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Symbolic Expert System
In expert system, symbolic synthetic intelligence (also referred to as classical synthetic intelligence or logic-based artificial intelligence) [1] [2] is the term for the collection of all methods in synthetic intelligence research study that are based on top-level symbolic (human-readable) representations of issues, logic and search. [3] Symbolic AI utilized tools such as reasoning programs, production guidelines, semantic internet and frames, and it developed applications such as knowledge-based systems (in specific, professional systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated preparation and scheduling systems. The Symbolic AI paradigm led to critical ideas in search, symbolic shows languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of official understanding and reasoning systems.
Symbolic AI was the dominant paradigm of AI research from the mid-1950s till the mid-1990s. [4] Researchers in the 1960s and the 1970s were persuaded that symbolic techniques would ultimately succeed in creating a device with synthetic basic intelligence and considered this the ultimate objective of their field. [citation needed] An early boom, with early successes such as the Logic Theorist and Samuel’s Checkers Playing Program, caused unrealistic expectations and promises and was followed by the first AI Winter as moneying dried up. [5] [6] A second boom (1969-1986) happened with the rise of expert systems, their guarantee of catching corporate knowledge, and an enthusiastic business welcome. [7] [8] That boom, and some early successes, e.g., with XCON at DEC, was followed once again by later frustration. [8] Problems with troubles in understanding acquisition, keeping big understanding bases, and brittleness in handling out-of-domain issues arose. Another, 2nd, AI Winter (1988-2011) followed. [9] Subsequently, AI scientists concentrated on addressing underlying issues in managing unpredictability and in knowledge acquisition. [10] Uncertainty was addressed with official methods such as covert Markov designs, Bayesian reasoning, and analytical relational knowing. [11] [12] Symbolic machine discovering addressed the knowledge acquisition issue with contributions consisting of Version Space, Valiant’s PAC knowing, Quinlan’s ID3 decision-tree knowing, case-based knowing, and inductive logic programs to discover relations. [13]
Neural networks, a subsymbolic method, had been pursued from early days and reemerged highly in 2012. Early examples are Rosenblatt’s perceptron knowing work, the backpropagation work of Rumelhart, Hinton and Williams, [14] and work in convolutional neural networks by LeCun et al. in 1989. [15] However, neural networks were not seen as effective until about 2012: “Until Big Data ended up being commonplace, the general consensus in the Al community was that the so-called neural-network approach was helpless. Systems just didn’t work that well, compared to other methods. … A transformation can be found in 2012, when a number of people, consisting of a team of researchers working with Hinton, worked out a way to utilize the power of GPUs to enormously increase the power of neural networks.” [16] Over the next a number of years, deep knowing had amazing success in handling vision, speech recognition, speech synthesis, image generation, and machine translation. However, given that 2020, as inherent problems with predisposition, explanation, comprehensibility, and effectiveness ended up being more evident with deep learning techniques; an increasing number of AI scientists have actually required integrating the finest of both the symbolic and neural network approaches [17] [18] and resolving areas that both approaches have difficulty with, such as common-sense reasoning. [16]
A brief history of symbolic AI to today 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 short article on the History of AI, with dates and titles varying somewhat for increased clarity.
The first AI summer: illogical enthusiasm, 1948-1966
Success at early efforts in AI took place in three primary areas: artificial neural networks, knowledge representation, and heuristic search, adding to high expectations. This area summarizes Kautz’s reprise of early AI history.
Approaches influenced by human or animal cognition or behavior
Cybernetic approaches attempted to duplicate the feedback loops in between animals and their environments. A robotic turtle, with sensors, motors for driving and guiding, and 7 vacuum tubes for control, based on a preprogrammed neural internet, was built as early as 1948. This work can be viewed as an early precursor to later operate in neural networks, support learning, and situated robotics. [20]
A crucial early symbolic AI program was the Logic theorist, written by Allen Newell, Herbert Simon and Cliff Shaw in 1955-56, as it was able to prove 38 elementary theorems from Whitehead and Russell’s Principia Mathematica. Newell, Simon, and Shaw later on generalized this work to create a domain-independent issue solver, GPS (General Problem Solver). GPS fixed issues represented with official operators through state-space search utilizing means-ends analysis. [21]
During the 1960s, symbolic approaches attained excellent success at mimicing intelligent behavior in structured environments such as game-playing, symbolic mathematics, and theorem-proving. AI research study was focused in four institutions in the 1960s: Carnegie Mellon University, Stanford, MIT and (later) University of Edinburgh. Each one developed its own style of research study. Earlier methods based upon cybernetics or synthetic neural networks were abandoned or pushed into the background.
Herbert Simon and Allen Newell studied human problem-solving skills and tried to formalize them, and their work laid the structures of the field of expert system, in addition to cognitive science, operations research and management science. Their research group used the outcomes of mental experiments to develop programs that simulated the techniques that individuals used to fix issues. [22] [23] This tradition, focused at Carnegie Mellon University would ultimately culminate in the development of the Soar architecture in the center 1980s. [24] [25]
Heuristic search
In addition to the highly specialized domain-specific sort of understanding that we will see later on used in expert systems, early symbolic AI researchers found another more basic application of knowledge. These were called heuristics, guidelines that guide a search in promising directions: “How can non-enumerative search be useful when the underlying issue is tremendously hard? The technique promoted by Simon and Newell is to utilize heuristics: fast algorithms that might stop working on some inputs or output suboptimal services.” [26] Another crucial advance was to find a way to apply these heuristics that guarantees an option will be discovered, if there is one, not standing up to the occasional fallibility of heuristics: “The A * algorithm supplied a general frame for complete and ideal heuristically assisted search. A * is used as a subroutine within almost every AI algorithm today however is still no magic bullet; its warranty of completeness is purchased the cost of worst-case exponential time. [26]
Early work on understanding representation and thinking
Early work covered both applications of official thinking highlighting first-order logic, together with efforts to handle common-sense thinking in a less official way.
