Showing posts with label General Problem Solver. Show all posts
Showing posts with label General Problem Solver. Show all posts

AI Glossary - What Is ARF?


R.R. Fikes created ARF in the late 1960s as a general problem solver.

It used a combination of constraint-satisfaction and heuristic searches.

Fikes also created REF, a problem-statement language for ARF. 

REF-ARF is a procedure-based approach for addressing problems. 

The paper by Fikes presents an attempt to create a heuristic problem-solving software that takes issues expressed in a nondeterministic programming language and finds solutions using constraint fulfillment and heuristic search approaches. 

The usage of nondeterministic programming languages for presenting issues is examined, as well as ref, the language that the problem solver ARF accepts.

Different ref extensions are examined. 

The program's basic framework is detailed in depth, and several options for expanding it are examined. 

In sixteen example problems, the usage of the input language and the behavior of the program are presented and investigated.

The paper discusses Ref2 and POPS, two heuristic problem-solving algorithms. 

Both systems take issues expressed as nondeterministic programming language programs and solve them using heuristic approaches to locate successful program executions. 

Ref2 is built on Richard Fikes' REF-ARF system and includes REF-issue-solving ARF's techniques as well as new methods based on a different representation for the problem context. 

Ref2 may also handle issues involving integer programming. POPS is an updated and expanded version of Ref2 that incorporates goal-directed procedures based on GPS ideas.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

Artificial Intelligence - Who Was Herbert A. Simon?


Herbert A. Simon (1916–2001) was a multidisciplinary scholar who contributed significantly to artificial intelligence.

He is largely regarded as one of the twentieth century's most prominent social scientists.

His contributions at Carnegie Mellon University lasted five decades.

Early artificial intelligence research was driven by the idea of the computer as a symbol manipulator rather than a number cruncher.

Emil Post, who originally wrote about this sort of computational model in 1943, is credited with inventing production systems, which included sets of rules for symbol strings used to establish conditions—which must exist before rules can be applied—and the actions to be done or conclusions to be drawn.

Simon and his Carnegie Mellon colleague Allen Newell popularized these theories regarding symbol manipulation and production systems by praising their potential benefits for general-purpose reading, storing, and replicating, as well as comparing and contrasting various symbols and patterns.

Simon, Newell, and Cliff Shaw's Logic Theorist software was the first to employ symbol manipulation to construct "intelligent" behavior.

Theorems presented in Bertrand Russell and Alfred North Whitehead's Principia Mathematica (1910) might be independently proved by logic theorists.

Perhaps most notably, the Logic Theorist program uncovered a shorter, more elegant demonstration of Theorem 2.85 in the Principia Mathematica, which was subsequently rejected by the Journal of Symbolic Logic since it was coauthored by a machine.

Although it was theoretically conceivable to prove the Principia Mathematica's theorems in an exhaustively detailed and methodical manner, it was impractical in reality due to the time required.

Newell and Simon were fascinated by the human rules of thumb for solving difficult issues for which an extensive search for answers was impossible due to the massive quantities of processing necessary.

They used the term "heuristics" to describe procedures that may solve issues but do not guarantee success.

A heuristic is a "rule of thumb" used to solve a problem that is too difficult or time consuming to address using an exhaustive search, a formula, or a step-by-step method.

Heuristic approaches are often compared with algorithmic methods in computer science, with the result of the method being a significant differentiating element.

According to this contrast, a heuristic program will provide excellent results in most cases, but not always, while an algorithmic program is a clear technique that guarantees a solution.

This is not, however, a technical difference.

In fact, a heuristic procedure that consistently yields the best result may no longer be deemed "heuristic"—alpha-beta pruning is an example of this.

Simon's heuristics are still utilized by programmers who are trying to solve issues that demand a lot of time and/or memory.

The game of chess is one such example, in which an exhaustive search of all potential board configurations for the proper solution is beyond the human mind's or any computer's capabilities.

Indeed, for artificial intelligence research, Herbert Simon and Allen Newell referred to computer chess as the Drosophila or fruit fly.

