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Symbolic Artificial Intelligence
In artificial intelligence, symbolic synthetic intelligence (likewise referred to as classical expert system or logic-based expert system) [1] [2] is the term for the collection of all approaches in expert system research that are based upon high-level symbolic (human-readable) representations of problems, logic and search. [3] Symbolic AI used tools such as reasoning programming, production guidelines, semantic internet and frames, and it developed applications such as knowledge-based systems (in particular, expert systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm resulted in seminal concepts in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and restrictions of formal knowledge and thinking systems.
Symbolic AI was the dominant paradigm of AI research study from the mid-1950s until the mid-1990s. [4] Researchers in the 1960s and the 1970s were encouraged that symbolic methods would eventually be successful in developing a device with artificial basic intelligence and considered this the ultimate objective of their field. [citation required] An early boom, with early successes such as the Logic Theorist and Samuel’s Checkers Playing Program, caused impractical expectations and pledges and was followed by the very first AI Winter as moneying dried up. [5] [6] A 2nd boom (1969-1986) accompanied the rise of professional systems, their pledge of recording corporate knowledge, and a passionate business embrace. [7] [8] That boom, and some early successes, e.g., with XCON at DEC, was followed once again by later on frustration. [8] Problems with problems in understanding acquisition, preserving big understanding bases, and brittleness in dealing with out-of-domain issues arose. Another, 2nd, AI Winter (1988-2011) followed. [9] Subsequently, AI scientists concentrated on dealing with hidden issues in handling uncertainty and in understanding acquisition. [10] Uncertainty was attended to with approaches such as covert Markov designs, Bayesian thinking, and statistical relational knowing. [11] [12] Symbolic machine discovering dealt with the knowledge acquisition problem with contributions consisting of Version Space, Valiant’s PAC knowing, Quinlan’s ID3 decision-tree knowing, case-based knowing, and inductive reasoning programs to discover relations. [13]
Neural networks, a subsymbolic approach, had been pursued from early days and reemerged highly in 2012. Early examples are Rosenblatt’s perceptron learning work, the backpropagation work of Rumelhart, Hinton and Williams, [14] and operate in convolutional neural networks by LeCun et al. in 1989. [15] However, neural networks were not considered as successful till about 2012: “Until Big Data became commonplace, the basic consensus in the Al neighborhood was that the so-called neural-network technique was helpless. Systems simply didn’t work that well, compared to other methods. … A transformation came in 2012, when a number of people, consisting of a team of scientists dealing with Hinton, exercised a method to use the power of GPUs to enormously increase the power of neural networks.” [16] Over the next numerous years, deep knowing had incredible success in dealing with vision, speech recognition, speech synthesis, image generation, and machine translation. However, given that 2020, as intrinsic problems with bias, description, comprehensibility, and robustness ended up being more obvious with deep learning methods; an increasing variety of AI researchers have actually called for integrating the very best of both the symbolic and neural network techniques [17] [18] and dealing with locations that both techniques have problem with, such as common-sense thinking. [16]
A brief 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 [19] and the longer Wikipedia article on the History of AI, with dates and titles varying a little for increased clearness.
The first AI summer: unreasonable vitality, 1948-1966
Success at early efforts in AI took place in three main areas: synthetic neural networks, understanding representation, and heuristic search, adding to high expectations. This section sums up Kautz’s reprise of early AI history.
Approaches motivated by human or animal cognition or behavior
Cybernetic approaches attempted to replicate the feedback loops in between animals and their environments. A robotic turtle, with sensors, motors for driving and steering, and 7 vacuum tubes for control, based on a preprogrammed neural net, was constructed as early as 1948. This work can be seen as an early precursor to later work in neural networks, support knowing, and situated robotics. [20]
An important early symbolic AI program was the Logic theorist, composed by Allen Newell, Herbert Simon and Cliff Shaw in 1955-56, as it was able to prove 38 primary theorems from Whitehead and Russell’s Principia Mathematica. Newell, Simon, and Shaw later on generalized this work to produce a domain-independent problem solver, GPS (General Problem Solver). GPS resolved problems represented with formal operators by means of state-space search utilizing means-ends analysis. [21]
During the 1960s, symbolic techniques accomplished terrific success at simulating intelligent habits in structured environments such as game-playing, symbolic mathematics, and theorem-proving. AI research study was concentrated in 4 organizations in the 1960s: Carnegie Mellon University, Stanford, MIT and (later on) University of Edinburgh. Every one developed its own style of research. Earlier approaches based on cybernetics or synthetic neural networks were deserted or pushed into the background.
Herbert Simon and Allen Newell studied human problem-solving abilities and attempted to formalize them, and their work laid the foundations of the field of expert system, as well as cognitive science, operations research and management science. Their research study team utilized the results of mental experiments to develop programs that simulated the methods that people used to resolve problems. [22] [23] This tradition, focused at Carnegie Mellon University would ultimately culminate in the advancement of the Soar architecture in the center 1980s. [24] [25]
Heuristic search
In addition to the highly specialized domain-specific type of knowledge that we will see later on utilized in specialist systems, early symbolic AI researchers discovered another more general application of knowledge. These were called heuristics, guidelines that direct a search in promising directions: “How can non-enumerative search be useful when the underlying problem is tremendously tough? The approach promoted by Simon and Newell is to utilize heuristics: quick algorithms that may fail on some inputs or output suboptimal services.” [26] Another important advance was to discover a method to apply these heuristics that ensures a solution will be found, if there is one, not standing up to the occasional fallibility of heuristics: “The A * algorithm provided a basic frame for total and optimum heuristically guided search. A * is utilized as a subroutine within virtually every AI algorithm today but is still no magic bullet; its guarantee of completeness is bought at the expense of worst-case rapid time. [26]
Early work on understanding representation and thinking
Early work covered both applications of formal reasoning highlighting first-order logic, in addition to efforts to deal with sensible thinking in a less formal way.
