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 Probabilistic logic (also probability logic and probabilistic reasoning) involves the use of probability and logic to deal with uncertain situations. Probabilistic logic extends traditional logic truth tables with probabilistic expressions. A diffic ...
,
non-monotonic reasoning
A non-monotonic logic is a formal logic whose conclusion relation is not monotonic. In other words, non-monotonic logics are devised to capture and represent defeasible inferences (cf. defeasible reasoning), i.e., a kind of inference in which re ...
,
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
In computer science, a search algorithm is an algorithm designed to solve a search problem. Search algorithms work to retrieve information stored within particular data structure, or calculated in the search space of a problem domain, with eit ...
;
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
In computer science and information science, an ontology encompasses a representation, formal naming, and definition of the categories, properties, and relations between the concepts, data, and entities that substantiate one, many, or all domains ...
;
knowledge graphs;
non-monotonic logic
A non-monotonic logic is a formal logic whose conclusion relation is not monotonic. In other words, non-monotonic logics are devised to capture and represent defeasible inferences (cf. defeasible reasoning), i.e., a kind of inference in which re ...
;
circumscription
Circumscription may refer to:
*Circumscribed circle
* Circumscription (logic)
*Circumscription (taxonomy)
*Circumscription theory, a theory about the origins of the political state in the history of human evolution proposed by the American anthrop ...
;
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 maj ...
; 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
Knowledge engineering (KE) refers to all technical, scientific and social aspects involved in building, maintaining and using knowledge-based systems.
Background Expert systems
One of the first examples of an expert system was MYCIN, an appl ...
were used to implement
expert systems
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†...
. 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
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†...
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, the ...
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
In computer science and information science, an ontology encompasses a representation, formal naming, and definition of the categories, properties, and relations between the concepts, data, and entities that substantiate one, many, or all domains ...
with languages such as
RDF and
OWL
Owls are birds from the order Strigiformes (), which includes over 200 species of mostly solitary and nocturnal birds of prey typified by an upright stance, a large, broad head, binocular vision, binaural hearing, sharp talons, and feathers a ...
, 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 ob ...
,
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 Territo ...
's
version space
Version space learning is a Symbolic artificial intelligence, logical approach to machine learning, specifically binary classification. Version space learning algorithms search a predefined space of hypothesis, hypotheses, viewed as a set of Senten ...
learning,
Valiant's contributions to
PAC learning
In computational learning theory, probably approximately correct (PAC) learning is a framework for mathematical analysis of machine learning. It was proposed in 1984 by Leslie Valiant.L. Valiant. A theory of the learnable.' Communications of the A ...
,
statistical relational learning,
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 beli ...
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.
De ...
. Similarly, when Geoffrey Hinton
Geoffrey Everest Hinton One or more of the preceding sentences incorporates text from the royalsociety.org website where: (born 6 December 1947) is a British-Canadian cognitive psychologist and computer scientist, most noted for his work on a ...
refers to symbolic AI, the connotation of the term tends to be that of expert systems
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†...
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, 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 Computer-aided diag ...
, 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 consi ...
and argumentation, 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, 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.
De ...
. 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 bo ...
, his brother
Stuart Dreyfus
A native of Terre Haute, Indiana, Stuart E. Dreyfus is professor emeritus at University of California, Berkeley in the Industrial Engineering and Operations Research Department. While at the Rand Corporation he was a programmer of the JOHNNIAC com ...
, 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 com ...
, 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 ''ho ...
. As we saw in Chapter 13, probabilistic reasoning Probabilistic logic (also probability logic and probabilistic reasoning) involves the use of probability and logic to deal with uncertain situations. Probabilistic logic extends traditional logic truth tables with probabilistic expressions. A diffic ...
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
A non-monotonic logic is a formal logic whose conclusion relation is not monotonic. In other words, non-monotonic logics are devised to capture and represent defeasible inferences (cf. defeasible reasoning), i.e., a kind of inference in which re ...
,
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 other ...
,
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,
case-based learning, knowledge compilation (
chunking), 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 Scotl ...
(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 with historical links to England, Scotland, and Ireland ''clericus'' meaning "scribe", "secretary" or a scholar within a religious order, referring to someone who was educate ...
, 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