Heuristics may also be used to solve issues that don't have a precise answer, such as in medical diagnosis, when heuristics are applied to a collection of symptoms to determine the most probable diagnosis.

Production rules are derived from a class of cognitive science models that apply heuristic principles to productions (situations).

In practice, these rules reduce down to "IF-THEN" statements that reflect specific preconditions or antecedents, as well as the conclusions or consequences that these preconditions or antecedents justify.

"IF there are two X's in a row, THEN put an O to block," is a frequent example offered for the application of production rules to the tic-tac-toe game.

These IF-THEN statements are incorporated into expert systems' inference mechanisms so that a rule interpreter can apply production rules to specific situations lodged in the context data structure or short-term working memory buffer containing information supplied about that situation and draw conclusions or make recommendations.

Production rules were crucial in the development of artificial intelligence as a discipline.

Joshua Lederberg, Edward Feigenbaum, and other Stanford University partners would later use this fundamental finding to develop DENDRAL, an expert system for detecting molecular structure, in the 1960s.

These production guidelines were developed in DENDRAL after discussions between the system's developers and other mass spectrometry specialists.

Edward Shortliffe, Bruce Buchanan, and Edward Feigenbaum used production principles to create MYCIN in the 1970s.

MYCIN has over 600 IFTHEN statements in it, each reflecting domain-specific knowledge about microbial illness diagnosis and treatment.

PUFF, EXPERT, PROSPECTOR, R1, and CLAVIER were among the several production rule systems that followed.

Simon, Newell, and Shaw demonstrated how heuristics may overcome the drawbacks of classical algorithms, which promise answers but take extensive searches or heavy computing to find.

A process for solving issues in a restricted, clear sequence of steps is known as an algorithm.

Sequential operations, conditional operations, and iterative operations are the three kinds of fundamental instructions required to create computable algorithms.

Sequential operations perform tasks in a step-by-step manner.

The algorithm only moves on to the next job when each step is completed.

Conditional operations are made up of instructions that ask questions and then choose the next step dependent on the response.

One kind of conditional operation is the "IF-THEN" expression.

Iterative operations run "loops" of instructions.

These statements tell the task flow to go back and repeat a previous series of statements in order to solve an issue.

Algorithms are often compared to cookbook recipes, in which a certain order and execution of actions in the manufacture of a product—in this example, food—are dictated by a specific sequence of set instructions.

Newell, Shaw, and Simon created list processing for the Logic Theorist software in 1956.

List processing is a programming technique for allocating dynamic storage.

It's mostly utilized in symbol manipulation computer applications like compiler development, visual or linguistic data processing, and artificial intelligence, among others.

Allen Newell, J. Clifford Shaw, and Herbert A. Simon are credited with creating the first list processing software with enormous, sophisticated, and flexible memory structures that were not reliant on subsequent computer/machine memory.

List processing techniques are used in a number of higher-order languages.

IPL and LISP, two artificial intelligence languages, are the most well-known.

Simon and Newell's Generic Problem Solver (GPS) was published in the early 1960s, and it thoroughly explains the essential properties of symbol manipulation as a general process that underpins all types of intelligent problem-solving behavior.

GPS formed the foundation for decades of early AI research.

To arrive at a solution, General Problem Solver is a software for a problem-solving method that employs means-ends analysis and planning.

GPS was created with the goal of separating the problem-solving process from information particular to the situation at hand, allowing it to be used to a wide range of issues.

Simon is an economist, a political scientist, and a cognitive psychologist.

Simon is known for the notions of limited rationality, satisficing, and power law distributions in complex systems, in addition to his important contributions to organizational theory, decision-making, and problem-solving.

Computer and data scientists are interested in all three themes.

Human reasoning is inherently constrained, according to bounded rationality.

Humans lack the time or knowledge required to make ideal judgments; problems are difficult, and the mind has cognitive limitations.

Satisficing is a term used to describe a decision-making process that produces a solution that "satisfies" and "suffices," rather than the most ideal answer.

