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## Chapter 3 Solving Problems by Searching 

When the correct action to take is not immediately obvious, an agent may need to plan ahead : to consider a sequence of actions that form a path to a goal state. Such an agent is called a problem-solving agent , and the computational process it undertakes is called search .

Problem-solving agents use atomic representations, that is, states of the world are considered as wholes, with no internal structure visible to the problem-solving algorithms. Agents that use factored or structured representations of states are called planning agents .

We distinguish between informed algorithms, in which the agent can estimate how far it is from the goal, and uninformed algorithms, where no such estimate is available.

## 3.1 Problem-Solving Agents 

If the agent has no additional information—that is, if the environment is unknown —then the agent can do no better than to execute one of the actions at random. For now, we assume that our agents always have access to information about the world. With that information, the agent can follow this four-phase problem-solving process:

GOAL FORMULATION : Goals organize behavior by limiting the objectives and hence the actions to be considered.

PROBLEM FORMULATION : The agent devises a description of the states and actions necessary to reach the goal—an abstract model of the relevant part of the world.

SEARCH : Before taking any action in the real world, the agent simulates sequences of actions in its model, searching until it finds a sequence of actions that reaches the goal. Such a sequence is called a solution .

EXECUTION : The agent can now execute the actions in the solution, one at a time.

It is an important property that in a fully observable, deterministic, known environment, the solution to any problem is a fixed sequence of actions . The open-loop system means that ignoring the percepts breaks the loop between agent and environment. If there is a chance that the model is incorrect, or the environment is nondeterministic, then the agent would be safer using a closed-loop approach that monitors the percepts.

In partially observable or nondeterministic environments, a solution would be a branching strategy that recommends different future actions depending on what percepts arrive.

## 3.1.1 Search problems and solutions 

A search problem can be defined formally as follows:

A set of possible states that the environment can be in. We call this the state space .

The initial state that the agent starts in.

A set of one or more goal states . We can account for all three of these possibilities by specifying an $$Is\-Goal$$ method for a problem.

The actions available to the agent. Given a state $$s$$ , $$Actions(s)$$ returns a finite set of actions that can be executed in $$s$$ . We say that each of these actions is applicable in $$s$$ .

A transition model , which describes what each action does. $$Result(s,a)$$ returns the state that results from doing action $$a$$ in state $$s$$ .

An action cost function , denote by $$Action\-Cost(s,a,s\pr)$$ when we are programming or $$c(s,a,s\pr)$$ when we are doing math, that gives the numeric cost of applying action $$a$$ in state $$s$$ to reach state $$s\pr$$ .

A sequence of actions forms a path , and a solution is a path from the initial state to a goal state. We assume that action costs are additive; that is, the total cost of a path is the sum of the individual action costs. An optimal solution has the lowest path cost among all solutions.

The state space can be represented as a graph in which the vertices are states and the directed edges between them are actions.

## 3.1.2 Formulating problems 

The process of removing detail from a representation is called abstraction . The abstraction is valid if we can elaborate any abstract solution into a solution in the more detailed world. The abstraction is useful if carrying out each of the actions in the solution is easier than the original problem.

## 3.2 Example Problems 

A standardized problem is intended to illustrate or exercise various problem-solving methods. It can be given a concise, exact description and hence is suitable as a benchmark for researchers to compare the performance of algorithms. A real-world problem , such as robot navigation, is one whose solutions people actually use, and whose formulation is idiosyncratic, not standardized, because, for example, each robot has different sensors that produce different data.

## 3.2.1 Standardized problems 

A grid world problem is a two-dimensional rectangular array of square cells in which agents can move from cell to cell.