Modeling official thinking with logic: the “neats”
Unlike Simon and Newell, John McCarthy felt that devices did not need to mimic the exact mechanisms of human idea, but could rather look for the essence of abstract thinking and analytical with reasoning, [27] no matter whether people utilized the same algorithms. [a] His laboratory at Stanford (SAIL) concentrated on using official reasoning to resolve a wide array of problems, including understanding representation, preparation and knowing. [31] Logic was likewise the focus of the work at the University of Edinburgh and elsewhere in Europe which led to the advancement of the programs language Prolog and the science of logic programming. [32] [33]
Modeling implicit common-sense knowledge with frames and scripts: the “scruffies”
Researchers at MIT (such as Marvin Minsky and Seymour Papert) [34] [35] [6] found that solving hard problems in vision and natural language processing required ad hoc solutions-they argued that no simple and basic principle (like reasoning) would capture all the elements of smart behavior. Roger Schank explained their “anti-logic” techniques as “scruffy” (rather than the “cool” paradigms at CMU and Stanford). [36] [37] Commonsense knowledge bases (such as Doug Lenat’s Cyc) are an example of “shabby” AI, since they should be constructed by hand, one complicated concept at a time. [38] [39] [40]
The first AI winter season: crushed dreams, 1967-1977
The very first AI winter season was a shock:
During the first AI summer season, many individuals believed that machine intelligence could be achieved in just a few years. The Defense Advance Research Projects Agency (DARPA) launched programs to support AI research to use AI to solve issues of national security; in specific, to automate the translation of Russian to English for intelligence operations and to create autonomous tanks for the battleground. Researchers had actually begun to recognize that accomplishing AI was going to be much more difficult than was supposed a years earlier, but a combination of hubris and disingenuousness led lots of university and think-tank researchers to accept financing with guarantees of deliverables that they should have known they might not fulfill. By the mid-1960s neither helpful natural language translation systems nor self-governing tanks had actually been produced, and a significant backlash set in. New DARPA leadership canceled existing AI funding programs.
Outside of the United States, the most fertile ground for AI research was the UK. The AI winter in the UK was spurred on not a lot by disappointed military leaders as by rival academics who saw AI scientists as charlatans and a drain on research financing. A teacher of applied mathematics, Sir James Lighthill, was commissioned by Parliament to examine the state of AI research in the country. The report mentioned that all of the problems being worked on in AI would be much better dealt with by scientists from other disciplines-such as applied mathematics. The report likewise claimed that AI successes on toy problems might never scale to real-world applications due to combinatorial surge. [41]
The 2nd AI summer: understanding is power, 1978-1987
Knowledge-based systems
As restrictions with weak, domain-independent methods became more and more apparent, [42] researchers from all 3 traditions started to develop knowledge into AI applications. [43] [7] The knowledge transformation was driven by the awareness that understanding underlies high-performance, domain-specific AI applications.
Edward Feigenbaum said:
– “In the understanding lies the power.” [44]
to explain that high efficiency in a particular domain needs both basic and highly domain-specific knowledge. Ed Feigenbaum and Doug Lenat called this The Knowledge Principle:
( 1) The Knowledge Principle: if a program is to carry out an intricate task well, it must understand a fantastic offer about the world in which it runs.
( 2) A plausible extension of that principle, called the Breadth Hypothesis: there are 2 extra abilities required for intelligent behavior in unexpected scenarios: falling back on increasingly general understanding, and analogizing to specific but remote knowledge. [45]
Success with specialist systems
This “knowledge revolution” led to the advancement and release of expert systems (introduced by Edward Feigenbaum), the very first commercially effective kind of AI software application. [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 detected bacteremia – and recommended more laboratory tests, when necessary – by interpreting laboratory results, client history, and medical professional observations. “With about 450 rules, MYCIN was able to perform as well as some experts, and significantly much better than junior medical professionals.” [49] INTERNIST and CADUCEUS which took on internal medication medical diagnosis. Internist attempted to catch the proficiency of the chairman of internal medicine at the University of Pittsburgh School of Medicine while CADUCEUS might eventually identify as much as 1000 different illness.
– GUIDON, which revealed how a knowledge base constructed for specialist issue resolving could be repurposed for mentor. [50] XCON, to configure VAX computer systems, a then laborious procedure that might take up to 90 days. XCON reduced the time to about 90 minutes. [9]
DENDRAL is thought about the first professional system that depend on knowledge-intensive analytical. It is explained listed below, by Ed Feigenbaum, from a Communications of the ACM interview, Interview with Ed Feigenbaum:
Among the individuals at Stanford thinking about computer-based designs of mind was Joshua Lederberg, the 1958 Nobel Prize winner in genes. When I told him I desired an induction “sandbox”, he stated, “I have just the one for you.” His laboratory was doing mass spectrometry of amino acids. The question was: how do you go from taking a look at the spectrum of an amino acid to the chemical structure of the amino acid? That’s how we began the DENDRAL Project: I was proficient at heuristic search methods, and he had an algorithm that was good 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 tablet, and also among the world’s most appreciated mass spectrometrists. Carl and his postdocs were world-class specialists in mass spectrometry. We started to add to their knowledge, developing understanding of engineering as we went along. These experiments totaled up to titrating DENDRAL more and more knowledge. The more you did that, the smarter the program became. We had great outcomes.