Customers use satisficing in market conditions when they choose things that are "good enough," meaning sufficient or acceptable.

Simon described how power law distributions were obtained from preferred attachment mechanisms in his study on complex organizations.

When a relative change in one variable induces a proportionate change in another, power laws, also known as scaling laws, come into play.

A square is a simple illustration; when the length of a side doubles, the square's area quadruples.

Power laws may be found in biological systems, fractal patterns, and wealth distributions, among other things.

Preferential attachment processes explain why the affluent grow wealthier in income/wealth distributions: Individuals' wealth is dispersed according on their current level of wealth; those with more wealth get proportionately more income, and hence greater overall wealth, than those with less.

When graphed, such distributions often create so-called long tails.

These long-tailed distributions are being employed to explain crowdsourcing, microfinance, and online marketing, among other things.

Simon was born in Milwaukee, Wisconsin, to a Jewish electrical engineer with multiple patents who came from Germany in the early twentieth century.

His mother was a musical prodigy. Simon grew interested in the social sciences after reading books on psychology and economics written by an uncle.

He has said that two works inspired his early thinking on the subjects: Norman Angell's The Great Illusion (1909) and Henry George's Progress and Poverty (1879).

Simon obtained his doctorate in organizational decision-making from the University of Chicago in 1943.

Rudolf Carnap, Harold Lasswell, Edward Merriam, Nicolas Rashevsky, and Henry Schultz were among his instructors.

He started his career as a political science professor at the Illinois Institute of Technology, where he taught and conducted research.

In 1949, he transferred to Carnegie Mellon University, where he stayed until 2001.

He progressed through the ranks of the Department of Industrial Management to become its chair.

He has written twenty-seven books and several articles that have been published.

In 1959, he was elected a member of the American Academy of Arts and Sciences.

In 1975, Simon was awarded the coveted Turing Award, and in 1978, he was awarded the Nobel Prize in Economics.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

Dartmouth AI Conference; Expert Systems; General Problem Solver; Newell, Allen.

References & Further Reading:

Crowther-Heyck, Hunter. 2005. Herbert A. Simon: The Bounds of Reason in Modern America. Baltimore: Johns Hopkins Press.

Newell, Allen, and Herbert A. Simon. 1956. The Logic Theory Machine: A Complex Information Processing System. Santa Monica, CA: The RAND Corporation.

Newell, Allen, and Herbert A. Simon. 1976. “Computer Science as Empirical Inquiry: Symbols and Search.” Communications of the ACM 19, no. 3: 113–26.

Simon, Herbert A. 1996. Models of My Life. Cambridge, MA: MIT Press.

Artificial Intelligence - Who Was Allen Newell?


Allen Newell (1927–1992) was an American writer who lived from 1927 to 1992.

 Allen In the late 1950s and early 1960s, Newell collaborated with Herbert Simon to develop the earliest models of human cognition.

The Logic Theory Machine depicted how logical rules might be used in a proof, the General Problem Solver modeled how basic problem solving could be done, and an early chess software mimicked how to play chess (the Newell-Shaw-Simon chess program).

Newell and Simon demonstrated for the first time in these models how computers can modify symbols and how these manipulations may be used to describe, produce, and explain intelligent behavior.

Newell began his career at Stanford University as a physics student.

He joined to the RAND Corporation to work on complex system models after a year of graduate studies in mathematics at Princeton.

He met and was inspired by Oliver Selfridge while at RAND, who led him to modeling cognition.

He also met Herbert Simon, who would go on to receive the Nobel Prize in Economics for his work on economic decision-making processes, particularly satisficing.

Simon persuaded Newell to attend Carnegie Institute of Technology (now Carnegie Mellon University).

For the most of his academic career, Newell worked with Simon.

Newell's main goal was to simulate the human mind's operations using computer models in order to better comprehend it.

Newell earned his PhD at Carnegie Mellon, where he worked with Simon.

He began his academic career as a tenured and chaired professor.