Vacuum world

Sokoban puzzle

Sliding-tile puzzle

## 3.2.2 Real-world problems 

Route-finding problem

Touring problems

Trveling salesperson problem (TSP)

VLSI layout problem

Automatic assembly sequencing

## 3.3 Search Algorithms 

A search algorithm takes a search problem as input and returns a solution, or an indication of failure. We consider algorithms that superimpose a search tree over the state-space graph, forming various paths from the initial state, trying to find a path that reaches a goal state. Each node in the search tree corresponds to a state in the state space and the edges in the search tree correspond to actions. The root of the tree corresponds to the initial state of the problem.

The state space describes the (possibly infinite) set of states in the world, and the actions that allow transitions from one state to another. The search tree describes paths between these states, reaching towards the goal. The search tree may have multiple paths to (and thus multiple nodes for) any given state, but each node in the tree has a unique path back to the root (as in all trees).

The frontier separates two regions of the state-space graph: an interior region where every state has been expanded, and an exterior region of states that have not yet been reached.

## 3.3.1 Best-first search 

In best-first search we choose a node, $$n$$ , with minimum value of some evaluation function , $$f(n)$$ .

## 3.3.2 Search data structures 

A node in the tree is represented by a data structure with four components

$$node.State$$ : the state to which the node corresponds;

$$node.Parent$$ : the node in the tree that generated this node;

$$node.Action$$ : the action that was applied to the parent’s state to generate this node;

$$node.Path\-Cost$$ : the total cost of the path from the initial state to this node. In mathematical formulas, we use $$g(node)$$ as a synonym for $$Path\-Cost$$ .

Following the $$PARENT$$ pointers back from a node allows us to recover the states and actions along the path to that node. Doing this from a goal node gives us the solution.

We need a data structure to store the frontier . The appropriate choice is a queue of some kind, because the operations on a frontier are:

$$Is\-Empty(frontier)$$ returns true only if there are no nodes in the frontier.

$$Pop(frontier)$$ removes the top node from the frontier and returns it.

$$Top(frontier)$$ returns (but does not remove) the top node of the frontier.

$$Add(node, frontier)$$ inserts node into its proper place in the queue.

Three kinds of queues are used in search algorithms:

A priority queue first pops the node with the minimum cost according to some evaluation function, $$f$$ . It is used in best-first search.

A FIFO queue or first-in-first-out queue first pops the node that was added to the queue first; we shall see it is used in breadth-first search.

A LIFO queue or last-in-first-out queue (also known as a stack ) pops first the most recently added node; we shall see it is used in depth-first search.

## 3.3.3 Redundant paths 

A cycle is a special case of a redundant path .

As the saying goes, algorithms that cannot remember the past are doomed to repeat it . There are three approaches to this issue.

First, we can remember all previously reached states (as best-first search does), allowing us to detect all redundant paths, and keep only the best path to each state.

Second, we can not worry about repeating the past. We call a search algorithm a graph search if it checks for redundant paths and a tree-like search if it does not check.

Third, we can compromise and check for cycles, but not for redundant paths in general.

## 3.3.4 Measuring problem-solving performance 

COMPLETENESS : Is the algorithm guaranteed to find a solution when there is one, and to correctly report failure when there is not?

COST OPTIMALITY : Does it find a solution with the lowest path cost of all solutions?

TIME COMPLEXITY : How long does it take to find a solution?

SPACE COMPLEXITY : How much memory is needed to perform the search?

To be complete, a search algorithm must be systematic in the way it explores an infinite state space, making sure it can eventually reach any state that is connected to the initial state.

In theoretical computer science, the typical measure of time and space complexity is the size of the state-space graph, $$|V|+|E|$$ , where $$|V|$$ is the number of vertices (state nodes) of the graph and $$|E|$$ is the number of edges (distinct state/action pairs). For an implicit state space, complexity can be measured in terms of $$d$$ , the depth or number of actions in an optimal solution; $$m$$ , the maximum number of actions in any path; and $$b$$ , the branching factor or number of successors of a node that need to be considered.

## 3.4 Uninformed Search Strategies 

When all actions have the same cost, an appropriate strategy is breadth-first search , in which the root node is expanded first, then all the successors of the root node are expanded next, then their successors, and so on.