The generalization was: in the knowledge lies the power. That was the big idea. In my career that is the substantial, “Ah ha!,” and it wasn’t the way AI was being done previously. Sounds basic, however it’s probably AI‘s most powerful generalization. [51]
The other expert systems discussed above followed DENDRAL. MYCIN exemplifies the traditional specialist system architecture of a knowledge-base of rules paired to a symbolic thinking mechanism, including the usage of certainty aspects to deal with unpredictability. GUIDON demonstrates how an explicit knowledge base can be repurposed for a 2nd application, tutoring, and is an example of an intelligent tutoring system, a specific type of knowledge-based application. Clancey showed that it was not enough merely to use MYCIN’s guidelines for instruction, however that he also required to include rules for dialogue management and student modeling. [50] XCON is substantial due to the fact that of the countless dollars it saved DEC, which triggered the expert system boom where most all major corporations in the US had skilled systems groups, to record corporate proficiency, maintain 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 development. Nearly every significant U.S. corporation had its own Al group and was either using or examining professional systems. [49]
Chess specialist knowledge was encoded in Deep Blue. In 1996, this enabled IBM’s Deep Blue, with the assistance of symbolic AI, to win in a game of chess against the world champion at that time, Garry Kasparov. [52]
Architecture of knowledge-based and expert systems
A key component of the system architecture for all expert systems is the knowledge base, which stores truths and rules for problem-solving. [53] The most basic approach for an expert system understanding base is just a collection or network of production rules. Production rules link symbols in a relationship similar to an If-Then statement. The specialist system processes the guidelines to make deductions and to identify what additional details it requires, i.e. what concerns to ask, using human-readable signs. For instance, OPS5, CLIPS and their successors Jess and Drools operate 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 prerequisites – way. More advanced knowledge-based systems, such as Soar can likewise perform meta-level thinking, that is reasoning about their own thinking in regards to deciding how to fix issues and keeping an eye on the success of problem-solving techniques.
Blackboard systems are a second type of knowledge-based or skilled system architecture. They model a community of experts incrementally contributing, where they can, to solve a problem. The issue is represented in numerous levels of abstraction or alternate views. The specialists (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 problem circumstance modifications. A controller chooses how helpful each contribution is, and who must make the next problem-solving action. One example, the BB1 chalkboard architecture [54] was originally influenced by studies of how human beings plan to carry out numerous jobs in a trip. [55] An innovation of BB1 was to apply the exact same blackboard design to resolving its control problem, i.e., its controller carried out meta-level thinking with knowledge sources that kept track of how well a strategy or the analytical was continuing and might change from one method to another as conditions – such as objectives or times – altered. BB1 has been used in several domains: building and construction site planning, intelligent tutoring systems, and real-time patient tracking.
The second AI winter, 1988-1993
At the height of the AI boom, business such as Symbolics, LMI, and Texas Instruments were selling LISP devices particularly targeted to accelerate the advancement of AI applications and research study. In addition, numerous synthetic intelligence companies, such as Teknowledge and Inference Corporation, were offering expert system shells, training, and consulting to corporations.
Unfortunately, the AI boom did not last and Kautz finest describes the 2nd AI winter season that followed:
Many reasons can be used for the arrival of the second AI winter. The hardware business stopped working when far more economical general Unix workstations from Sun together with excellent compilers for LISP and Prolog came onto the marketplace. Many commercial deployments of expert systems were stopped when they showed too expensive to preserve. Medical expert systems never ever caught on for numerous reasons: the trouble in keeping them up to date; the obstacle for doctor to learn how to use a bewildering range of different specialist systems for various medical conditions; and maybe most crucially, the reluctance of physicians to rely on a computer-made medical diagnosis over their gut instinct, even for particular domains where the expert systems could outperform an average medical professional. Equity capital money deserted AI almost overnight. The world AI conference IJCAI hosted an enormous and extravagant trade convention and countless nonacademic guests in 1987 in Vancouver; the primary AI conference the following year, AAAI 1988 in St. Paul, was a little and strictly scholastic affair. [9]
Adding in more strenuous structures, 1993-2011
Uncertain thinking
Both statistical methods and extensions to logic were attempted.
One statistical approach, hidden Markov designs, had actually currently been popularized in the 1980s for speech recognition work. [11] Subsequently, in 1988, Judea Pearl promoted using Bayesian Networks as a noise but efficient way of managing unpredictable thinking with his publication of the book Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. [56] and Bayesian methods were used successfully in expert systems. [57] Even later, in the 1990s, statistical relational learning, a technique that integrates likelihood with rational solutions, permitted likelihood to be integrated with first-order reasoning, e.g., with either Markov Logic Networks or Probabilistic Soft Logic.