He was a founding member of the Department of Computer Science (today known as the school), where he held his major position.

With Simon, Newell examined the mind, especially problem solving, as part of his major line of study.

Their book Human Problem Solving, published in 1972, outlined their idea of intelligence and included examples from arithmetic problems and chess.

To assess what resources are being utilized in cognition, they employed a lot of verbal talk-aloud proto cols, which are more accurate than think-aloud or retrospective protocols.

Ericsson and Simon eventually documented the science of verbal protocol data in more detail.

In his final lecture ("Desires and Diversions"), he stated that if you're going to be distracted, you should make the most of it.

He accomplished this via remarkable accomplishments in the areas of his diversions, as well as the use of some of them in his final effort.

One of the early hypertext systems, ZOG, was one of these diversions.

Newell also collaborated with Digital Equipment Corporation (DEC) founder Gordon Bell on a textbook on computer architectures and worked on voice recognition systems with CMU colleague Raj Reddy.

Working with Stuart Card and Thomas Moran at Xerox PARC to develop ideas of how people interact with computers was maybe the longest-running and most fruitful diversion.

The Psychology of Human-Computer Interaction documents these theories (1983).

Their study resulted in the Keystroke Level Model and GOMS, two models for representing human behavior, as well as the Model Human Processor, a simplified description of the mechanics of cognition in this domain.

Some of the first work in human-computer interface was done here (HCI).

Their strategy advocated for first knowing the user and the task, then employing technology to assist the user in completing the job.

In his farewell talk, Newell also said that scientists should have a last endeavor that would outlive them.

Newell's last goal was to advocate for unified theories of cognition (UTCs) and to develop Soar, a proposed UTC and example.

His idea imagined what it would be like to have a theory that combined all of psychology's restrictions, facts, and theories into a single unified outcome that could be implemented by a computer program.

Soar continues to be a successful continuing project, despite the fact that it is not yet completed.

While Soar has yet fully unify psychology, it has made significant progress in describing problem solving, learning, and their interactions, as well as how to create autonomous, reactive entities in huge simulations.

He looked into how learning could be modeled as part of his final project (with Paul Rosenbloom).

Later, this project was merged with Soar.

Learning, according to Newell and Rosenbloom, follows a power law of practice, in which the time to complete a task is proportional to the practice (trial) number raised to a small negative power (e.g., Time trial -).

This holds true across a broad variety of activities.

Their explanation was that when tasks were completed in a hierarchical order, what was learnt at the lowest level had the greatest impact on reaction time, but as learning progressed up the hierarchy, it was less often employed and saved less time, thus learning slowed but did not cease.

Newell delivered the William James Lectures at Harvard in 1987.

He detailed what it would take to develop a unified theory in psychology in these lectures.

These lectures were taped and are accessible in CMU's library.

He gave them again the following autumn and turned them into a book (1990).

Soar's representation of cognition is based on searching through issue spaces.

It takes the form of a manufacturing system (using IF-THEN rules).

It makes an effort to use an operator.

Soar recurses with an impasse to solve the issue if it doesn't have one or can't apply it.

As a result, knowledge is represented as operator parts and issue spaces, as well as how to overcome impasses.

As a result, the architecture is how these choices and information may be organized.

Soar models have been employed in a range of cognitive science and AI applications, including military simulations, and systems with up to one million rules have been constructed.

Kathleen Carley, a social scientist at CMU, and Newell discussed how to use these cognitive models to simulate social agents.

Work on Soar continues, notably at the University of Michigan under the direction of John Laird, with a concentration on intelligent agents presently.

In 1975, the ACM A. M. Turing Award was given to Newell and Simon for their contributions to artificial intelligence, psychology of human cognition, and list processing.

Their work is credited with making significant contributions to computer science as an empirical investigation.

Newell has also been inducted into the National Academies of Sciences and Engineering.

He was awarded the National Medal of Science in 1992.

Newell was instrumental in establishing a productive and supportive research group, department, and institution.

His son said at his memorial service that he was not only a great scientist, but also a great father.

