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    Artificial Intelligence
    COMP2121
    Progress0 / 19 topics
    Topics
    1. An Introduction to Artificial Intelligence and its applications towards Knowledge Based Systems2. Introduction to Reasoning and Knowledge Representation3. Problem Solving by Searching: Informed searching4. Problem Solving by Searching: Uninformed searching5. Heuristics in Problem Solving6. Local searching algorithms7. Minimax algorithm8. Alpha-beta pruning9. Game-playing in AI10. Case Study: General Problem Solver11. Case Study: ELIZA12. Case Study: Student13. Case Study: Macsyma14. Learning from examples15. Artificial Neural Networks (ANN)16. Natural Language Processing17. Recent trends and applications of AI algorithms18. Python programming for AI19. Implementation of AI techniques in Python
    COMP2121›Case Study: General Problem Solver
    Artificial IntelligenceTopic 10 of 19Regular Notes

    Case Study: General Problem Solver

    2 minread
    330words
    Beginnerlevel

    📘 Case Study: General Problem Solver (GPS)


    1. What is the General Problem Solver (GPS)?

    The General Problem Solver (GPS) is an early AI program developed in 1957 by Allen Newell and Herbert A. Simon at the RAND Corporation.

    🎯 Goal: To create a program that could solve any well-defined problem, using human-like reasoning.


    2. Objectives of GPS

    • Model human problem-solving behavior.
    • Work on a wide range of problems (not just one domain).
    • Separate general problem-solving techniques from domain knowledge.

    3. Architecture of GPS

    GPS has two main components:

    Component Description
    Goal Structure Keeps track of the current goal and subgoals
    Means-Ends Analysis Engine Compares current and goal states, selects actions to reduce differences

    4. How GPS Works (Means-Ends Analysis)

    1. Compare the current state with the goal state.
    2. Identify differences between them.
    3. Select an operator (action) that reduces one of the differences.
    4. If an operator has a precondition, set that as a new subgoal.
    5. Continue until the goal is achieved.

    🧠 This mimics how humans solve problems: break big goals into smaller steps.


    5. Example Problem Solved by GPS

    Problem: Prove a theorem in logic Operators: Rules of inference (e.g., Modus Ponens) Goal: Derive a conclusion from premises GPS uses its logic rules to reach the target conclusion step-by-step.


    6. Strengths of GPS

    ✅ Introduced means-ends analysis, a major idea in AI ✅ Showed that general-purpose problem solving is possible ✅ Was an early model of human cognitive processes


    7. Limitations of GPS

    Limitation Explanation
    Not scalable Worked only on simple or toy problems
    Needs formal problem definition Struggles in complex or poorly-defined domains
    No learning ability Did not adapt or improve over time
    Domain knowledge was limited Couldn’t handle rich real-world tasks

    ✅ Summary

    Feature Details
    Name General Problem Solver (GPS)
    Developers Allen Newell & Herbert A. Simon
    Year 1957
    Method Means-Ends Analysis
    Purpose Solve general problems like a human
    Impact Influential in early AI and cognitive science

    Previous topic 9
    Game-playing in AI
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    Case Study: ELIZA

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      Reading Stats
      Est. reading time2 min
      Word count330
      Code examples0
      DifficultyBeginner