the present day follows below. Time periods and titles are drawn from Henry Kautz's 2020 AAAI Robert S. Engelmore Memorial Lecture and the longer Wikipedia article on the
History of AI, with dates and titles differing slightly for increased clarity.
The first AI summer: irrational exuberance, 1948–1966 Success at early attempts in AI occurred in three main areas: artificial neural networks, knowledge representation, and heuristic search, contributing to high expectations. This section summarizes Kautz's reprise of early AI history.
Approaches inspired by human or animal cognition or behavior Cybernetic approaches attempted to replicate the feedback loops between animals and their environments. A robotic turtle, with sensors, motors for driving and steering, and seven vacuum tubes for control, based on a preprogrammed neural net, was built as early as 1948. This work can be seen as an early precursor to later work in neural networks, reinforcement learning, and situated robotics. An important early symbolic AI program was the
Logic theorist, written by
Allen Newell,
Herbert Simon and
Cliff Shaw in 1955–56, as it was able to prove 38 elementary theorems from Whitehead and Russell's
Principia Mathematica. Newell, Simon, and Shaw later generalized this work to create a domain-independent problem solver,
GPS (General Problem Solver). GPS solved problems represented with formal operators via state-space search using
means-ends analysis. During the 1960s, symbolic approaches achieved great success at simulating intelligent behavior in structured environments such as game-playing, symbolic mathematics, and theorem-proving. AI research was concentrated in four institutions in the 1960s:
Carnegie Mellon University,
Stanford,
MIT and (later)
University of Edinburgh. Each one developed its own style of research. Earlier approaches based on
cybernetics or
artificial neural networks were abandoned or pushed into the background.
Herbert Simon and
Allen Newell studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as
cognitive science,
operations research and
management science. Their research team used the results of
psychological experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the
Soar architecture in the middle 1980s.
Heuristic search In addition to the highly specialized domain-specific kinds of knowledge that we will see later used in expert systems, early symbolic AI researchers discovered another more general application of knowledge. These were called heuristics, rules of thumb that guide a search in promising directions: "How can non-enumerative search be practical when the underlying problem is exponentially hard? The approach advocated by Simon and Newell is to employ
heuristics: fast algorithms that may fail on some inputs or output suboptimal solutions." Another important advance was to find a way to apply these heuristics that guarantees a solution will be found, if there is one, not withstanding the occasional fallibility of heuristics: "The
A* algorithm provided a general frame for complete and optimal heuristically guided search. A* is used as a subroutine within practically every AI algorithm today but is still no magic bullet; its guarantee of completeness is bought at the cost of worst-case exponential time.
Early work on knowledge representation and reasoning Early work covered both applications of formal reasoning emphasizing
first-order logic, along with attempts to handle
common-sense reasoning in a less formal manner.
Modeling formal reasoning with logic: the "neats" Unlike Simon and Newell,
John McCarthy felt that machines did not need to simulate the exact mechanisms of human thought, but could instead try to find the essence of abstract reasoning and problem-solving with logic, regardless of whether people used the same algorithms. His laboratory at
Stanford (
SAIL) focused on using formal
logic to solve a wide variety of problems, including
knowledge representation, planning and
learning. Logic was also the focus of the work at the
University of Edinburgh and elsewhere in Europe which led to the development of the programming language
Prolog and the science of logic programming.
Modeling implicit common-sense knowledge with frames and scripts: the "scruffies" Researchers at
MIT (such as
Marvin Minsky and
Seymour Papert) found that solving difficult problems in
vision and
natural language processing required ad hoc solutions—they argued that no simple and general principle (like
logic) would capture all the aspects of intelligent behavior.
Roger Schank described their "anti-logic" approaches as "
scruffy" (as opposed to the "
neat" paradigms at
CMU and Stanford).
Commonsense knowledge bases (such as
Doug Lenat's
Cyc) are an example of "scruffy" AI, since they must be built by hand, one complicated concept at a time.
The first AI winter: crushed dreams, 1967–1977 The first AI winter was a shock:
The second AI summer: knowledge is power, 1978–1987 Knowledge-based systems As limitations with weak, domain-independent methods became more and more apparent, researchers from all three traditions began to build
knowledge into AI applications. The knowledge revolution was driven by the realization that knowledge underlies high-performance, domain-specific AI applications.