His weaknesses were that he was very intelligent, that he worked really hard, and that he had the same opinion of you.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

Dartmouth AI Conference; General Problem Solver; Simon, Herbert A.

References & Further Reading:

Newell, Allen. 1990. Unified Theories of Cognition. Cambridge, MA: Harvard University Press.

Newell, Allen. 1993. Desires and Diversions. Carnegie Mellon University, School of Computer Science. Stanford, CA: University Video Communications.

Simon, Herbert A. 1998. “Allen Newell: 1927–1992.” IEEE Annals of the History of Computing 20, no. 2: 63–76.

Artificial Intelligence - The General Problem Solver Software.


    To arrive at a solution, General Problem Solver is a software for a problem-solving method that employs means-ends analysis and planning.

    The software was created in such a way that the problem-solving process is separated from information unique to the situation at hand, allowing it to be used to a wide range of issues.

    The software, which was first created by Allen Newell and Herbert Simon in 1957, took over a decade to complete.

    George W. Ernst, a graduate student at Newell, wrote the latest edition while doing research for his dissertation in 1966.

    The General Problem Solver arose from Newell and Simon's work on the Logic Theorist, another problem-solving tool.

    The duo likened Logic Theorist's problem-solving approach to that of people solving comparable issues after inventing it.

    They discovered that the logic theorist's method varied significantly from that of humans.

    Newell and Simon developed General Problem Solver using the knowledge on human problem solving obtained from these experiments, hoping that their artificial intelligence work would contribute to a better understanding of human cognitive processes.

    They discovered that human problem-solvers could look at the intended outcome and, using both backward and forward thinking, decide actions they might take to get closer to that outcome, resulting in the development of a solution.

    The General Problem Solver, which Newell and Simon felt was not just reflective of artificial intelligence but also a theory of human cognition, included this mechanism.

    To solve issues, General Problem Solver uses two heuristic techniques: 

    1. means-ends analysis and 
    2. planning.

    As an example of means-ends analysis in action, consider the following: 

    • If a person coveted a certain book, they would want to be in possession of it.
    • The book is currently kept by the library, and they do not have it in their possession.
    • The individual has the ability to eliminate the gap between their existing and ideal states.
    • They may do so by borrowing the book from the library, and they have other alternatives for getting there, including driving.
    • If the book has been checked out by another customer, however, there are possibilities for obtaining it elsewhere.
    • To buy it, the consumer may go to a bookshop or order it online.
    • The individual must next consider the many possibilities open to them.
    • And so on.

    The individual is aware of a number of pertinent activities they may do, and if they select the right ones and carry them out in the right sequence, they will be able to receive the book.

    Means ends analysis in action is the person who chooses and implements suitable activities.

    The programmer sets up the issue as a starting state and a state to be attained when using means-ends analysis with General Problem Solver.

    The difference between these two states is calculated using the General Problem Solver (called objects).

    • Operators that lessen the difference between the two states must also be coded into the General Problem Solver.
    • It picks and implements an operator to address the issue, then assesses if the operation has got it closer to its objective or ideal state.
    • If that's the case, it'll go on to the next operator.
    • If it doesn't work, it may go back and try another operator.
    • The difference between the original state and the target state is decreased to zero by applying operators.
    • The capacity to plan was also held by General Problem Solver.

    General Problem Solver might sketch a solution to the problem by removing the specifics of the operators and the difference between the starting and desired states.

    After a broad solution had been defined, the specifics could be reinserted into the issue, and the subproblems formed by these details could be addressed within the solution guide lines produced during the outlining step.

    Defining an issue and operators to program the General Problem Solver was a time-consuming task for programmers.

    It also meant that, as a theory of human cognition or an example of artificial intelligence, General Problem Solver took for granted the very actions that, in part, represent intelligence, namely the acts of defining a problem and selecting relevant actions (or operations) from an infinite number of possible actions in order to solve the problem.

    In the mid-1960s, Ernst continued to work on General Problem Solver.