Breadth-first search always finds a solution with a minimal number of actions, because when it is generating nodes at depth $$d$$ , it has already generated all the nodes at depth $$d-1$$ , so if one of them were a solution, it would have been found.

All the nodes remain in memory, so both time and space complexity are $$O(b^d)$$ . The memory requirements are a bigger problem for breadth-first search than the execution time . In general, exponential-complexity search problems cannot be solved by uninformed search for any but the smallest instances .

## 3.4.2 Dijkstra’s algorithm or uniform-cost search 

When actions have different costs, an obvious choice is to use best-first search where the evaluation function is the cost of the path from the root to the current node. This is called Dijkstra’s algorithm by the theoretical computer science community, and uniform-cost search by the AI community.

The complexity of uniform-cost search is characterized in terms of $$C^*$$ , the cost of the optimal solution, and $$\epsilon$$ , a lower bound on the cost of each action, with $$\epsilon>0$$ . Then the algorithm’s worst-case time and space complexity is $$O(b^{1+\lfloor C^*/\epsilon\rfloor})$$ , which can be much greater than $$b^d$$ .

When all action costs are equal, $$b^{1+\lfloor C^*/\epsilon\rfloor}$$ is just $$b^{d+1}$$ , and uniform-cost search is similar to breadth-first search.

## 3.4.3 Depth-first search and the problem of memory 

Depth-first search always expands the deepest node in the frontier first. It could be implemented as a call to $$Best\-First\-Search$$ where the evaluation function $$f$$ is the negative of the depth.

For problems where a tree-like search is feasible, depth-first search has much smaller needs for memory. A depth-first tree-like search takes time proportional to the number of states, and has memory complexity of only $$O(bm)$$ , where $$b$$ is the branching factor and $$m$$ is the maximum depth of the tree.

A variant of depth-first search called backtracking search uses even less memory.

## 3.4.4 Depth-limited and iterative deepening search 

To keep depth-first search from wandering down an infinite path, we can use depth-limited search , a version of depth-first search in which we supply a depth limit, $$l$$ , and treat all nodes at depth $$l$$ as if they had no successors. The time complexity is $$O(b^l)$$ and the space complexity is $$O(bl)$$

Iterative deepening search solves the problem of picking a good value for $$l$$ by trying all values: first 0, then 1, then 2, and so on—until either a solution is found, or the depth- limited search returns the failure value rather than the cutoff value.

Its memory requirements are modest: $$O(bd)$$ when there is a solution, or $$O(bm)$$ on finite state spaces with no solution. The time complexity is $$O(bd)$$ when there is a solution, or $$O(bm)$$ when there is none.

In general, iterative deepening is the preferred uninformed search method when the search state space is larger than can fit in memory and the depth of the solution is not known .

## 3.4.5 Bidirectional search 

An alternative approach called bidirectional search simultaneously searches forward from the initial state and backwards from the goal state(s), hoping that the two searches will meet.

## 3.5 Informed (Heuristic) Search Strategies 

An informed search strategy uses domain–specific hints about the location of goals to find colutions more efficiently than an uninformed strategy. The hints come in the form of a heuristic function , denoted $$h(n)$$ :

$$h(n)$$ = estimated cost of the cheapest path from the state at node $$n$$ to a goal state.

## 3.5.1 Greedy best-first search 

Greedy best-first search is a form of best-first search that expands first the node with the lowest $$h(n)$$ value—the node that appears to be closest to the goal—on the grounds that this is likely to lead to a solution quickly. So the evaluation function $$f(n)=h(n)$$ .