Other, non-probabilistic extensions to first-order reasoning to assistance were also tried. For example, non-monotonic thinking might be used with reality upkeep systems. A truth upkeep system tracked presumptions and justifications for all reasonings. It permitted inferences to be withdrawn when presumptions were discovered to be incorrect or a contradiction was obtained. Explanations might be attended to a reasoning by explaining which rules were applied to create it and after that continuing through underlying reasonings and rules all the way back to root presumptions. [58] Lofti Zadeh had actually presented a various sort of extension to handle the representation of uncertainty. For example, in deciding how “heavy” or “tall” a man is, there is regularly no clear “yes” or “no” answer, and a predicate for heavy or high would instead return worths in between 0 and 1. Those values represented to what degree the predicates were real. His fuzzy logic even more supplied a method for propagating mixes of these values through rational formulas. [59]
Artificial intelligence
Symbolic device learning approaches were examined to resolve the understanding acquisition bottleneck. Among the earliest is Meta-DENDRAL. Meta-DENDRAL used a generate-and-test strategy to generate possible guideline hypotheses to check versus spectra. Domain and job knowledge minimized the number of prospects checked to a manageable size. Feigenbaum described Meta-DENDRAL as
… the culmination of my imagine the early to mid-1960s having to do with 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 understanding to guide and prune the search. That knowledge got in there due to the fact that we interviewed 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 understanding of mass spectrometry that DENDRAL might utilize to solve private hypothesis formation problems. We did it. We were even able to release new knowledge of mass spectrometry in the Journal of the American Chemical Society, giving credit just in a footnote that a program, Meta-DENDRAL, really did it. We had the ability to do something that had been a dream: to have a computer system program come up with a brand-new and publishable piece of science. [51]
In contrast to the knowledge-intensive technique of Meta-DENDRAL, Ross Quinlan created a domain-independent approach to statistical classification, choice tree knowing, beginning initially with ID3 [60] and then later on extending its capabilities to C4.5. [61] The decision trees produced are glass box, interpretable classifiers, with human-interpretable classification guidelines.
Advances were made in understanding 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 specific, borders encompassing all viable hypotheses constant with the examples seen so far. [62] More officially, Valiant introduced Probably Approximately Correct Learning (PAC Learning), a framework for the mathematical analysis of device learning. [63]
Symbolic device discovering incorporated more than finding out by example. E.g., John Anderson supplied a cognitive design of human learning where ability practice leads to a compilation of guidelines from a declarative format to a procedural format with his ACT-R cognitive architecture. For instance, a trainee may discover to apply “Supplementary angles are two angles whose measures sum 180 degrees” as numerous different procedural guidelines. E.g., one guideline may say that if X and Y are additional and you know X, then Y will be 180 – X. He called his approach “knowledge compilation”. ACT-R has been used successfully to model elements of human cognition, such as finding out and retention. ACT-R is also used in intelligent tutoring systems, called cognitive tutors, to successfully teach geometry, computer system programs, and algebra to school kids. [64]
Inductive logic programming was another approach to learning that permitted reasoning programs to be manufactured from input-output examples. E.g., Ehud Shapiro’s MIS (Model Inference System) might synthesize Prolog programs from examples. [65] John R. Koza applied hereditary algorithms to program synthesis to develop hereditary shows, which he used to synthesize LISP programs. Finally, Zohar Manna and Richard Waldinger offered a more general method to program synthesis that synthesizes a practical program in the course of showing its specifications to be correct. [66]
As an option to logic, Roger Schank presented case-based reasoning (CBR). The CBR approach laid out in his book, Dynamic Memory, [67] focuses initially on remembering essential analytical cases for future use and generalizing them where appropriate. When confronted with a brand-new problem, CBR recovers the most similar previous case and adapts it to the specifics of the existing problem. [68] Another option to reasoning, hereditary algorithms and genetic shows are based upon an evolutionary model of learning, where sets of rules are encoded into populations, the rules 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 concepts, guidelines, heuristics, and problem-solving. Approaches, other than those above, include:
1. Learning from instruction or advice-i.e., taking human guideline, impersonated recommendations, and determining how to operationalize it in specific scenarios. For example, in a video game of Hearts, finding out exactly how to play a hand to “prevent taking points.” [70] 2. Learning from exemplars-improving performance by accepting subject-matter expert (SME) feedback during training. When problem-solving stops working, querying the expert to either find out a new exemplar for problem-solving or to find out a new description as to exactly why one exemplar is more pertinent than another. For example, the program Protos found out to diagnose tinnitus cases by communicating with an audiologist. [71] 3. Learning by analogy-constructing issue options based on similar problems seen in the past, and after that customizing their services to fit a brand-new situation or domain. [72] [73] 4. Apprentice knowing systems-learning unique services to problems by observing human analytical. Domain knowledge discusses why novel options are proper and how the solution can be generalized. LEAP discovered how to develop VLSI circuits by observing human designers. [74] 5. Learning by discovery-i.e., developing jobs to perform experiments and after that finding out from the outcomes. Doug Lenat’s Eurisko, for example, discovered heuristics to beat human players at the Traveller role-playing video game for two years in a row. [75] 6. Learning macro-operators-i.e., looking for beneficial macro-operators to be gained from series of basic problem-solving actions. Good macro-operators simplify problem-solving by enabling issues to be solved at a more abstract level. [76]
Deep knowing and neuro-symbolic AI 2011-now
With the increase of deep knowing, the symbolic AI method has actually been compared to deep learning as complementary “… with parallels having actually been drawn sometimes by AI scientists in between Kahneman’s research on human thinking 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 modelled by deep learning and symbolic reasoning, respectively.” In this view, symbolic thinking is more apt for deliberative thinking, planning, and description while deep learning is more apt for fast pattern recognition in affective applications with noisy information. [17] [18]
Neuro-symbolic AI: integrating neural and symbolic approaches
Neuro-symbolic AI efforts to integrate neural and symbolic architectures in a way that addresses strengths and weak points of each, in a complementary fashion, in order to support robust AI efficient in reasoning, finding out, and cognitive modeling. As argued by Valiant [77] and lots of others, [78] the efficient building of abundant computational cognitive designs requires the combination of sound symbolic thinking and efficient (device) knowing models. Gary Marcus, similarly, argues that: “We can not build abundant cognitive models in a sufficient, automatic method without the set of three of hybrid architecture, rich anticipation, and sophisticated techniques for thinking.”, [79] and in specific: “To develop a robust, knowledge-driven method to AI we need to have the equipment of symbol-manipulation in our toolkit. Too much of useful knowledge is abstract to make do without tools that represent and manipulate abstraction, and to date, the only equipment that we understand of that can control such abstract knowledge dependably is the apparatus of sign adjustment. ” [80]
Henry Kautz, [19] Francesca Rossi, [81] and Bart Selman [82] have likewise argued for a synthesis. Their arguments are based on a requirement to deal with the two type of thinking gone over in Daniel Kahneman’s book, Thinking, Fast and Slow. Kahneman describes human thinking as having 2 parts, System 1 and System 2. System 1 is quickly, automatic, intuitive and unconscious. System 2 is slower, step-by-step, and explicit. System 1 is the kind used for pattern recognition while System 2 is far much better suited for planning, reduction, and deliberative thinking. In this view, deep learning finest designs the first type of thinking while symbolic reasoning finest models the 2nd kind and both are needed.