    He wasn't interested in human problem-solving procedures; instead, he wanted to discover a way to broaden the scope of General Problem Solver so that it could solve issues outside of the logic domain.

    In his version of General Problem Solver, the intended state or object was expressed as a set of constraints rather than an explicit specification.

    Ernst also altered the form of the operators such that the output of an operator may be written as a function of the starting state or object (the input).

    His updated General Problem Solver was only somewhat successful in solving problems.

    Even on basic situations, it often ran out of memory.

    "We do believe that this specific aggregation of IPL-Vcode should be set to rest, as having done its bit in furthering our knowledge of the mechanics of intelligence," Ernst and Newell proclaimed in the foreword of their 1969 book GPS: A Case Study in Generality and Problem Solving(Ernst and Newell 1969, vii).

    Artificial Intelligence Problem Solving

    The AI reflex agent converts states into actions. 

    When these agents fail to function in an environment where the state of mapping is too vast for the agent to handle, the stated issue is resolved and passed to a problem-solving domain, which divides the huge stored problem into smaller storage areas and resolves them one by one. 

    The targeted objectives will be the final integrated action.

    Different sorts of issue-solving agents are created and used at an atomic level without any internal state observable with a problem-solving algorithm based on the problem and their working area. 

    By describing issues and many solutions, the problem-solving agent executes exactly. 

    So we may say that issue solving is a subset of artificial intelligence that includes a variety of problem-solving approaches such as tree, B-tree, and heuristic algorithms.

    A problem-solving agent is also known as a goal-oriented agent since it is constantly focused on achieving the desired outcomes.

    AI problem-solving steps: 

    The nature of people and their behaviors are intimately related to the AI dilemma. 

    To solve a problem, we require a set of discrete steps, which makes human labor simple. These are the actions that must be taken to fix a problem:

    • Goal formulation is the first and most basic stage in addressing a problem. 

    It arranges discrete stages to establish a target/goals that need some action in order to be achieved. 

    AI agents are now used to formulate the aim. 

    One of the most important processes in issue resolution is problem formulation, which determines what actions should be followed to reach the specified objective. 

    This essential aspect of AI relies on a software agent, which consists of the following components to construct the issue. 

    Components needed to formulate the problem: 

    This state necessitates a beginning state for the challenge, which directs the AI agent toward a certain objective. 

    In this scenario, new methods also use a particular class to solve the issue area. 

    ActionIn this step of issue formulation, all feasible actions are performed using a function with a specified class obtained from the starting state.

    Transition: In this step of issue formulation, the actual action performed by the previous action stage is combined with the final stage to be sent to the next stage.

    Objective test: This step assesses if the integrated transition model accomplished the given goal or not; if it did, halt the action and go to the next stage to estimate the cost of achieving the goal.

    Path costing is a component of problem-solving that assigns a numerical value to the expense of achieving the objective. 

    It necessitates the purchase of all gear, software, and human labor.

    General Problem Solver Overview

    In theory, GPS may solve any issue that can be described as a collection of well-formed formulas (WFFs) or Horn clauses that create a directed graph with one or more sources (that is, axioms) and sinks (that is, desired conclusions). 

    Predicate logic proofs and Euclidean geometry problem spaces are two primary examples of the domain in which GPS may be used. 

    It was based on the theoretical work on logic machines by Simon and Newell. 

    GPS was the first computer software to segregate its issue knowledge (expressed as input data) from its problem-solving method (a generic solver engine). 

    IPL, a third-order programming language, was used to build GPS. 

    While GPS was able to tackle small issues like the Hanoi Towers that could be adequately described, it was unable to handle any real-world problems since search was quickly lost in the combinatorial explosion. 

    Alternatively, the number of "walks" across the inferential digraph became computationally prohibitive. 

    (In fact, even a simple state space search like the Towers of Hanoi may become computationally infeasible, however smart pruning of the state space can be accomplished using basic AI methods like A* and IDA*.)

    In order to solve issues, the user identified objects and procedures that might be performed on them, and GPS created heuristics via means-ends analysis. 