## Artificial Intelligence Search Methods For Problem Solving

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 Course Status : Completed Course Type : Elective Duration : 12 weeks Category : Credit Points : 3 Undergraduate Start Date : 26 Jul 2021 End Date : 15 Oct 2021 Enrollment Ends : 09 Aug 2021 Exam Date : 23 Oct 2021 IST
 Deepak Khemani. A First Course in Artificial Intelligence, McGraw Hill Education (India), 2013. Stefan Edelkamp and Stefan Schroedl. Heuristic Search: Theory and Applications, Morgan Kaufmann, 2011. John Haugeland, Artificial Intelligence: The Very Idea, A Bradford Book, The MIT Press, 1985. Pamela McCorduck, Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence, A K Peters/CRC Press; 2 edition, 2004. Zbigniew Michalewicz and David B. Fogel. How to Solve It: Modern Heuristics. Springer; 2nd edition, 2004. Judea Pearl. Heuristics: Intelligent Search Strategies for Computer Problem Solving, Addison-Wesley, 1984. Elaine Rich and Kevin Knight. Artificial Intelligence, Tata McGraw Hill, 1991. Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach, 3rd Edition, Prentice Hall, 2009. Eugene Charniak, Drew McDermott. Introduction to Artificial Intelligence, Addison-Wesley, 1985. Patrick Henry Winston. Artificial Intelligence, Addison-Wesley, 1992.

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

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## 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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• Computer Science and Engineering
• 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
• 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
• Assignments
• Transcripts