Garcez and Lamb describe research study in this area as being ongoing for at least the past twenty years, [83] dating from their 2002 book on neurosymbolic knowing systems. [84] A series of workshops on neuro-symbolic reasoning has actually been held every year since 2005, see http://www.neural-symbolic.org/ for information.
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 fairly little research study neighborhood over the last twenty years and has actually yielded several substantial outcomes. Over the last decade, neural symbolic systems have been revealed capable of getting rid of the so-called propositional fixation of neural networks, as McCarthy (1988) put it in response to Smolensky (1988 ); see also (Hinton, 1990). Neural networks were revealed capable of representing modal and temporal reasonings (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 actually been applied to a number of issues in the areas of bioinformatics, control engineering, software application confirmation and adjustment, visual intelligence, ontology knowing, and computer video games. [78]
Approaches for integration are differed. Henry Kautz’s taxonomy of neuro-symbolic architectures, together with some examples, follows:
– Symbolic Neural symbolic-is the existing technique of lots of neural designs in natural language processing, where words or subword tokens are both the supreme input and output of big language designs. Examples consist of BERT, RoBERTa, and GPT-3.
– Symbolic [Neural] -is exhibited by AlphaGo, where symbolic techniques are utilized to call neural methods. In this case the symbolic method is Monte Carlo tree search and the neural strategies find out how to examine video game positions.
– Neural|Symbolic-uses a neural architecture to translate affective data as signs and relationships that are then reasoned about symbolically.
– Neural: Symbolic → Neural-relies on symbolic reasoning to generate or identify training information that is consequently learned by a deep learning design, e.g., to train a neural model for symbolic computation by using a Macsyma-like symbolic mathematics system to create or label examples.
– Neural _ Symbolic -uses a neural web that is produced 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 guidelines and terms. Logic Tensor Networks [86] likewise fall under this classification.
– Neural [Symbolic] -allows a neural model to straight call a symbolic thinking engine, e.g., to perform an action or assess a state.
Many crucial research study concerns remain, 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 knowledge be discovered and reasoned about?
– How can abstract understanding that is tough to encode logically be handled?
Techniques and contributions
This section offers a summary of methods and contributions in an overall context resulting in numerous other, more detailed posts in Wikipedia. Sections on Artificial Intelligence and Uncertain Reasoning are covered previously in the history area.
AI shows languages
The crucial AI programs language in the US during the last symbolic AI boom period was LISP. LISP is the 2nd earliest programs language after FORTRAN and was produced in 1958 by John McCarthy. LISP offered the first read-eval-print loop to support rapid program advancement. Compiled functions could be freely blended with interpreted functions. Program tracing, stepping, and breakpoints were likewise supplied, together with the ability to alter values or functions and continue from breakpoints or mistakes. It had the very first self-hosting compiler, implying that the compiler itself was initially composed in LISP and after that ran interpretively to assemble the compiler code.
Other crucial developments originated by LISP that have spread to other programs languages consist of:
Garbage collection
Dynamic typing
Higher-order functions
Recursion
Conditionals
Programs were themselves data structures that other programs might run on, permitting the easy definition of higher-level languages.
In contrast to the US, in Europe the key AI programming language during that same duration was Prolog. Prolog supplied an integrated store of realities and clauses that might be queried by a read-eval-print loop. The shop might function as a knowledge base and the stipulations might act as rules or a limited type of reasoning. As a subset of first-order reasoning Prolog was based on Horn stipulations with a closed-world assumption-any facts not known were considered false-and a special name presumption for primitive terms-e.g., the identifier barack_obama was considered to describe precisely one item. Backtracking and unification are integrated to Prolog.
Alain Colmerauer and Philippe Roussel are credited as the developers of Prolog. Prolog is a type of reasoning programs, which was developed by Robert Kowalski. Its history was likewise influenced by Carl Hewitt’s PLANNER, an assertional database with pattern-directed invocation of methods. For more information see the area on the origins of Prolog in the PLANNER short article.
Prolog is also a sort of declarative programming. The logic stipulations that explain programs are straight translated to run the programs specified. No explicit series of actions is required, as holds true with essential programming languages.