Edward Feigenbaum said: • "In the knowledge lies the power." to describe that high performance in a specific domain requires both general and highly domain-specific knowledge. Ed Feigenbaum and Doug Lenat called this The Knowledge Principle: {{Blockquote
Success with expert systems This "knowledge revolution" led to the development and deployment of
expert systems (introduced by
Edward Feigenbaum), the first commercially successful form of AI software. Key expert systems were: •
DENDRAL, which found the structure of organic molecules from their chemical formula and mass spectrometer readings. •
MYCIN, which diagnosed bacteremia – and suggested further lab tests, when necessary – by interpreting lab results, patient history, and doctor observations. "With about 450 rules, MYCIN was able to perform as well as some experts, and considerably better than junior doctors." •
INTERNIST and
CADUCEUS which tackled internal medicine diagnosis. Internist attempted to capture the expertise of the chairman of internal medicine at the
University of Pittsburgh School of Medicine while CADUCEUS could eventually diagnose up to 1000 different diseases. • GUIDON, which showed how a knowledge base built for expert problem solving could be repurposed for teaching. •
XCON, to configure VAX computers, a then laborious process that could take up to 90 days. XCON reduced the time to about 90 minutes.
DENDRAL is considered the first expert system that relied on knowledge-intensive problem-solving. It is described below, by
Ed Feigenbaum, from a
Communications of the ACM interview, Interview with Ed Feigenbaum: The other expert systems mentioned above came after DENDRAL. MYCIN exemplifies the classic expert system architecture of a knowledge-base of rules coupled to a symbolic reasoning mechanism, including the use of certainty factors to handle uncertainty. GUIDON shows how an explicit knowledge base can be repurposed for a second application, tutoring, and is an example of an
intelligent tutoring system, a particular kind of knowledge-based application. Clancey showed that it was not sufficient simply to use
MYCIN's rules for instruction, but that he also needed to add rules for dialogue management and student modeling. XCON is significant because of the millions of dollars it saved
DEC, which triggered the expert system boom where most all major corporations in the US had expert systems groups, to capture corporate expertise, preserve it, and automate it: Chess expert knowledge was encoded in
Deep Blue. In 1996, this allowed
IBM's
Deep Blue, with the help of symbolic AI, to win in a game of chess against the world champion at that time,
Garry Kasparov.
Architecture of knowledge-based and expert systems A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules for problem-solving. The simplest approach for an expert system knowledge base is simply a collection or network of
production rules. Production rules connect symbols in a relationship similar to an If-Then statement. The expert system processes the rules to make deductions and to determine what additional information it needs, i.e. what questions to ask, using human-readable symbols. For example,
OPS5,
CLIPS and their successors
Jess and
Drools operate in this fashion. Expert systems can operate in either a
forward chaining – from evidence to conclusions – or
backward chaining – from goals to needed data and prerequisites – manner. More advanced knowledge-based systems, such as
Soar can also perform meta-level reasoning, that is reasoning about their own reasoning in terms of deciding how to solve problems and monitoring the success of problem-solving strategies.
Blackboard systems are a second kind of
knowledge-based or
expert system architecture. They model a community of experts incrementally contributing, where they can, to solve a problem. The problem is represented in multiple levels of abstraction or alternate views. The experts (knowledge sources) volunteer their services whenever they recognize they can contribute. Potential problem-solving actions are represented on an agenda that is updated as the problem situation changes. A controller decides how useful each contribution is, and who should make the next problem-solving action. One example, the BB1 blackboard architecture was originally inspired by studies of how humans plan to perform multiple tasks in a trip. An innovation of BB1 was to apply the same blackboard model to solving its control problem, i.e., its controller performed meta-level reasoning with knowledge sources that monitored how well a plan or the problem-solving was proceeding and could switch from one strategy to another as conditions – such as goals or times – changed. BB1 has been applied in multiple domains: construction site planning, intelligent tutoring systems, and real-time patient monitoring.
The second AI winter, 1988–1993 At the height of the AI boom, companies such as
Symbolics,
LMI, and
Texas Instruments were selling
LISP machines specifically targeted to accelerate the development of AI applications and research. In addition, several artificial intelligence companies, such as Teknowledge and
Inference Corporation, were selling expert system shells, training, and consulting to corporations. Unfortunately, the AI boom did not last and Kautz best describes the second AI winter that followed:
Adding in more rigorous foundations, 1993–2011 Uncertain reasoning Both statistical approaches and extensions to logic were tried. One statistical approach,
hidden Markov models, had already been popularized in the 1980s for speech recognition work. Subsequently, in 1988,
Judea Pearl popularized the use of
Bayesian Networks as a sound but efficient way of handling uncertain reasoning with his publication of the book Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. and Bayesian approaches were applied successfully in expert systems. Even later, in the 1990s, statistical relational learning, an approach that combines probability with logical formulas, allowed probability to be combined with first-order logic, e.g., with either
Markov Logic Networks or
Probabilistic Soft Logic. Other, non-probabilistic extensions to first-order logic to support were also tried. For example,
non-monotonic reasoning could be used with
truth maintenance systems. A
truth maintenance system tracked assumptions and justifications for all inferences. It allowed inferences to be withdrawn when assumptions were found out to be incorrect or a contradiction was derived. Explanations could be provided for an inference by
explaining which rules were applied to create it and then continuing through underlying inferences and rules all the way back to root assumptions.