Modeling formal thinking with logic: the “neats”
Unlike Simon and Newell, John McCarthy felt that machines did not need to imitate the specific systems of human idea, but could rather look for the essence of abstract thinking and problem-solving with logic, [27] despite whether individuals used the same algorithms. [a] His laboratory at Stanford (SAIL) concentrated on using formal logic to resolve a wide array of problems, consisting of knowledge representation, planning and learning. [31] Logic was also the focus of the work at the University of Edinburgh and somewhere else in Europe which caused the advancement of the programs language Prolog and the science of reasoning programs. [32] [33]
Modeling implicit sensible knowledge with frames and scripts: the “scruffies”
Researchers at MIT (such as Marvin Minsky and Seymour Papert) [34] [35] [6] discovered that resolving tough problems in vision and natural language processing needed advertisement hoc solutions-they argued that no simple and general concept (like reasoning) would catch all the elements of intelligent behavior. Roger Schank described their “anti-logic” techniques as “shabby” (as opposed to the “cool” paradigms at CMU and Stanford). [36] [37] Commonsense knowledge bases (such as Doug Lenat’s Cyc) are an example of “shabby” AI, because they must be built by hand, one complicated concept at a time. [38] [39] [40]
The first AI winter: crushed dreams, 1967-1977
The very first AI winter was a shock:
During the first AI summer, many individuals believed that machine intelligence could be accomplished in just a couple of years. The Defense Advance Research Projects Agency (DARPA) introduced programs to support AI research to utilize AI to solve issues of nationwide security; in specific, to automate the translation of Russian to English for intelligence operations and to create self-governing tanks for the battleground. Researchers had begun to understand that attaining AI was going to be much harder than was expected a decade earlier, however a mix of hubris and disingenuousness led numerous university and think-tank researchers to accept financing with pledges of deliverables that they should have understood they could not fulfill. By the mid-1960s neither beneficial natural language translation systems nor self-governing tanks had actually been developed, and a significant backlash set in. New DARPA management canceled existing AI financing programs.
Beyond the United States, the most fertile ground for AI research study was the UK. The AI winter season in the United Kingdom was stimulated on not so much by dissatisfied military leaders as by rival academics who saw AI scientists as charlatans and a drain on research financing. A professor of used mathematics, Sir James Lighthill, was commissioned by Parliament to evaluate the state of AI research in the nation. The report stated that all of the problems being worked on in AI would be much better handled by scientists from other disciplines-such as used mathematics. The report also declared that AI successes on toy issues could never ever scale to real-world applications due to combinatorial explosion. [41]
The 2nd AI summertime: knowledge is power, 1978-1987
Knowledge-based systems
As constraints with weak, domain-independent techniques ended up being more and more apparent, [42] scientists from all three customs started to develop understanding into AI applications. [43] [7] The knowledge transformation was driven by the realization that knowledge underlies high-performance, domain-specific AI applications.
Edward Feigenbaum said:
– “In the knowledge lies the power.” [44]
to explain that high performance in a particular domain requires both general and extremely domain-specific understanding. Ed Feigenbaum and Doug Lenat called this The Knowledge Principle:
( 1) The Knowledge Principle: if a program is to carry out a complex task well, it needs to understand a fantastic deal about the world in which it runs.
( 2) A plausible extension of that principle, called the Breadth Hypothesis: there are two extra abilities necessary for smart habits in unanticipated circumstances: drawing on increasingly basic understanding, and analogizing to particular but distant knowledge. [45]
Success with professional systems
This “understanding revolution” caused the development and deployment of professional systems (introduced by Edward Feigenbaum), the first commercially successful type of AI software. [46] [47] [48]
Key specialist systems were:
DENDRAL, which discovered the structure of organic particles from their chemical formula and mass spectrometer readings.
MYCIN, which identified bacteremia – and suggested additional laboratory tests, when essential – by translating laboratory results, patient history, and doctor observations. “With about 450 guidelines, MYCIN had the ability to perform as well as some professionals, and substantially better than junior medical professionals.” [49] INTERNIST and CADUCEUS which dealt with internal medication medical diagnosis. Internist tried to record the knowledge of the chairman of internal medicine at the University of Pittsburgh School of Medicine while CADUCEUS could eventually identify as much as 1000 various diseases.
– GUIDON, which revealed how an understanding base constructed for expert issue resolving could be repurposed for mentor. [50] XCON, to configure VAX computer systems, a then tiresome procedure that might use up to 90 days. XCON decreased the time to about 90 minutes. [9]
DENDRAL is thought about the first professional system that count on knowledge-intensive problem-solving. It is described below, by Ed Feigenbaum, from a Communications of the ACM interview, Interview with Ed Feigenbaum:
One of individuals at Stanford thinking about computer-based designs of mind was Joshua Lederberg, the 1958 Nobel Prize winner in genes. When I informed him I desired an induction “sandbox”, he said, “I have just the one for you.” His lab was doing mass spectrometry of amino acids. The concern was: how do you go from looking at the spectrum of an amino acid to the chemical structure of the amino acid? That’s how we started the DENDRAL Project: I was proficient at heuristic search methods, and he had an algorithm that was proficient at creating the chemical issue space.
We did not have a grand vision. We worked bottom up. Our chemist was Carl Djerassi, innovator of the chemical behind the birth control pill, and also among the world’s most respected mass spectrometrists. Carl and his postdocs were world-class experts in mass spectrometry. We began to include to their knowledge, developing knowledge of engineering as we went along. These experiments amounted to titrating DENDRAL more and more knowledge. The more you did that, the smarter the program became. We had great results.
The generalization was: in the understanding lies the power. That was the big idea. In my profession that is the huge, “Ah ha!,” and it wasn’t the way AI was being done previously. Sounds easy, but it’s probably AI‘s most powerful generalization. [51]
The other specialist systems discussed above came after DENDRAL. MYCIN exemplifies the timeless specialist system architecture of a knowledge-base of guidelines paired to a symbolic reasoning mechanism, including using certainty aspects to deal with unpredictability. GUIDON demonstrates how a specific understanding base can be repurposed for a second application, tutoring, and is an example of a smart tutoring system, a specific type of knowledge-based application. Clancey revealed that it was not enough merely to utilize MYCIN’s rules for direction, but that he also required to add guidelines for dialogue management and trainee modeling. [50] XCON is significant because of the countless dollars it conserved DEC, which triggered the professional system boom where most all significant corporations in the US had skilled systems groups, to capture business knowledge, preserve it, and automate it:
By 1988, DEC’s AI group had 40 expert systems released, with more en route. DuPont had 100 in usage and 500 in advancement. Nearly every significant U.S. corporation had its own Al group and was either utilizing or investigating expert systems. [49]
Chess expert knowledge was encoded in Deep Blue. In 1996, this permitted IBM’s Deep Blue, with the assistance of symbolic AI, to win in a video game of chess against the world champ at that time, Garry Kasparov. [52]
Architecture of knowledge-based and professional systems
A key element of the system architecture for all specialist systems is the knowledge base, which stores facts and guidelines for analytical. [53] The easiest method for a skilled system understanding base is just a collection or network of production guidelines. Production guidelines link symbols in a relationship similar to an If-Then statement. The expert system processes the guidelines to make deductions and to determine what additional info it needs, i.e. what questions to ask, using human-readable signs. For instance, OPS5, CLIPS and their successors Jess and Drools run in this fashion.