Japan championed Prolog for its Fifth Generation Project, intending to build special hardware for high performance. Similarly, LISP machines were developed to run LISP, however as the 2nd AI boom turned to bust these business might not compete with brand-new workstations that might now run LISP or Prolog natively at comparable speeds. See the history section for more information.
Smalltalk was another influential AI programming language. For instance, it presented metaclasses and, in addition to Flavors and CommonLoops, affected the Common Lisp Object System, or (CLOS), that is now part of Common Lisp, the existing basic Lisp dialect. CLOS is a Lisp-based object-oriented system that enables numerous inheritance, in addition to incremental extensions to both classes and metaclasses, hence supplying a run-time meta-object protocol. [88]
For other AI shows languages see this list of shows languages for expert system. Currently, Python, a multi-paradigm shows language, is the most popular programming language, partially due to its extensive package library that supports data science, natural language processing, and deep knowing. Python consists of a read-eval-print loop, practical elements such as higher-order functions, and object-oriented programs that consists of metaclasses.
Search
Search occurs in numerous sort of issue solving, including preparation, restriction satisfaction, and playing video games such as checkers, chess, and go. The very best understood 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 games, alpha-beta pruning, branch and bound, and minimax were early contributions.
Knowledge representation and thinking
Multiple various techniques to represent knowledge and then factor with those representations have been investigated. Below is a quick overview of methods to understanding representation and automated reasoning.
Knowledge representation
Semantic networks, conceptual graphs, frames, and reasoning are all methods to modeling understanding such as domain understanding, problem-solving knowledge, and the semantic meaning of language. Ontologies model crucial ideas 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 also be considered as an ontology. YAGO includes WordNet as part of its ontology, to line up facts drawn out from Wikipedia with WordNet synsets. The Disease Ontology is an example of a medical ontology presently being used.
Description logic is a reasoning for automated category of ontologies and for detecting inconsistent category data. OWL is a language utilized to represent ontologies with description logic. Protégé is an ontology editor that can read 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 logic. Horn stipulation reasoning is more restricted than first-order logic and is utilized in reasoning programs languages such as Prolog. Extensions to first-order logic include temporal reasoning, to deal with time; epistemic reasoning, to factor about representative knowledge; modal logic, to handle possibility and need; and probabilistic reasonings to deal with reasoning and possibility together.
Automatic theorem proving
Examples of automated theorem provers for first-order reasoning are:
Prover9.
ACL2.
Vampire.
Prover9 can be utilized in combination with the Mace4 design checker. ACL2 is a theorem prover that can manage proofs 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, typically of rules, to boost reusability across domains by separating procedural code and domain understanding. A separate inference engine processes rules and includes, deletes, or modifies a knowledge store.
Forward chaining inference engines are the most common, and are seen in CLIPS and OPS5. Backward chaining occurs in Prolog, where a more limited logical representation is utilized, Horn Clauses. Pattern-matching, particularly marriage, is utilized in Prolog.
A more versatile sort of problem-solving takes place when reasoning about what to do next occurs, instead of just picking among the offered actions. This type of meta-level reasoning is utilized in Soar and in the BB1 chalkboard architecture.
Cognitive architectures such as ACT-R may have additional capabilities, such as the capability to put together frequently used understanding into higher-level chunks.
Commonsense reasoning
Marvin Minsky first proposed frames as a way of interpreting common visual situations, such as an office, and Roger Schank extended this concept to scripts for common routines, such as eating in restaurants. Cyc has actually tried to catch beneficial common-sense understanding and has “micro-theories” to deal with particular sort of domain-specific thinking.
Qualitative simulation, such as Benjamin Kuipers’s QSIM, [90] estimates human thinking about ignorant physics, such as what occurs when we warm a liquid in a pot on the range. We anticipate it to heat and possibly boil over, although we may not know its temperature level, its boiling point, or other details, such as atmospheric pressure.
Similarly, Allen’s temporal period algebra is a simplification of thinking about time and Region Connection Calculus is a simplification of thinking about spatial relationships. Both can be solved with restraint solvers.
Constraints and constraint-based thinking
Constraint solvers carry out a more restricted sort of reasoning than first-order logic. They can simplify sets of spatiotemporal restraints, such as those for RCC or Temporal Algebra, in addition to fixing other type of puzzle issues, such as Wordle, Sudoku, cryptarithmetic problems, and so on. Constraint reasoning programs can be used to solve scheduling problems, for example with constraint managing guidelines (CHR).
Automated planning
The General Problem Solver (GPS) cast preparation as analytical used means-ends analysis to develop plans. STRIPS took a different technique, viewing preparation as theorem proving. Graphplan takes a least-commitment method to planning, instead of sequentially choosing actions from an initial state, working forwards, or an objective state if working in reverse. Satplan is an approach to planning where a preparation issue is decreased to a Boolean satisfiability issue.
Natural language processing
Natural language processing focuses on dealing with language as information to carry out jobs such as determining topics without necessarily comprehending the desired meaning. Natural language understanding, on the other hand, constructs a significance representation and utilizes that for further processing, such as responding to questions.
Parsing, tokenizing, spelling correction, part-of-speech tagging, noun and verb phrase chunking are all elements of natural language processing long handled by symbolic AI, but considering that improved by deep knowing approaches. In symbolic AI, discourse representation theory and first-order reasoning have actually been used to represent sentence significances. Latent semantic analysis (LSA) and specific semantic analysis likewise offered vector representations of documents. In the latter case, vector components are interpretable as ideas called by Wikipedia posts.
New deep knowing methods based upon Transformer models have actually now eclipsed these earlier symbolic AI approaches and achieved state-of-the-art performance in natural language processing. However, Transformer designs are opaque and do not yet produce human-interpretable semantic representations for sentences and documents. Instead, they produce task-specific vectors where the significance of the vector parts is opaque.
Agents and multi-agent systems
Agents are self-governing systems embedded in an environment they view and act upon in some sense. Russell and Norvig’s basic textbook on expert system is organized to reflect representative architectures of increasing elegance. [91] The elegance of representatives varies from simple reactive representatives, to those with a design of the world and automated preparation capabilities, possibly a BDI representative, i.e., one with beliefs, desires, and objectives – or additionally a support finding out model discovered with time to choose actions – up to a mix of alternative architectures, such as a neuro-symbolic architecture [87] that includes deep knowing for understanding. [92]
On the other hand, a multi-agent system includes numerous representatives that communicate amongst themselves with some inter-agent communication language such as Knowledge Query and Manipulation Language (KQML). The agents need not all have the exact same internal architecture. Advantages of multi-agent systems consist of the ability to divide work amongst the agents and to increase fault tolerance when representatives are lost. Research problems consist of how agents reach agreement, distributed problem fixing, multi-agent learning, multi-agent preparation, and distributed restraint optimization.
Controversies occurred 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 between those who accepted AI but declined symbolic approaches-primarily connectionists-and those outside the field. Critiques from outside of the field were primarily from philosophers, on intellectual grounds, but also from financing agencies, especially throughout the two AI winter seasons.
The Frame Problem: knowledge representation obstacles for first-order reasoning
Limitations were discovered in using basic first-order logic to factor about vibrant domains. Problems were found both with concerns to enumerating the preconditions for an action to succeed and in providing axioms for what did not alter after an action was performed.
McCarthy and Hayes presented the Frame Problem in 1969 in the paper, “Some Philosophical Problems from the Standpoint of Expert System.” [93] A basic example takes place in “showing that one individual could enter into 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 deduction to prosper. Similar axioms would be needed for other domain actions to define what did not alter.
A similar issue, called the Qualification Problem, takes place in attempting to mention the prerequisites for an action to be successful. An infinite number of pathological conditions can be pictured, e.g., a banana in a tailpipe might prevent a vehicle from running correctly.
McCarthy’s approach to fix the frame problem was circumscription, a sort of non-monotonic reasoning where deductions could be made from actions that need just specify what would change while not needing to clearly specify whatever that would not alter. Other non-monotonic reasonings supplied reality maintenance systems that revised beliefs leading to contradictions.
Other ways of managing more open-ended domains consisted of probabilistic reasoning systems and artificial intelligence to learn new concepts and guidelines. McCarthy’s Advice Taker can be seen as a motivation here, as it might integrate brand-new knowledge provided by a human in the kind of assertions or guidelines. For instance, speculative symbolic maker discovering systems explored the ability to take top-level natural language guidance and to analyze it into domain-specific actionable rules.
Similar to the problems in handling vibrant domains, common-sense thinking is also challenging to record in formal reasoning. Examples of sensible thinking include implicit thinking about how individuals believe or basic understanding of daily events, items, and living creatures. This sort of knowledge is taken for given and not considered as noteworthy. Common-sense reasoning is an open location of research study and challenging both for symbolic systems (e.g., Cyc has attempted to record essential parts of this knowledge over more than a decade) and neural systems (e.g., self-driving cars and trucks that do not understand not to drive into cones or not to hit pedestrians strolling a bike).
McCarthy viewed his Advice Taker as having sensible, but his definition of sensible was different than the one above. [94] He defined a program as having typical sense “if it automatically deduces for itself a sufficiently wide class of immediate repercussions of anything it is told and what it already understands. “
Connectionist AI: philosophical challenges and sociological disputes
Connectionist methods consist of earlier deal with neural networks, [95] such as perceptrons; work 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 operate in deep knowing.
Three philosophical positions [96] have been described amongst connectionists:
1. Implementationism-where connectionist architectures execute the abilities for symbolic processing,
2. Radical connectionism-where symbolic processing is declined absolutely, and connectionist architectures underlie intelligence and are fully sufficient to discuss it,
3. Moderate connectionism-where symbolic processing and connectionist architectures are viewed as complementary and both are required for intelligence
Olazaran, in his sociological history of the controversies within the neural network neighborhood, described the moderate connectionism consider as essentially suitable with current research study in neuro-symbolic hybrids:
The 3rd and last position I would like to examine here is what I call the moderate connectionist view, a more eclectic view of the existing dispute between connectionism and symbolic AI. One of the researchers who has actually elaborated this position most explicitly is Andy Clark, a theorist from the School of Cognitive and Computing Sciences of the University of Sussex (Brighton, England). Clark protected hybrid (partly symbolic, partially connectionist) systems. He declared that (at least) two kinds of theories are needed in order to study and model cognition. On the one hand, for some information-processing jobs (such as pattern recognition) connectionism has advantages over symbolic models. But on the other hand, for other cognitive procedures (such as serial, deductive reasoning, and generative sign adjustment processes) the symbolic paradigm provides appropriate models, and not just “approximations” (contrary to what radical connectionists would claim). [97]
Gary Marcus has actually declared that the animus in the deep knowing community against symbolic approaches now may 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 current AI earnings. Hinton and lots of others have attempted hard to banish signs entirely. The deep learning hope-seemingly grounded not so much in science, but in a sort of historic grudge-is that smart behavior will emerge simply from the confluence of massive data and deep knowing. Where classical computer systems and software solve tasks by defining sets of symbol-manipulating guidelines devoted to specific tasks, such as modifying a line in a word processor or performing an estimation in a spreadsheet, neural networks usually try to resolve jobs by statistical approximation and gaining from examples.
