artificial intelligence search methods for problem solving pdf

Artificial Intelligence : Search Methods For Problem solving

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Artificial Intelligence: Session 3 Problem Solving Agent and searching for solutions

2023, Asst.Prof.M. Gokilavani

Related Papers

International Journal of Scientific Research in Science, Engineering and Technology

International Journal of Scientific Research in Science, Engineering and Technology IJSRSET

Problem-solving strategies in Artificial Intelligence are steps to overcome the barriers to achieve a goal, the "problem-solving cycle". Most common steps in such cycle involve- recognizing a problem, defining it, developing a strategy in order to fix it, organizing knowledge and resources available, monitoring progress, and evaluating the effectiveness of the solution. Once a solution is obtained, another problem usually comes, and the cycle starts again. Searching techniques in problem-solving by using artificial intelligence (A.I) are surveyed in this paper. An overview of definitions, development and dimensions of A.I in the light of search for solutions to problems are accepted. Dimensions as well as relevance of search in A.I research is reviewed. A classification of searching in the matter of depth of known parameters of search was also investigated. At last, the forethoughts of searching in AI research are brought into prominence. Search is noticed that it is the common thread that binds most of the problem-solving strategies in AI together.

Garima Srivastava

Pankaj Vashisht

ARS Publication, Chennai

P Krishna Sankar

This book "Computational Intelligence" is to understand the various characteristics of Intelligent agents and their search strategies. It contributes an impression towards representing knowledge in solving AI problems, reasoning, natural language understand, computer vision, automatic programming and machine learning. It provides a preliminary study to design and implement the different ways of software agents for problem solving. Unit I: Introduction towards future of Artificial Intelligence and characteristics of Intelligent agents. Outline towards search strategies through Uninformed, Informed and Heuristics along with optimization problems. Summary about the typical expert system and its functional models. Unit II: Introduction to Proposition logic and First order predicate logic was demonstrated with straight forward and backtracking approach. Awareness towards the ontology and reasoning based on knowledge availability. Demonstrated the Unification, Forward and Backward chaining, Ontological Engineering and Events with Prolog programming. Unit III: Brief awareness on uncertainty, non-monotonic reasoning, fuzzy, temporal and neural networks. Unit IV: Contributes a knowledge on learning with understanding towards Bayesian network and Hidden Markov Models. An Illustration on Supervised learning, Decision Tree, Regression, Neural Networks, Support vector machines and Reinforcement learning were briefed in this section. Unit V: Provides a study over Natural Language Processing and Machine Learning. It provides illustration towards Information extraction, Information retrieval, Machine Translation, Symbol-Based an Connectionist.

Hassan Sebak

Janet Kolodner

Sarvesh Kumar

— Artificial Intelligence is the science and technology appertained to make machines intelligent. The main aim of researchers is to develop universal intelligent system to rival the intelligence capabilities of human beings. It should be kept in mind that for the time being, the most important applications of AI is to develop intelligent systems to solve real world problems. Which otherwise take considerable amount of time and human efforts, and hence become uneconomical as well as inefficient at times? Hence problem solving becomes major area of study, which involves methods and various techniques used in problem solving using AI.

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• Artificial Intelligence: Search Methods for Problem Solving (Video) 
• Co-ordinated by : IIT Madras
• Available from : 2014-05-06
• Intro Video
• 1. Artificial Intelligence: Introduction
• 2. Introduction to AI
• 3. AI Introduction: Philosophy
• 4. AI Introduction
• 5. Introduction: Philosophy
• 6. State Space Search - Introduction
• 7. Search - DFS and BFS
• 8. Search DFID
• 9. Heuristic Search
• 10. Hill climbing
• 11. Solution Space Search,Beam Search
• 12. TSP Greedy Methods
• 13. Tabu Search
• 14. Optimization - I (Simulated Annealing)
• 15. Optimization II (Genetic Algorithms)
• 16. Population based methods for Optimization
• 17. Population Based Methods II
• 18. Branch and Bound, Dijkstra's Algorithm
• 19. A* Algorithm
• 20. Admissibility of A*
• 21. A* Monotone Property, Iterative Deeping A*
• 22. Recursive Best First Search, Sequence Allignment
• 23. Pruning the Open and Closed lists
• 24. Problem Decomposition with Goal Trees
• 25. AO* Algorithm
• 26. Game Playing
• 27. Game Playing- Minimax Search
• 28. Game Playing - AlphaBeta
• 29. Game Playing-SSS *
• 30. Rule Based Systems
• 31. Inference Engines
• 32. Rete Algorithm
• 33. Planning
• 34. Planning FSSP, BSSP
• 35. Goal Stack Planning. Sussman's Anomaly
• 36. Non-linear planning
• 37. Plan Space Planning
• 38. GraphPlan
• 39. Constraint Satisfaction Problems
• 40. CSP continued
• 41. Knowledge-based systems
• 42. Knowledge-based Systems, PL
• 43. Propositional Logic
• 44. Resolution Refutation for PL
• 45. First-order Logic (FOL)
• 46. Reasoning in FOL
• 47. Backward chaining
• 48. Resolution for FOL
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Problem-solving methods in artificial intelligence

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Computer Science > Artificial Intelligence

Title: gold: geometry problem solver with natural language description.

Abstract: Addressing the challenge of automated geometry math problem-solving in artificial intelligence (AI) involves understanding multi-modal information and mathematics. Current methods struggle with accurately interpreting geometry diagrams, which hinders effective problem-solving. To tackle this issue, we present the Geometry problem sOlver with natural Language Description (GOLD) model. GOLD enhances the extraction of geometric relations by separately processing symbols and geometric primitives within the diagram. Subsequently, it converts the extracted relations into natural language descriptions, efficiently utilizing large language models to solve geometry math problems. Experiments show that the GOLD model outperforms the Geoformer model, the previous best method on the UniGeo dataset, by achieving accuracy improvements of 12.7% and 42.1% in calculation and proving subsets. Additionally, it surpasses the former best model on the PGPS9K and Geometry3K datasets, PGPSNet, by obtaining accuracy enhancements of 1.8% and 3.2%, respectively.

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