Expert systems can operate in either a forward chaining – from proof to conclusions – or backward chaining – from goals to needed information and requirements – way. Advanced knowledge-based systems, such as Soar can likewise perform meta-level thinking, that is reasoning about their own reasoning in terms of deciding how to fix issues and keeping track of the success of analytical strategies.
Blackboard systems are a second kind of knowledge-based or skilled system architecture. They design a community of professionals incrementally contributing, where they can, to resolve a problem. The issue is represented in multiple levels of abstraction or alternate views. The experts (knowledge sources) volunteer their services whenever they acknowledge they can contribute. Potential problem-solving actions are represented on an agenda that is upgraded as the issue situation modifications. A controller chooses how useful each contribution is, and who ought to make the next analytical action. One example, the BB1 blackboard architecture [54] was originally motivated by research studies of how people prepare to perform multiple tasks in a trip. [55] An innovation of BB1 was to apply the very same blackboard model to resolving its control issue, i.e., its controller performed meta-level thinking with knowledge sources that monitored how well a strategy or the problem-solving was continuing and could change from one technique to another as conditions – such as objectives or times – changed. BB1 has actually been applied in several domains: building and construction website preparation, smart tutoring systems, and real-time client monitoring.
The second AI winter season, 1988-1993
At the height of the AI boom, companies such as Symbolics, LMI, and Texas Instruments were offering LISP machines specifically targeted to speed up the development of AI applications and research. In addition, a number of expert system business, 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 finest explains the second AI winter that followed:
Many factors can be provided for the arrival of the 2nd AI winter season. The hardware business stopped working when a lot more cost-efficient general Unix workstations from Sun together with great compilers for LISP and Prolog came onto the market. Many commercial deployments of expert systems were discontinued when they showed too costly to keep. Medical expert systems never caught on for numerous factors: the difficulty in keeping them approximately date; the obstacle for physician to learn how to utilize an overwelming variety of various expert systems for different medical conditions; and maybe most crucially, the unwillingness of doctors to trust a computer-made medical diagnosis over their gut impulse, even for specific domains where the specialist systems might surpass an average medical professional. Equity capital cash deserted AI almost over night. The world AI conference IJCAI hosted an enormous and luxurious trade convention and thousands of nonacademic participants in 1987 in Vancouver; the main AI conference the list below year, AAAI 1988 in St. Paul, was a small and strictly academic affair. [9]
Including more rigorous foundations, 1993-2011
Uncertain thinking
Both analytical approaches and extensions to logic were tried.
One statistical approach, concealed Markov designs, had already been promoted in the 1980s for speech recognition work. [11] Subsequently, in 1988, Judea Pearl promoted using Bayesian Networks as a sound however effective way of dealing with unsure thinking with his publication of the book Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. [56] and Bayesian approaches were used effectively in specialist systems. [57] Even later on, in the 1990s, analytical relational knowing, an approach that combines probability with logical solutions, allowed likelihood to be combined with first-order reasoning, e.g., with either Markov Logic Networks or Probabilistic Soft Logic.
Other, non-probabilistic extensions to first-order reasoning to support were likewise tried. For instance, non-monotonic reasoning might be utilized with fact upkeep systems. A fact maintenance system tracked assumptions and reasons for all reasonings. It permitted inferences to be withdrawn when presumptions were learnt to be incorrect or a contradiction was obtained. Explanations might be offered for a reasoning by discussing which guidelines were applied to create it and after that continuing through underlying inferences and guidelines all the way back to root presumptions. [58] Lofti Zadeh had presented a different type of extension to manage the representation of ambiguity. For instance, in deciding how “heavy” or “high” a guy is, there is frequently no clear “yes” or “no” answer, and a predicate for heavy or tall would rather return worths in between 0 and 1. Those worths represented to what degree the predicates were real. His fuzzy reasoning further offered a means for propagating combinations of these worths through logical formulas. [59]
Artificial intelligence
Symbolic machine discovering approaches were investigated to deal with the understanding acquisition bottleneck. One of the earliest is Meta-DENDRAL. Meta-DENDRAL utilized a generate-and-test technique to produce possible guideline hypotheses to check versus spectra. Domain and job knowledge reduced the number of candidates tested to a workable size. Feigenbaum described Meta-DENDRAL as
… the culmination of my imagine the early to mid-1960s pertaining to theory development. The conception was that you had an issue solver like DENDRAL that took some inputs and produced an output. In doing so, it utilized layers of knowledge to steer and prune the search. That knowledge acted due to the fact that we talked to individuals. But how did individuals get the understanding? By taking a look at thousands of spectra. So we wanted a program that would look at countless spectra and infer the knowledge of mass spectrometry that DENDRAL could use to fix individual hypothesis development issues. We did it. We were even able to release brand-new understanding of mass spectrometry in the Journal of the American Chemical Society, giving credit only in a footnote that a program, Meta-DENDRAL, actually did it. We had the ability to do something that had actually been a dream: to have a computer system program developed a new and publishable piece of science. [51]
In contrast to the knowledge-intensive method of Meta-DENDRAL, Ross Quinlan created a domain-independent method to analytical classification, choice tree knowing, starting initially with ID3 [60] and then later on extending its abilities to C4.5. [61] The decision trees created are glass box, interpretable classifiers, with human-interpretable category rules.