## Video Transcript:

• Lecture Notes (1)
Lecture Note 203M
Week_01_Assignment_01
Week_02_Assignment_02
Week_03_Assignment_03
Week_04_Assignment_04
Week_05_Assignment_05
Week_06_Assignment_06
Week_07_Assignment_07
Week_08_Assignment_08
Week_09_Assignment_09
Week_10_Assignment_10
Week_11_Assignment_11
Week_12_Assignment_12
11. Artificial Intelligence: Introduction
22. Introduction to AI
33. AI Introduction: Philosophy
44. AI Introduction
55. Introduction: Philosophy
66. State Space Search - Introduction
77. Search - DFS and BFS
88. Search DFID
99. Heuristic Search
1010. Hill climbing
1111. Solution Space Search,Beam Search
1212. TSP Greedy Methods
1313. Tabu Search
1414. Optimization - I (Simulated Annealing)
1515. Optimization II (Genetic Algorithms)
1616. Population based methods for Optimization
1717. Population Based Methods II
1818. Branch and Bound, Dijkstra's Algorithm
1919. A* Algorithm
2121. A* Monotone Property, Iterative Deeping A*
2222. Recursive Best First Search, Sequence Allignment
2323. Pruning the Open and Closed lists
2424. Problem Decomposition with Goal Trees
2525. AO* Algorithm
2626. Game Playing
2727. Game Playing- Minimax Search
2828. Game Playing - AlphaBeta
2929. Game Playing-SSS *
3030. Rule Based Systems
3131. Inference Engines
3232. Rete Algorithm
3333. Planning
3434. Planning FSSP, BSSP
3535. Goal Stack Planning. Sussman's Anomaly
3636. Non-linear planning
3737. Plan Space Planning
3838. GraphPlan
3939. Constraint Satisfaction Problems
4040. CSP continued
4141. Knowledge-based systems
4242. Knowledge-based Systems, PL
4343. Propositional Logic
4444. Resolution Refutation for PL
4545. First-order Logic (FOL)
4646. Reasoning in FOL
4747. Backward chaining
4848. Resolution for FOL
Sl.No Chapter Name English
11. Artificial Intelligence: Introduction
22. Introduction to AI
33. AI Introduction: Philosophy
44. AI Introduction
55. Introduction: Philosophy
66. State Space Search - Introduction
77. Search - DFS and BFS
88. Search DFID
99. Heuristic Search
1010. Hill climbing
1111. Solution Space Search,Beam Search
1212. TSP Greedy Methods
1313. Tabu Search
1414. Optimization - I (Simulated Annealing)
1515. Optimization II (Genetic Algorithms)
1616. Population based methods for Optimization
1717. Population Based Methods II
1818. Branch and Bound, Dijkstra's Algorithm
1919. A* Algorithm
2121. A* Monotone Property, Iterative Deeping A*
2222. Recursive Best First Search, Sequence Allignment
2323. Pruning the Open and Closed lists
2424. Problem Decomposition with Goal Trees
2525. AO* Algorithm
2626. Game Playing
2727. Game Playing- Minimax Search
2828. Game Playing - AlphaBeta
2929. Game Playing-SSS *
3030. Rule Based Systems
3131. Inference Engines
3232. Rete Algorithm
3333. Planning
3434. Planning FSSP, BSSP
3535. Goal Stack Planning. Sussman's Anomaly
3636. Non-linear planning
3737. Plan Space Planning
3838. GraphPlan
3939. Constraint Satisfaction Problems
4040. CSP continued
4141. Knowledge-based systems
4242. Knowledge-based Systems, PL
4343. Propositional Logic
4444. Resolution Refutation for PL
4545. First-order Logic (FOL)
4646. Reasoning in FOL
4747. Backward chaining
4848. Resolution for FOL
Sl.No Chapter Name Bengali
11. Artificial Intelligence: Introduction
22. Introduction to AI
33. AI Introduction: Philosophy
44. AI Introduction
55. Introduction: Philosophy
66. State Space Search - Introduction
77. Search - DFS and BFS
88. Search DFID
99. Heuristic Search
1010. Hill climbing
1111. Solution Space Search,Beam Search
1212. TSP Greedy Methods
1313. Tabu Search
1414. Optimization - I (Simulated Annealing)
1515. Optimization II (Genetic Algorithms)
1616. Population based methods for Optimization
1717. Population Based Methods II
1818. Branch and Bound, Dijkstra's Algorithm
1919. A* Algorithm
2121. A* Monotone Property, Iterative Deeping A*
2222. Recursive Best First Search, Sequence Allignment
2323. Pruning the Open and Closed lists
2424. Problem Decomposition with Goal Trees
2525. AO* Algorithm
2626. Game Playing
2727. Game Playing- Minimax Search
2828. Game Playing - AlphaBeta
2929. Game Playing-SSS *
3030. Rule Based Systems
3131. Inference Engines
3232. Rete Algorithm
3333. Planning
3434. Planning FSSP, BSSP
3535. Goal Stack Planning. Sussman's Anomaly
3636. Non-linear planning
3737. Plan Space Planning
3838. GraphPlan
3939. Constraint Satisfaction Problems
4040. CSP continued
4141. Knowledge-based systems
4242. Knowledge-based Systems, PL
4343. Propositional Logic
4444. Resolution Refutation for PL
4545. First-order Logic (FOL)
4646. Reasoning in FOL
4747. Backward chaining
4848. Resolution for FOL
Sl.No Chapter Name Hindi
11. Artificial Intelligence: Introduction
22. Introduction to AI
33. AI Introduction: Philosophy
44. AI Introduction
55. Introduction: Philosophy
66. State Space Search - Introduction
77. Search - DFS and BFS
88. Search DFID
99. Heuristic Search
1010. Hill climbing
1111. Solution Space Search,Beam Search
1212. TSP Greedy Methods
1313. Tabu Search
1414. Optimization - I (Simulated Annealing)
1515. Optimization II (Genetic Algorithms)
1616. Population based methods for Optimization
1717. Population Based Methods II
1818. Branch and Bound, Dijkstra's Algorithm
1919. A* Algorithm
2121. A* Monotone Property, Iterative Deeping A*
2222. Recursive Best First Search, Sequence Allignment
2323. Pruning the Open and Closed lists
2424. Problem Decomposition with Goal Trees
2525. AO* Algorithm
2626. Game Playing
2727. Game Playing- Minimax Search
2828. Game Playing - AlphaBeta
2929. Game Playing-SSS *
3030. Rule Based Systems
3131. Inference Engines
3232. