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GOFAI is an acronym for "Good Old-Fashioned Artificial Intelligence" invented by the philosopher
John Haugeland John Haugeland (; March 13, 1945 – June 23, 2010) was a professor of philosophy, specializing in the philosophy of mind, cognitive science, phenomenology, and Heidegger. He spent most of his career at the University of Pittsburgh, followed ...
in his 1985 book, ''Artificial Intelligence: The Very Idea''. Technically, GOFAI refers only to a restricted kind of symbolic AI, namely rule-based or logical agents. This approach was popular in the 1980s, especially as an approach to implementing expert systems, but symbolic AI has since been extended in many ways to better handle uncertain reasoning and more open-ended systems. Some of these extensions include probabilistic reasoning, non-monotonic reasoning,
multi-agent systems A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents.Hu, J.; Bhowmick, P.; Jang, I.; Arvin, F.; Lanzon, A.,A Decentralized Cluster Formation Containment Framework fo ...
, and neuro-symbolic systems. Significant contributions of symbolic AI, not encompassed by the GOFAI view, include search algorithms;
automated planning and scheduling Automation describes a wide range of technologies that reduce human intervention in processes, namely by predetermining decision criteria, subprocess relationships, and related actions, as well as embodying those predeterminations in machines ...
; constraint-based reasoning; the semantic web; ontologies; knowledge graphs; non-monotonic logic; circumscription;
automated theorem proving Automated theorem proving (also known as ATP or automated deduction) is a subfield of automated reasoning and mathematical logic dealing with proving mathematical theorems by computer programs. Automated reasoning over mathematical proof was a ...
; and
symbolic mathematics In mathematics and computer science, computer algebra, also called symbolic computation or algebraic computation, is a scientific area that refers to the study and development of algorithms and software for manipulating mathematical expressions ...
. For a more complete list, see the main article on symbolic AI.


Symbolic AI after GOFAI and confusions caused by viewing symbolic AI as only GOFAI

Although the term GOFAI encompasses only a small part of symbolic AI, prominent in the 1980s, contemporary critics of symbolic AI sometimes use GOFAI as a synonym for it. This conflation of terms can lead to conclusions that symbolic AI research ended in the 1980s and avoided machine learning. Since both conclusions are false and important to correct, we address them below.


The 1980s GOFAI version of symbolic AI characterized by production rules and expert systems

During the Second AI Summer, i.e., the expert systems boom of the 1980s, production-rule systems requiring knowledge engineering were used to implement expert systems. Knowledge engineering required working with subject matter experts to model task knowledge as rules. At the time, rules were hand-authored by knowledge engineers or the subject matter experts. GOFAI correctly describes this approach. Haugeland and Dreyfus also correctly pointed out various limitations, discussed in later sections. The Second AI Winter occurred after the expert systems and
Lisp Machines Lisp machines are general-purpose computers designed to efficiently run Lisp as their main software and programming language, usually via hardware support. They are an example of a high-level language computer architecture, and in a sense, t ...
markets collapsed. Expert systems did not handle uncertainty well, required more resources to build and maintain than expected, and proved brittle outside their intended domains due to a lack of common-sense reasoning capabilities. Language-specific Lisp machines could become easily replaced by newer workstations with similar performance, obviating their need.


Symbolic AI after GOFAI

Symbolic AI continued, albeit with reduced funding. It redirected focus to address limitations in handling uncertainty, using statistical AI; and to speed knowledge acquisition, with symbolic approaches to machine learning. Work in
semantic networks A semantic network, or frame network is a knowledge base that represents semantic relations between concepts in a network. This is often used as a form of knowledge representation. It is a directed or undirected graph consisting of vertices, ...
and knowledge representations led to formalizing ontologies with languages such as RDF and OWL, leading to large ontologies such as YAGO. However, the research and applications of symbolic AI since the Second AI Winter and outside of the production-rule approach to expert systems are less well known and now eclipsed by the media focus on deep learning since 2012. For example, research in symbolic AI machine learning includes
decision tree learning Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of ...
,
Mitchell Mitchell may refer to: People *Mitchell (surname) *Mitchell (given name) Places Australia * Mitchell, Australian Capital Territory, a light-industrial estate * Mitchell, New South Wales, a suburb of Bathurst * Mitchell, Northern Territ ...
's
version space Version space learning is a logical approach to machine learning, specifically binary classification. Version space learning algorithms search a predefined space of hypotheses, viewed as a set of logical sentences. Formally, the hypothesis spac ...
learning, Valiant's contributions to PAC learning,
statistical relational learning Statistical relational learning (SRL) is a subdiscipline of artificial intelligence and machine learning that is concerned with domain models that exhibit both uncertainty (which can be dealt with using statistical methods) and complex, relational ...
, inductive logic programming, and various interactive learning approaches to incorporate user advice, examples, and explanations as an integral part of the learning process.


Problems when current symbolic AI is viewed as GOFAI

Using GOFAI as a synonym for current symbolic AI leads to erroneous conclusions and confusion. Garcez and Lamb provide an example:
Turing award winner
Judea Pearl Judea Pearl (born September 4, 1936) is an Israeli-American computer scientist and philosopher, best known for championing the probabilistic approach to artificial intelligence and the development of Bayesian networks (see the article on belief ...
offers a critique of machine learning which, unfortunately, conflates the terms
machine learning Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine ...
and
deep learning Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. ...
. Similarly, when Geoffrey Hinton refers to symbolic AI, the connotation of the term tends to be that of expert systems dispossessed of any ability to learn. The use of the terminology is in need of clarification.
Machine learning Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine ...
is not confined to
association rule mining Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness.Pi ...
, c.f. the body of work on symbolic ML achine learningand relational learning (the differences to deep learning being the choice of representation, localist logical rather than distributed, and the non-use of gradient-based learning algorithms). Equally, symbolic AI is not just about production rules written by hand. A proper definition of AI concerns
knowledge representation and reasoning Knowledge representation and reasoning (KRR, KR&R, KR²) 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 medic ...
, autonomous
multi-agent systems A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents.Hu, J.; Bhowmick, P.; Jang, I.; Arvin, F.; Lanzon, A.,A Decentralized Cluster Formation Containment Framework fo ...
,
planning Planning is the process of thinking regarding the activities required to achieve a desired goal. Planning is based on foresight, the fundamental capacity for mental time travel. The evolution of forethought, the capacity to think ahead, is c ...
and
argumentation Argumentation theory, or argumentation, is the interdisciplinary study of how conclusions can be supported or undermined by premises through logical reasoning. With historical origins in logic, dialectic, and rhetoric, argumentation theory, incl ...
, as well as learning.
The key points above are that symbolic AI research has long since moved beyond GOFAI, research continues, and GOFAI no longer describes it. Further, there are symbolic learning approaches to machine learning, such as inductive logic programming and
statistical relational learning Statistical relational learning (SRL) is a subdiscipline of artificial intelligence and machine learning that is concerned with domain models that exhibit both uncertainty (which can be dealt with using statistical methods) and complex, relational ...
, i.e., it is not just the domain of
deep learning Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. ...
. Below, we return to the philosophical critiques leveled against GOFAI, the symbolic AI approach of the 1980s.


The GOFAI critique of rule-based agents

GOFAI, the rule-based approach of 1980s symbolic AI, was attacked by philosophers such as
Hubert Dreyfus Hubert Lederer Dreyfus (; October 15, 1929 – April 22, 2017) was an American philosopher and professor of philosophy at the University of California, Berkeley. His main interests included phenomenology, existentialism and the philosophy of ...
, his brother Stuart Dreyfus, and philosopher Kenneth Sayre. The essence of what they criticized was described earlier by computer scientist
Alan Turing Alan Mathison Turing (; 23 June 1912 – 7 June 1954) was an English mathematician, computer scientist, logician, cryptanalyst, philosopher, and theoretical biologist. Turing was highly influential in the development of theoretical ...
, in his 1950 paper
Computing Machinery and Intelligence "Computing Machinery and Intelligence" is a seminal paper written by Alan Turing on the topic of artificial intelligence. The paper, published in 1950 in ''Mind'', was the first to introduce his concept of what is now known as the Turing test to ...
, when he said that "human behavior is far too complex to be captured by any formal set of rules—humans must be using some informal guidelines that … could never be captured in a formal set of rules and thus could never be codified in a computer program." Turing called this "The Argument from Informality of Behaviour." Russell and Norvig, describe the GOFAI critique in ''Artificial Intelligence: A Modern Approach'':
The position they criticize came to be called "Good Old-Fashioned Al," or GOFAI, a term coined by Haugeland (1985). GOFAI is supposed to claim that all intelligent behavior can be captured by a system that reasons logically from a set of facts and rules describing the domain. It therefore corresponds to the simplest logical agent described in Chapter 7. Dreyfus is correct in saying that logical agents are vulnerable to the
qualification problem In philosophy and AI (especially, knowledge-based systems), the qualification problem is concerned with the impossibility of listing ''all'' the preconditions required for a real-world action to have its intended effect. It might be posed as ''h ...
. As we saw in Chapter 13, probabilistic reasoning systems are more appropriate for open-ended domains. The Dreyfus critique therefore is not addressed against computers per se, but rather against one particular way of programming them. It is reasonable to suppose, however, that a book called ''What First-Order Logical Rule-Based Systems Without Learning Can't Do'' might have had less impact.
In other words, GOFAI restricts its view of agents to those controlled by logical rules. In contrast to this view, symbolic AI also includes non-monotonic logic,
modal logic Modal logic is a collection of formal systems developed to represent statements about necessity and possibility. It plays a major role in philosophy of language, epistemology, metaphysics, and natural language semantics. Modal logics extend ot ...
, probabilistic logics,
multi-agent systems A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents.Hu, J.; Bhowmick, P.; Jang, I.; Arvin, F.; Lanzon, A.,A Decentralized Cluster Formation Containment Framework fo ...
, symbolic machine learning, and hybrid neuro-symbolic architectures. Symbolic machine learning, i.e., non-connectionist
machine learning Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine ...
specific to symbolic AI, includes inductive logic programming,
statistical relational learning Statistical relational learning (SRL) is a subdiscipline of artificial intelligence and machine learning that is concerned with domain models that exhibit both uncertainty (which can be dealt with using statistical methods) and complex, relational ...
, case-based learning, knowledge compilation (
chunking Chunking may mean: * Chunking (division), an approach for doing simple mathematical division sums, by repeated subtraction * Chunking (computational linguistics), a method for parsing natural language sentences into partial syntactic structures * ...
), macro-operator learning, learning from analogy, and interactive learning from human
advice Advice (noun) or advise (verb) may refer to: * Advice (opinion), an opinion or recommendation offered as a guide to action, conduct * Advice (constitutional law) a frequently binding instruction issued to a constitutional office-holder * Advice (p ...
, explanations, and exemplars.


The GOFAI critique of disembodied agents

Russell and Norvig do not reject all of Dreyfus’s arguments, they accept his strongest argument, one that applies to all disembodied AIs, whatever their approach:
One of Dreyfus's strongest arguments is for situated agents rather than disembodied logical inference engines. An agent whose understanding of "dog" comes only from a limited set of logical sentences such as "Dog(x) ⇒ Mammal(x)" is at a disadvantage compared to an agent that has watched dogs run, has played fetch with them, and has been licked by one. As philosopher
Andy Clark Andy Clark, (born 1957) is a British philosopher who is Professor of Cognitive Philosophy at the University of Sussex. Prior to this, he was at professor of philosophy and Chair in Logic and Metaphysics at the University of Edinburgh in ...
(1998) says, "Biological brains are first and foremost the control systems for biological bodies. Biological bodies move and act in rich real-world surroundings: According to
Clark Clark is an English language surname, ultimately derived from the Latin language, Latin with historical links to England, Scotland, and Ireland ''clericus'' meaning "scribe", "secretary" or a scholar within a religious order, referring to someone ...
, we are "good at frisbee, bad at logic." The
embodied cognition Embodied cognition is the theory that many features of cognition, whether human or otherwise, are shaped by aspects of an organism's entire body. Sensory and motor systems are seen as fundamentally integrated with cognitive processing. The cognit ...
approach claims that it makes no sense to consider the brain separately: cognition takes place within a body, which is embedded in an environment. We need to study the system as a whole; the brain's functioning exploits regularities in its environment, including the rest of its body. Under the
embodied cognition Embodied cognition is the theory that many features of cognition, whether human or otherwise, are shaped by aspects of an organism's entire body. Sensory and motor systems are seen as fundamentally integrated with cognitive processing. The cognit ...
approach, robotics, vision, and other sensors become central, not peripheral.


Citations


References

* * * * * * * * * * * * * * {{Cite journal, doi = 10.1093/mind/LIX.236.433, issn = 0026-4423, volume = LIX, issue = 236, pages = 433–460, last = Turing, first = A. M., title = I.—Computing Machinery and Intelligence, journal = Mind, accessdate = 2022-09-14, date = 1950, url = https://doi.org/10.1093/mind/LIX.236.433 zh-yue:GOFAI Terms in science and technology Artificial intelligence