Advances were made in comprehending artificial intelligence theory, too. Tom Mitchell presented version space learning which describes knowing as a search through a space of hypotheses, with upper, more general, and lower, more particular, borders including all viable hypotheses consistent with the examples seen up until now. [62] More formally, Valiant introduced Probably Approximately Correct Learning (PAC Learning), a structure for the mathematical analysis of machine knowing. [63]
Symbolic device learning incorporated more than discovering by example. E.g., John Anderson offered a cognitive model of human learning where skill practice leads to a collection of rules from a declarative format to a procedural format with his ACT-R cognitive architecture. For instance, a trainee may discover to use “Supplementary angles are 2 angles whose steps sum 180 degrees” as several different procedural rules. E.g., one rule may state that if X and Y are supplemental and you know X, then Y will be 180 – X. He called his method “knowledge compilation”. ACT-R has actually been used effectively to model elements of human cognition, such as finding out and retention. ACT-R is likewise utilized in smart tutoring systems, called cognitive tutors, to successfully teach geometry, computer programming, and algebra to school children. [64]
Inductive logic shows was another method to discovering that enabled reasoning programs to be manufactured from input-output examples. E.g., Ehud Shapiro’s MIS (Model Inference System) could manufacture Prolog programs from examples. [65] John R. Koza applied hereditary algorithms to program synthesis to create hereditary programs, which he used to manufacture LISP programs. Finally, Zohar Manna and Richard Waldinger offered a more general approach to program synthesis that synthesizes a functional program in the course of proving its specifications to be right. [66]
As an alternative to reasoning, Roger Schank introduced case-based thinking (CBR). The CBR technique laid out in his book, Dynamic Memory, [67] focuses first on keeping in mind crucial analytical cases for future use and generalizing them where appropriate. When confronted with a brand-new issue, CBR retrieves the most comparable previous case and adapts it to the specifics of the present issue. [68] Another option to reasoning, hereditary algorithms and genetic programs are based on an evolutionary model of knowing, where sets of guidelines are encoded into populations, the guidelines govern the habits of individuals, and choice of the fittest prunes out sets of unsuitable guidelines over numerous generations. [69]
Symbolic artificial intelligence was used to finding out principles, guidelines, heuristics, and problem-solving. Approaches, aside from those above, consist of:
1. Learning from guideline or advice-i.e., taking human direction, postured as guidance, and determining how to operationalize it in specific scenarios. For instance, in a video game of Hearts, finding out precisely how to play a hand to “prevent taking points.” [70] 2. Learning from exemplars-improving performance by accepting subject-matter specialist (SME) feedback throughout training. When analytical fails, querying the expert to either discover a brand-new prototype for analytical or to discover a brand-new description as to precisely why one prototype is more pertinent than another. For example, the program Protos discovered to detect tinnitus cases by interacting with an audiologist. [71] 3. Learning by analogy-constructing issue options based on similar issues seen in the past, and after that customizing their services to fit a new situation or domain. [72] [73] 4. Apprentice learning systems-learning novel options to problems by observing human analytical. Domain knowledge discusses why unique options are appropriate and how the service can be generalized. LEAP discovered how to create VLSI circuits by observing human designers. [74] 5. Learning by discovery-i.e., developing jobs to bring out experiments and after that learning from the outcomes. Doug Lenat’s Eurisko, for example, found out heuristics to beat human gamers at the Traveller role-playing game for two years in a row. [75] 6. Learning macro-operators-i.e., browsing for beneficial macro-operators to be gained from sequences of basic problem-solving actions. Good macro-operators simplify problem-solving by enabling problems to be solved at a more abstract level. [76]
Deep learning and neuro-symbolic AI 2011-now
With the increase of deep learning, the symbolic AI approach has actually been compared to deep learning as complementary “… with parallels having been drawn often times by AI researchers between Kahneman’s research study on human reasoning and choice making – shown in his book Thinking, Fast and Slow – and the so-called “AI systems 1 and 2″, which would in concept be designed by deep knowing and symbolic reasoning, respectively.” In this view, symbolic reasoning is more apt for deliberative thinking, preparation, and explanation while deep learning is more apt for quick pattern acknowledgment in affective applications with noisy data. [17] [18]
Neuro-symbolic AI: integrating neural and symbolic approaches
Neuro-symbolic AI efforts to incorporate neural and symbolic architectures in a way that addresses strengths and weak points of each, in a complementary style, in order to support robust AI capable of reasoning, finding out, and cognitive modeling. As argued by Valiant [77] and many others, [78] the efficient construction of abundant computational cognitive designs requires the mix of sound symbolic thinking and effective (device) knowing designs. Gary Marcus, similarly, argues that: “We can not construct rich cognitive designs in an adequate, automatic way without the set of three of hybrid architecture, rich prior knowledge, and advanced techniques for reasoning.”, [79] and in particular: “To build a robust, knowledge-driven technique to AI we need to have the equipment of symbol-manipulation in our toolkit. Too much of useful understanding is abstract to make do without tools that represent and control abstraction, and to date, the only machinery that we understand of that can manipulate such abstract understanding dependably is the device of sign manipulation. ” [80]
Henry Kautz, [19] Francesca Rossi, [81] and Bart Selman [82] have also argued for a synthesis. Their arguments are based on a need to deal with the two type of thinking gone over in Daniel Kahneman’s book, Thinking, Fast and Slow. Kahneman explains human thinking as having two parts, System 1 and System 2. System 1 is fast, automatic, instinctive and unconscious. System 2 is slower, detailed, and explicit. System 1 is the kind utilized for pattern acknowledgment while System 2 is far much better matched for planning, reduction, and deliberative thinking. In this view, deep learning best models the first kind of believing while symbolic reasoning finest designs the second kind and both are required.
Garcez and Lamb explain research study in this location as being continuous for a minimum of the previous twenty years, [83] dating from their 2002 book on neurosymbolic knowing systems. [84] A series of workshops on neuro-symbolic thinking has actually been held every year because 2005, see http://www.neural-symbolic.org/ for details.
In their 2015 paper, Neural-Symbolic Learning and Reasoning: Contributions and Challenges, Garcez et al. argue that:
The integration of the symbolic and connectionist paradigms of AI has been pursued by a relatively little research study community over the last 2 years and has actually yielded a number of substantial outcomes. Over the last decade, neural symbolic systems have actually been shown capable of conquering the so-called propositional fixation of neural networks, as McCarthy (1988) put it in reaction to Smolensky (1988 ); see also (Hinton, 1990). Neural networks were revealed efficient in representing modal and temporal logics (d’Avila Garcez and Lamb, 2006) and pieces of first-order reasoning (Bader, Hitzler, Hölldobler, 2008; d’Avila Garcez, Lamb, Gabbay, 2009). Further, neural-symbolic systems have been used to a variety of issues in the areas of bioinformatics, control engineering, software application verification and adjustment, visual intelligence, ontology learning, and computer system video games. [78]
Approaches for integration are differed. Henry Kautz’s taxonomy of neuro-symbolic architectures, in addition to some examples, follows:
– Symbolic Neural symbolic-is the current method of lots of neural designs in natural language processing, where words or subword tokens are both the supreme input and output of big language models. Examples include BERT, RoBERTa, and GPT-3.
– Symbolic [Neural] -is exemplified by AlphaGo, where symbolic methods are used to call neural methods. In this case the symbolic method is Monte Carlo tree search and the neural techniques learn how to examine game positions.
– Neural|Symbolic-uses a neural architecture to analyze affective data as symbols and relationships that are then reasoned about symbolically.
– Neural: Symbolic → Neural-relies on symbolic reasoning to create or label training information that is consequently learned by a deep learning design, e.g., to train a neural model for symbolic computation by utilizing a Macsyma-like symbolic mathematics system to produce or identify examples.
– Neural _ Symbolic -uses a neural internet that is created from symbolic rules. An example is the Neural Theorem Prover, [85] which constructs a neural network from an AND-OR proof tree generated from understanding base rules and terms. Logic Tensor Networks [86] likewise fall into this category.
– Neural [Symbolic] -allows a neural design to straight call a symbolic thinking engine, e.g., to carry out an action or evaluate a state.
Many essential research concerns stay, such as:
– What is the very best method to incorporate neural and symbolic architectures? [87]- How should symbolic structures be represented within neural networks and extracted from them?
– How should sensible understanding be found out and reasoned about?
– How can abstract understanding that is tough to encode realistically be dealt with?
Techniques and contributions
This area provides an overview of techniques and contributions in a general context resulting in lots of other, more comprehensive short articles in Wikipedia. Sections on Artificial Intelligence and Uncertain Reasoning are covered previously in the history section.
AI programming languages
The key AI programs language in the US throughout the last symbolic AI boom duration was LISP. LISP is the 2nd oldest programs language after FORTRAN and was developed in 1958 by John McCarthy. LISP provided the very first read-eval-print loop to support fast program advancement. Compiled functions could be easily blended with translated functions. Program tracing, stepping, and breakpoints were likewise offered, in addition to the capability to alter values or functions and continue from breakpoints or errors. It had the very first self-hosting compiler, suggesting that the compiler itself was originally composed in LISP and then ran interpretively to compile the compiler code.
Other essential developments pioneered by LISP that have actually spread to other programming languages consist of:
Garbage collection
Dynamic typing
Higher-order functions
Recursion
Conditionals
Programs were themselves information structures that other programs could run on, enabling the easy meaning of higher-level languages.
In contrast to the US, in Europe the essential AI programs language during that same duration was Prolog. Prolog supplied a built-in store of facts and stipulations that could be queried by a read-eval-print loop. The shop could serve as a knowledge base and the clauses could act as guidelines or a restricted type of reasoning. As a subset of first-order logic Prolog was based upon Horn clauses with a closed-world assumption-any realities not known were thought about false-and an unique name presumption for primitive terms-e.g., the identifier barack_obama was considered to describe precisely one object. Backtracking and marriage are integrated to Prolog.
Alain Colmerauer and Philippe Roussel are credited as the creators of Prolog. Prolog is a type of reasoning programs, which was invented by Robert Kowalski. Its history was likewise affected by Carl Hewitt’s PLANNER, an assertional database with pattern-directed invocation of approaches. For more detail see the area on the origins of Prolog in the PLANNER post.
Prolog is also a sort of declarative shows. The reasoning clauses that explain programs are straight interpreted to run the programs defined. No specific series of actions is needed, as is the case with crucial programs languages.
Japan championed Prolog for its Fifth Generation Project, meaning to build special hardware for high performance. Similarly, LISP makers were developed to run LISP, however as the second AI boom turned to bust these companies might not compete with new workstations that might now run LISP or Prolog natively at equivalent speeds. See the history section for more detail.
Smalltalk was another prominent AI programs language. For instance, it presented metaclasses and, in addition to Flavors and CommonLoops, influenced the Common Lisp Object System, or (CLOS), that is now part of Common Lisp, the existing standard Lisp dialect. CLOS is a Lisp-based object-oriented system that enables several inheritance, in addition to incremental extensions to both classes and metaclasses, hence supplying a run-time meta-object protocol. [88]
For other AI programs languages see this list of shows languages for expert system. Currently, Python, a multi-paradigm programming language, is the most popular programming language, partially due to its substantial plan library that supports information science, natural language processing, and deep learning. Python includes a read-eval-print loop, functional elements such as higher-order functions, and object-oriented shows that consists of metaclasses.
Search
Search emerges in numerous type of problem fixing, consisting of preparation, restriction satisfaction, and playing video games such as checkers, chess, and go. The finest known AI-search tree search algorithms are breadth-first search, depth-first search, A *, and Monte Carlo Search. Key search algorithms for Boolean satisfiability are WalkSAT, conflict-driven provision learning, and the DPLL algorithm. For adversarial search when playing video games, alpha-beta pruning, branch and bound, and minimax were early contributions.
Knowledge representation and thinking
Multiple different techniques to represent knowledge and after that factor with those representations have been investigated. Below is a fast overview of methods to understanding representation and automated reasoning.
Knowledge representation
Semantic networks, conceptual graphs, frames, and reasoning are all techniques to modeling knowledge such as domain understanding, analytical understanding, and the semantic meaning of language. Ontologies model crucial concepts and their relationships in a domain. Example ontologies are YAGO, WordNet, and DOLCE. DOLCE is an example of an upper ontology that can be utilized for any domain while WordNet is a lexical resource that can likewise be considered as an ontology. YAGO integrates WordNet as part of its ontology, to line up truths drawn out from Wikipedia with WordNet synsets. The Disease Ontology is an example of a medical ontology presently being utilized.