According to Marcus, Geoffrey Hinton and his colleagues have actually been vehemently “anti-symbolic”:
When deep learning reemerged in 2012, it was with a kind of take-no-prisoners mindset that has actually identified most of the last decade. By 2015, his hostility toward all things signs had totally taken shape. He provided a talk at an AI workshop at Stanford comparing signs to aether, among science’s biggest mistakes.
…
Since then, his anti-symbolic campaign has actually just increased in strength. In 2016, Yann LeCun, Bengio, and Hinton composed a manifesto for deep learning in one of science’s crucial journals, Nature. It closed with a direct attack on symbol adjustment, calling not for reconciliation but for outright replacement. Later, Hinton told a gathering of European Union leaders that investing any further money in symbol-manipulating approaches was “a big mistake,” comparing it to investing in internal combustion engines in the age of electric automobiles. [98]
Part of these disputes may be because of uncertain terms:
Turing award winner Judea Pearl uses a review of machine knowing which, sadly, conflates the terms artificial intelligence and deep knowing. Similarly, when Geoffrey Hinton refers to symbolic AI, the connotation of the term tends to be that of specialist systems dispossessed of any ability to find out. Making use of the terms is in requirement of explanation. Machine knowing is not confined to association guideline mining, c.f. the body of work on symbolic ML and relational learning (the differences to deep knowing being the choice of representation, localist sensible rather than dispersed, and the non-use of gradient-based learning algorithms). Equally, symbolic AI is not almost production rules composed by hand. A proper meaning of AI concerns understanding representation and reasoning, autonomous multi-agent systems, planning and argumentation, in addition to knowing. [99]
Situated robotics: the world as a design
Another review of symbolic AI is the embodied cognition method:
The embodied cognition method declares that it makes no sense to think about the brain independently: cognition takes place within a body, which is embedded in an environment. We need to study the system as a whole; the brain’s working exploits consistencies in its environment, including the rest of its body. Under the embodied cognition approach, robotics, vision, and other sensing units become central, not peripheral. [100]
Rodney Brooks invented behavior-based robotics, one method to embodied cognition. Nouvelle AI, another name for this method, is deemed an alternative to both symbolic AI and connectionist AI. His method declined representations, either symbolic or distributed, as not only unnecessary, however as detrimental. Instead, he created the subsumption architecture, a layered architecture for embodied agents. Each layer attains a different function and needs to operate in the real life. For instance, the first robot he explains in Intelligence Without Representation, has three layers. The bottom layer analyzes sonar sensors to prevent things. The middle layer causes the robotic to roam around when there are no challenges. The top layer causes the robotic to go to more distant locations for further exploration. Each layer can momentarily prevent or suppress a lower-level layer. He slammed AI researchers for defining AI problems for their systems, when: “There is no tidy division between understanding (abstraction) and thinking in the real life.” [101] He called his robotics “Creatures” and each layer was “composed of a fixed-topology network of easy finite state devices.” [102] In the Nouvelle AI technique, “First, it is critically important to check the Creatures we construct in the real life; i.e., in the very same world that we human beings occupy. It is devastating to fall into the temptation of testing them in a simplified world initially, even with the very best intentions of later transferring activity to an unsimplified world.” [103] His emphasis on real-world testing was in contrast to “Early operate in AI focused on games, geometrical issues, symbolic algebra, theorem proving, and other official systems” [104] and making use of the blocks world in symbolic AI systems such as SHRDLU.
Current views
Each approach-symbolic, connectionist, and behavior-based-has benefits, but has actually been criticized by the other techniques. Symbolic AI has been slammed as disembodied, liable to the credentials problem, and bad in handling the perceptual issues where deep finding out excels. In turn, connectionist AI has been slammed as inadequately matched for deliberative detailed problem resolving, including knowledge, and dealing with preparation. Finally, Nouvelle AI masters reactive and real-world robotics domains but has actually been slammed for problems in integrating learning and understanding.
Hybrid AIs incorporating one or more of these approaches are presently viewed as the course forward. [19] [81] [82] Russell and Norvig conclude that:
Overall, Dreyfus saw areas where AI did not have total responses and stated that Al is for that reason impossible; we now see a number of these very same areas undergoing continued research study and development leading to increased ability, not impossibility. [100]
Expert system.
Automated planning and scheduling
Automated theorem proving
Belief revision
Case-based reasoning
Cognitive architecture
Cognitive science
Connectionism
Constraint shows
Deep learning
First-order logic
GOFAI
History of expert system
Inductive reasoning programming
Knowledge-based systems
Knowledge representation and thinking
Logic programming
Artificial intelligence
Model monitoring
Model-based reasoning
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 when said: “This is AI, so we do not care if it’s psychologically real”. [4] McCarthy repeated his position in 2006 at the AI@50 conference where he said “Artificial intelligence is not, by definition, simulation of human intelligence”. [28] Pamela McCorduck writes that there are “2 significant branches of expert system: one targeted at producing smart habits despite how it was achieved, and the other targeted at modeling smart procedures found in nature, particularly human ones.”, [29] Stuart Russell and Peter Norvig composed “Aeronautical engineering texts do not define the goal of their field as making ‘devices that fly so exactly like pigeons that they can fool 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 expert system: representing items 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 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.
^ McCorduck 2004, p. 489.
^ 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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