    • It concentrated on the available processes, determining which inputs and outputs were acceptable. 
    • It then established sub goals in order to move closer to the ultimate objective.
    • The GPS concept ultimately developed into the Soar artificial intelligence framework.

    Jai Krishna Ponnappan

    You may also want to read more about Artificial Intelligence here.

    See also: 

    Expert Systems; Simon, Herbert A.

    Frequently Asked Questions:

    In Artificial Intelligence, what is a generic problem solver?

    Herbert Simon, J.C. Shaw, and Allen Newell suggested the General Problem Solver (GPS) as an AI software. It was the first useful computer software in the field of artificial intelligence. It was intended to function as a global problem-solving machine.

    What is the procedure for using the generic issue solver?

    In order to solve issues, the user identified objects and procedures that might be performed on them, and GPS created heuristics via means-ends analysis. It concentrated on the available processes, determining which inputs and outputs were acceptable.

    What exactly did the General Problem Solver accomplish?

    The General Problem Solver (GPS) was their next effort, which debuted in 1957. GPS would use heuristic approaches (modifiable "rules of thumb") repeatedly to a problem and then undertake a "means-ends" analysis at each stage to see whether it was getting closer to the intended answer.

    What are the three key domain universal issue-solving heuristics that Newell and Simon's general problem solver incorporated in 1972?

    According to Newell and Simon (1972), every issue has a problem space that is described by three components: 1) the issue's beginning state; 2) a collection of operators for transforming a problem state; 3) a test to see whether a problem state is a solution.

    What is heuristic search and how does it work?

    Heuristic search is a kind of strategy for finding the best solution to a problem by searching a solution space. The heuristic here use some mechanism for searching the solution space while determining where the solution is most likely to be found and concentrating the search on that region.

    What are the elements of a broad issue?

    The issue itself, articulated clearly and with sufficient context to explain why it is significant; the way of fixing the problem, frequently presented as a claim or a working thesis; and the goal, declaration of objective, and scope of the paper the writer is writing.

    What are the stages of a basic development process employing a problem-solving approach?

    Problem-Solving Process in 8 Steps:

    Step 1: Identify the issue. What exactly is the issue?

    Step 2: Identify the issue.

    Step 3: Establish the objectives.

    Step 4: Determine the problem's root cause.

    Step 5: Make a plan of action.

    Step 6: Put your plan into action.

    Step 7: Assess the Outcomes

    Step 8: Always strive to improve.


    What's the difference between heuristic and algorithmic problem-solving?

    A step-by-step technique for addressing a given issue in a limited number of steps is known as an algorithm. Given the same parameters, an algorithm's outcome (output) is predictable and repeatable (input). A heuristic is an informed assumption that serves as a starting point for further investigation.

    What makes algorithms superior than heuristics?

    Heuristics entail using a learning and discovery strategy to obtain a solution, while an algorithm is a clearly defined set of instructions for solving a problem. Use an algorithm if you know how to solve an issue.

    References and Further Reading:

    Barr, Avron, and Edward Feigenbaum, eds. 1981. The Handbook of Artificial Intelligence, vol. 1, 113–18. Stanford, CA: HeurisTech Press.

    Ernst, George W., and Allen Newell. 1969. GPS: A Case Study in Generality and Problem Solving. New York: Academic Press.

    Newell, Allen, J. C. Shaw, and Herbert A. Simon. 1960. “Report on a General Problem Solving Program.” In Proceedings of the International Conference on Information Processing (June 15–20, 1959), 256–64. Paris: UNESCO.

    Simon, Herbert A. 1991. Models of My Life. New York: Basic Books.

    Simon, Herbert A., and Allen Newell. 1961. “Computer Simulation of Human Thinking and Problem Solving.” Datamation 7, part 1 (June): 18–20, and part 2 (July): 35–37

    What Is Artificial General Intelligence?

    Artificial General Intelligence (AGI) is defined as the software representation of generalized human cognitive capacities that enables the ...