Lotfi Zadeh had introduced a different kind of extension to handle the representation of vagueness. For example, in deciding how "heavy" or "tall" a man is, there is frequently no clear "yes" or "no" answer, and a predicate for heavy or tall would instead return values between 0 and 1. Those values represented to what degree the predicates were true. His
fuzzy logic further provided a means for propagating combinations of these values through logical formulas.
Machine learning Symbolic machine learning approaches were investigated to address the
knowledge acquisition bottleneck. One of the earliest is
Meta-DENDRAL. Meta-DENDRAL used a generate-and-test technique to generate plausible rule hypotheses to test against spectra. Domain and task knowledge reduced the number of candidates tested to a manageable size.
Feigenbaum described Meta-DENDRAL as In contrast to the knowledge-intensive approach of Meta-DENDRAL,
Ross Quinlan invented a domain-independent approach to statistical classification,
decision tree learning, starting first with
ID3 and then later extending its capabilities to
C4.5. The decision trees created are
glass box, interpretable classifiers, with human-interpretable classification rules. Advances were made in understanding machine learning theory, too.
Tom Mitchell introduced
version space learning which describes learning as a search through a space of hypotheses, with upper, more general, and lower, more specific, boundaries encompassing all viable hypotheses consistent with the examples seen so far. More formally,
Valiant introduced
Probably Approximately Correct Learning (PAC Learning), a framework for the mathematical analysis of machine learning. Symbolic machine learning encompassed more than learning by example. E.g.,
John Anderson provided a
cognitive model of human learning where skill practice results in a compilation of rules from a declarative format to a procedural format with his
ACT-R cognitive architecture. For example, a student might learn to apply "Supplementary angles are two angles whose measures sum 180 degrees" as several different procedural rules. E.g., one rule might say that if X and Y are supplementary and you know X, then Y will be 180 - X. He called his approach "knowledge compilation".
ACT-R has been used successfully to model aspects of human cognition, such as learning and retention. ACT-R is also used in
intelligent tutoring systems, called
cognitive tutors, to successfully teach geometry, computer programming, and algebra to school children. Inductive logic programming was another approach to learning that allowed logic programs to be synthesized from input-output examples. E.g.,
Ehud Shapiro's MIS (Model Inference System) could synthesize Prolog programs from examples.
John R. Koza applied
genetic algorithms to
program synthesis to create
genetic programming, which he used to synthesize LISP programs. Finally,
Zohar Manna and
Richard Waldinger provided a more general approach to
program synthesis that synthesizes a
functional program in the course of proving its specifications to be correct. As an alternative to logic,
Roger Schank introduced case-based reasoning (CBR). The CBR approach outlined in his book, Dynamic Memory, focuses first on remembering key problem-solving cases for future use and generalizing them where appropriate. When faced with a new problem, CBR retrieves the most similar previous case and adapts it to the specifics of the current problem. Another alternative to logic,
genetic algorithms and
genetic programming are based on an evolutionary model of learning, where sets of rules are encoded into populations, the rules govern the behavior of individuals, and selection of the fittest prunes out sets of unsuitable rules over many generations. Symbolic machine learning was applied to learning concepts, rules, heuristics, and problem-solving. Approaches, other than those above, include: • Learning from instruction or advice—i.e., taking human instruction, posed as advice, and determining how to operationalize it in specific situations. For example, in a game of Hearts, learning
exactly how to play a hand to "avoid taking points." • Learning from exemplars—improving performance by accepting subject-matter expert (SME) feedback during training. When problem-solving fails, querying the expert to either learn a new exemplar for problem-solving or to learn a new explanation as to exactly why one exemplar is more relevant than another. For example, the program Protos learned to diagnose tinnitus cases by interacting with an audiologist. • Learning by analogy—constructing problem solutions based on similar problems seen in the past, and then modifying their solutions to fit a new situation or domain. • Apprentice learning systems—learning novel solutions to problems by observing human problem-solving. Domain knowledge explains why novel solutions are correct and how the solution can be generalized. LEAP learned how to design VLSI circuits by observing human designers. • Learning by discovery—i.e., creating tasks to carry out experiments and then learning from the results.