Description reasoning is a reasoning for automated category of ontologies and for identifying inconsistent category information. OWL is a language used to represent ontologies with description logic. Protégé is an ontology editor that can check out in OWL ontologies and after that inspect consistency with deductive classifiers such as such as HermiT. [89]
First-order reasoning is more basic than description logic. The automated theorem provers discussed listed below can prove theorems in first-order reasoning. Horn provision logic is more limited than first-order logic and is used in logic programming languages such as Prolog. Extensions to first-order logic include temporal reasoning, to manage time; epistemic logic, to factor about agent understanding; modal logic, to manage possibility and necessity; and probabilistic logics to handle logic and possibility together.
Automatic theorem proving
Examples of automated theorem provers for first-order reasoning are:
Prover9.
ACL2.
Vampire.
Prover9 can be utilized in conjunction with the Mace4 design checker. ACL2 is a theorem prover that can deal with evidence by induction and is a descendant of the Boyer-Moore Theorem Prover, likewise understood as Nqthm.
Reasoning in knowledge-based systems
Knowledge-based systems have a specific understanding base, normally of guidelines, to enhance reusability across domains by separating procedural code and domain understanding. A separate reasoning engine processes rules and adds, deletes, or modifies a knowledge store.
Forward chaining reasoning engines are the most common, and are seen in CLIPS and OPS5. Backward chaining happens in Prolog, where a more limited sensible representation is utilized, Horn Clauses. Pattern-matching, particularly marriage, is used in Prolog.
A more versatile type of problem-solving occurs when thinking about what to do next occurs, instead of just selecting one of the available actions. This type of meta-level thinking is used in Soar and in the BB1 chalkboard architecture.
Cognitive architectures such as ACT-R may have extra abilities, such as the ability to put together frequently utilized knowledge into higher-level pieces.
Commonsense thinking
Marvin Minsky first proposed frames as a way of translating typical visual situations, such as an office, and Roger Schank extended this concept to scripts for common regimens, such as eating in restaurants. Cyc has actually attempted to catch beneficial common-sense knowledge and has “micro-theories” to handle particular type of domain-specific reasoning.
Qualitative simulation, such as Benjamin Kuipers’s QSIM, [90] estimates human thinking about ignorant physics, such as what happens when we heat a liquid in a pot on the range. We anticipate it to heat and potentially boil over, even though we may not understand its temperature level, its boiling point, or other information, such as air pressure.
Similarly, Allen’s temporal interval algebra is a simplification of reasoning about time and Region Connection Calculus is a simplification of reasoning about spatial relationships. Both can be solved with constraint solvers.
Constraints and constraint-based reasoning
Constraint solvers carry out a more restricted kind of inference than first-order reasoning. They can simplify sets of spatiotemporal restrictions, such as those for RCC or Temporal Algebra, together with resolving other type of puzzle issues, such as Wordle, Sudoku, cryptarithmetic problems, and so on. Constraint logic shows can be utilized to fix scheduling problems, for example with constraint managing rules (CHR).
Automated preparation
The General Problem Solver (GPS) cast planning as problem-solving used means-ends analysis to produce plans. STRIPS took a various approach, viewing planning as theorem proving. Graphplan takes a least-commitment approach to preparation, instead of sequentially picking actions from a preliminary state, working forwards, or an objective state if working in reverse. Satplan is an approach to preparing where a preparation problem is lowered to a Boolean satisfiability issue.
Natural language processing
Natural language processing concentrates on treating language as data to perform jobs such as identifying topics without always understanding the designated meaning. Natural language understanding, on the other hand, constructs a meaning representation and uses that for more processing, such as answering questions.
Parsing, tokenizing, spelling correction, part-of-speech tagging, noun and verb expression chunking are all elements of natural language processing long handled by symbolic AI, but given that enhanced by deep learning techniques. In symbolic AI, discourse representation theory and first-order reasoning have actually been utilized to represent sentence meanings. Latent semantic analysis (LSA) and specific semantic analysis also provided vector representations of documents. In the latter case, vector elements are interpretable as concepts named by Wikipedia short articles.
New deep learning techniques based on Transformer models have now eclipsed these earlier symbolic AI techniques and attained modern efficiency in natural language processing. However, Transformer models are nontransparent and do not yet produce human-interpretable semantic representations for sentences and files. Instead, they produce task-specific vectors where the meaning of the vector components is nontransparent.
Agents and multi-agent systems
Agents are self-governing systems embedded in an environment they perceive and act on in some sense. Russell and Norvig’s basic book on expert system is organized to show representative architectures of increasing sophistication. [91] The sophistication of representatives differs from basic reactive representatives, to those with a model of the world and automated preparation abilities, perhaps a BDI agent, i.e., one with beliefs, desires, and objectives – or alternatively a support discovering design found out over time to pick actions – up to a mix of alternative architectures, such as a neuro-symbolic architecture [87] that includes deep learning for understanding. [92]
On the other hand, a multi-agent system includes multiple agents that interact amongst themselves with some inter-agent interaction language such as Knowledge Query and Manipulation Language (KQML). The agents need not all have the same internal architecture. Advantages of multi-agent systems consist of the capability to divide work among the agents and to increase fault tolerance when representatives are lost. Research problems consist of how agents reach consensus, dispersed issue resolving, multi-agent knowing, multi-agent planning, and distributed constraint optimization.
Controversies emerged from early in symbolic AI, both within the field-e.g., in between logicists (the pro-logic “neats”) and non-logicists (the anti-logic “scruffies”)- and in between those who welcomed AI however declined symbolic approaches-primarily connectionists-and those outside the field. Critiques from beyond the field were primarily from thinkers, on intellectual grounds, but likewise from funding agencies, specifically throughout the 2 AI winter seasons.
The Frame Problem: understanding representation challenges for first-order logic
Limitations were found in utilizing simple first-order logic to reason about dynamic domains. Problems were discovered both with concerns to specifying the preconditions for an action to succeed and in supplying axioms for what did not change after an action was carried out.
McCarthy and Hayes presented the Frame Problem in 1969 in the paper, “Some Philosophical Problems from the Standpoint of Artificial Intelligence.” [93] A simple example occurs in “showing that a person person might enter discussion with another”, as an axiom asserting “if an individual has a telephone he still has it after searching for a number in the telephone directory” would be required for the reduction to prosper. Similar axioms would be needed for other domain actions to specify what did not alter.
A comparable issue, called the Qualification Problem, happens in attempting to identify the prerequisites for an action to succeed. An infinite number of pathological conditions can be pictured, e.g., a banana in a tailpipe could prevent a vehicle from running correctly.
McCarthy’s approach to fix the frame problem was circumscription, a kind of non-monotonic logic where reductions could be made from actions that require just define what would alter while not needing to explicitly specify everything that would not alter. Other non-monotonic reasonings provided fact maintenance systems that modified beliefs leading to contradictions.
Other ways of handling more open-ended domains consisted of probabilistic reasoning systems and artificial intelligence to learn new concepts and guidelines. McCarthy’s Advice Taker can be considered as an inspiration here, as it might integrate new knowledge supplied by a human in the form of assertions or rules. For instance, experimental symbolic maker finding out systems explored the capability to take top-level natural language guidance and to interpret it into domain-specific actionable guidelines.
Similar to the problems in dealing with dynamic domains, common-sense reasoning is also tough to catch in official reasoning. Examples of sensible thinking include implicit reasoning about how people think or general knowledge of day-to-day occasions, objects, and living animals. This sort of knowledge is considered given and not considered as noteworthy. Common-sense thinking is an open area of research study and challenging both for symbolic systems (e.g., Cyc has actually attempted to capture essential parts of this understanding over more than a years) and neural systems (e.g., self-driving cars and trucks that do not understand not to drive into cones or not to hit pedestrians walking a bike).
McCarthy viewed his Advice Taker as having common-sense, however his definition of sensible was different than the one above. [94] He defined a program as having sound judgment “if it instantly deduces for itself an adequately broad class of instant effects of anything it is informed and what it currently knows. “
Connectionist AI: philosophical difficulties and sociological disputes
Connectionist techniques consist of earlier deal with neural networks, [95] such as perceptrons; operate in the mid to late 80s, such as Danny Hillis’s Connection Machine and Yann LeCun’s advances in convolutional neural networks; to today’s more sophisticated methods, such as Transformers, GANs, and other work in deep learning.
Three philosophical positions [96] have actually been detailed amongst connectionists:
1. Implementationism-where connectionist architectures implement the capabilities for symbolic processing,
2. Radical connectionism-where symbolic processing is rejected completely, and connectionist architectures underlie intelligence and are fully enough to explain it,
3. Moderate connectionism-where symbolic processing and connectionist architectures are considered as complementary and both are required for intelligence
Olazaran, in his sociological history of the debates within the neural network community, described the moderate connectionism view as basically compatible with current research in neuro-symbolic hybrids:
The third and last position I would like to examine here is what I call the moderate connectionist view, a more eclectic view of the existing argument between connectionism and symbolic AI. Among the researchers who has actually elaborated this position most clearly is Andy Clark, a philosopher from the School of Cognitive and Computing Sciences of the University of Sussex (Brighton, England). Clark protected hybrid (partly symbolic, partly connectionist) systems. He claimed that (a minimum of) 2 sort of theories are needed in order to study and design cognition. On the one hand, for some information-processing tasks (such as pattern recognition) connectionism has benefits over symbolic models. But on the other hand, for other cognitive processes (such as serial, deductive reasoning, and generative sign manipulation processes) the symbolic paradigm offers adequate designs, and not just “approximations” (contrary to what radical connectionists would declare). [97]
Gary Marcus has claimed that the animus in the deep learning neighborhood against symbolic techniques now might be more sociological than philosophical:
To think that we can simply desert symbol-manipulation is to suspend shock.
And yet, for the many part, that’s how most present AI profits. Hinton and many others have tried tough to eliminate signs completely. The deep learning hope-seemingly grounded not a lot in science, however in a sort of historic grudge-is that smart habits will emerge simply from the confluence of enormous data and deep learning. Where classical computers and software solve jobs by defining sets of symbol-manipulating rules devoted to particular jobs, such as editing a line in a word processor or performing a computation in a spreadsheet, neural networks normally try to solve jobs by analytical approximation and learning from examples.
According to Marcus, Geoffrey Hinton and his associates have actually been vehemently “anti-symbolic”:
When deep knowing reemerged in 2012, it was with a sort of take-no-prisoners attitude that has defined many of the last years. By 2015, his hostility towards all things symbols had completely taken shape. He gave a talk at an AI workshop at Stanford comparing signs to aether, one of science’s greatest mistakes.
…
Since then, his anti-symbolic project has actually only increased in intensity. In 2016, Yann LeCun, Bengio, and Hinton composed a manifesto for deep knowing in among science’s most crucial journals, Nature. It closed with a direct attack on sign manipulation, calling not for reconciliation but for outright replacement. Later, Hinton told a gathering of European Union leaders that investing any additional cash in symbol-manipulating techniques was “a huge error,” comparing it to purchasing internal combustion engines in the age of electric cars. [98]
Part of these disputes may be because of unclear terms:
Turing award winner Judea Pearl provides a critique of artificial intelligence which, sadly, conflates the terms artificial intelligence and deep knowing. Similarly, when Geoffrey Hinton describes symbolic AI, the undertone of the term tends to be that of specialist systems dispossessed of any ability to discover. The usage of the terms is in requirement of explanation. Artificial intelligence is not confined to association guideline mining, c.f. the body of work on symbolic ML and relational knowing (the differences to deep learning being the choice of representation, localist sensible rather than dispersed, and the non-use of gradient-based knowing algorithms). Equally, symbolic AI is not almost production guidelines composed by hand. An appropriate meaning of AI issues knowledge representation and reasoning, self-governing multi-agent systems, preparation and argumentation, along with knowing. [99]
Situated robotics: the world as a design
Another critique of symbolic AI is the embodied cognition method:
The embodied cognition technique declares that it makes no sense to consider the brain separately: cognition occurs within a body, which is embedded in an environment. We need to study the system as a whole; the brain’s operating exploits regularities in its environment, including the rest of its body. Under the embodied cognition approach, robotics, vision, and other sensors become main, not peripheral. [100]
Rodney Brooks developed behavior-based robotics, one technique to embodied cognition. Nouvelle AI, another name for this approach, is deemed an alternative to both symbolic AI and connectionist AI. His technique turned down representations, either symbolic or distributed, as not just unnecessary, however as harmful. Instead, he created the subsumption architecture, a layered architecture for embodied agents. Each layer achieves a different purpose and should function in the real life. For instance, the first robot he explains in Intelligence Without Representation, has 3 layers. The bottom layer analyzes finder sensing units to prevent objects. The middle layer causes the robot to roam around when there are no challenges. The top layer causes the robotic to go to more far-off locations for additional expedition. Each layer can momentarily hinder or reduce a lower-level layer. He criticized AI scientists for defining AI problems for their systems, when: “There is no clean division between perception (abstraction) and thinking in the real life.” [101] He called his robotics “Creatures” and each layer was “composed of a fixed-topology network of simple finite state devices.” [102] In the Nouvelle AI approach, “First, it is extremely essential to test the Creatures we integrate in the genuine world; i.e., in the very same world that we people inhabit. It is disastrous to fall into the temptation of checking them in a streamlined world first, even with the very best intentions of later moving activity to an unsimplified world.” [103] His emphasis on real-world testing remained in contrast to “Early operate in AI concentrated on games, geometrical issues, symbolic algebra, theorem proving, and other official systems” [104] and the usage of the blocks world in symbolic AI systems such as SHRDLU.