Doug Lenat's
Eurisko, for example, learned heuristics to beat human players at the
Traveller role-playing game for two years in a row. • Learning macro-operators—i.e., searching for useful macro-operators to be learned from sequences of basic problem-solving actions. Good macro-operators simplify problem-solving by allowing problems to be solved at a more abstract level.
Deep learning and neuro-symbolic AI 2011–now With the rise of deep learning, the symbolic AI approach has been compared to deep learning as complementary "...with parallels having been drawn many times by AI researchers between
Kahneman's research on human reasoning and decision making – reflected in his book
Thinking, Fast and Slow – and the so-called "AI systems 1 and 2", which would in principle be modelled by deep learning and symbolic reasoning, respectively." In this view, symbolic reasoning is more apt for deliberative reasoning, planning, and explanation while deep learning is more apt for fast pattern recognition in perceptual applications with noisy data.
Neuro-symbolic AI: integrating neural and symbolic approaches Neuro-symbolic AI attempts to integrate neural and symbolic architectures in a manner that addresses strengths and weaknesses of each, in a complementary fashion, in order to support robust AI capable of reasoning, learning, and cognitive modeling. As argued by
Valiant and many others, the effective construction of rich computational
cognitive models demands the combination of sound symbolic reasoning and efficient (machine) learning models.
Gary Marcus, similarly, argues that: "We cannot construct rich cognitive models in an adequate, automated way without the triumvirate of hybrid architecture, rich prior knowledge, and sophisticated techniques for reasoning.", and in particular: "To build a robust, knowledge-driven approach to AI we must have the machinery of symbol-manipulation in our toolkit. Too much of useful knowledge is abstract to make do without tools that represent and manipulate abstraction, and to date, the only machinery that we know of that can manipulate such abstract knowledge reliably is the apparatus of symbol manipulation."
Henry Kautz,
Francesca Rossi, and
Bart Selman have also argued for a synthesis. Their arguments are based on a need to address the two kinds of thinking discussed in
Daniel Kahneman's book,
Thinking, Fast and Slow. Kahneman describes human thinking as having two components,
System 1 and System 2. System 1 is fast, automatic, intuitive and unconscious. System 2 is slower, step-by-step, and explicit. System 1 is the kind used for pattern recognition while System 2 is far better suited for planning, deduction, and deliberative thinking. In this view, deep learning best models the first kind of thinking while symbolic reasoning best models the second kind and both are needed.
Garcez and Lamb describe research in this area as being ongoing for at least the past twenty years, dating from their 2002 book on neurosymbolic learning systems. A series of workshops on neuro-symbolic reasoning has been held every year since 2005. In their 2015 paper, Neural-Symbolic Learning and Reasoning: Contributions and Challenges, Garcez et al. argue that: Approaches for integration are varied.
Henry Kautz's taxonomy of neuro-symbolic architectures, along with some examples, follows: • Symbolic Neural symbolic—is the current approach of many neural models in natural language processing, where words or subword tokens are both the ultimate input and output of large language models. Examples include
BERT, RoBERTa, and
GPT-3. • Symbolic[Neural]—is exemplified by
AlphaGo, where symbolic techniques are used to call neural techniques. In this case the symbolic approach is
Monte Carlo tree search and the neural techniques learn how to evaluate game positions. • Neural|Symbolic—uses a neural architecture to interpret perceptual data as symbols and relationships that are then reasoned about symbolically. • Neural:Symbolic → Neural—relies on symbolic reasoning to generate or label training data that is subsequently learned by a deep learning model, e.g., to train a neural model for symbolic computation by using a
Macsyma-like symbolic mathematics system to create or label examples. • Neural_{Symbolic}—uses a neural net that is generated from symbolic rules. An example is the Neural Theorem Prover, which constructs a neural network from an
AND–OR proof tree generated from knowledge base rules and terms. Logic Tensor Networks also fall into this category. • Neural[Symbolic]—allows a neural model to directly call a symbolic reasoning engine, e.g., to perform an action or evaluate a state. Many key research questions remain, such as: • What is the best way to integrate neural and symbolic architectures? • How should symbolic structures be represented within neural networks and extracted from them? • How should common-sense knowledge be learned and reasoned about? • How can abstract knowledge that is hard to encode logically be handled? == Techniques and contributions ==