Current views
Each approach-symbolic, connectionist, and behavior-based-has benefits, however has been criticized by the other techniques. Symbolic AI has been criticized as disembodied, responsible to the qualification issue, and poor in handling the perceptual issues where deep learning excels. In turn, connectionist AI has been criticized as badly fit for deliberative detailed problem resolving, incorporating knowledge, and managing planning. Finally, Nouvelle AI excels in reactive and real-world robotics domains but has actually been slammed for problems in incorporating knowing and knowledge.
Hybrid AIs including one or more of these methods are currently deemed the course forward. [19] [81] [82] Russell and Norvig conclude that:
Overall, Dreyfus saw areas where AI did not have complete answers and stated that Al is for that reason difficult; we now see a number of these exact same locations going through ongoing research study and advancement leading to increased capability, not impossibility. [100]
Expert system.
Automated preparation and scheduling
Automated theorem proving
Belief revision
Case-based thinking
Cognitive architecture
Cognitive science
Connectionism
Constraint programs
Deep learning
First-order reasoning
GOFAI
History of expert system
Inductive reasoning shows
Knowledge-based systems
Knowledge representation and reasoning
Logic shows
Artificial intelligence
Model monitoring
Model-based thinking
Multi-agent system
Natural language processing
Neuro-symbolic AI
Ontology
Philosophy of expert system
Physical symbol systems hypothesis
Semantic Web
Sequential pattern mining
Statistical relational knowing
Symbolic mathematics
YAGO ontology
WordNet
Notes
^ McCarthy as soon as stated: “This is AI, so we do not care if it’s psychologically genuine”. [4] McCarthy reiterated his position in 2006 at the AI@50 conference where he said “Artificial intelligence is not, by meaning, simulation of human intelligence”. [28] Pamela McCorduck writes that there are “2 significant branches of artificial intelligence: one intended at producing smart behavior despite how it was achieved, and the other intended at modeling smart procedures found in nature, especially human ones.”, [29] Stuart Russell and Peter Norvig composed “Aeronautical engineering texts do not specify the goal of their field as making ‘makers that fly so exactly like pigeons that they can trick even other pigeons.'” [30] Citations
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^ Thomason, Richmond (February 27, 2024). “Logic-Based Artificial Intelligence”. In Zalta, Edward N. (ed.). Stanford Encyclopedia of Philosophy.
^ Garnelo, Marta; Shanahan, Murray (2019-10-01). “Reconciling deep knowing with symbolic synthetic intelligence: representing objects and relations”. Current Opinion in Behavioral Sciences. 29: 17-23. doi:10.1016/ j.cobeha.2018.12.010. hdl:10044/ 1/67796. S2CID 72336067.
^ a b Kolata 1982.
^ Kautz 2022, pp. 107-109.
^ a b Russell & Norvig 2021, p. 19.
^ a b Russell & Norvig 2021, pp. 22-23.
^ a b Kautz 2022, pp. 109-110.
^ a b c Kautz 2022, p. 110.
^ Kautz 2022, pp. 110-111.
^ a b Russell & Norvig 2021, p. 25.
^ Kautz 2022, p. 111.
^ Kautz 2020, pp. 110-111.
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^ a b Marcus & Davis 2019.
^ a b Rossi, Francesca. “Thinking Fast and Slow in AI”. AAAI. Retrieved 5 July 2022.
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^ a b c Kautz 2020.
^ Kautz 2022, p. 106.
^ Newell & Simon 1972.
^ & McCorduck 2004, pp. 139-179, 245-250, 322-323 (EPAM).
^ Crevier 1993, pp. 145-149.
^ McCorduck 2004, pp. 450-451.
^ Crevier 1993, pp. 258-263.
^ a b Kautz 2022, p. 108.
^ Russell & Norvig 2021, p. 9 (logicist AI), p. 19 (McCarthy’s work).
^ Maker 2006.
^ McCorduck 2004, pp. 100-101.
^ Russell & Norvig 2021, p. 2.
^ McCorduck 2004, pp. 251-259.
^ Crevier 1993, pp. 193-196.
^ Howe 1994.
^ McCorduck 2004, pp. 259-305.
^ Crevier 1993, pp. 83-102, 163-176.
^ McCorduck 2004, pp. 421-424, 486-489.
^ Crevier 1993, p. 168.
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^ Crevier 1993, pp. 239-243.
^ Russell & Norvig 2021, p. 316, 340.
^ Kautz 2022, p. 109.
^ Russell & Norvig 2021, p. 22.
^ McCorduck 2004, pp. 266-276, 298-300, 314, 421.
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^ Russell & Norvig 2021, pp. 22-24.
^ McCorduck 2004, pp. 327-335, 434-435.
^ Crevier 1993, pp. 145-62, 197-203.
^ a b Russell & Norvig 2021, p. 23.
^ a b Clancey 1987.
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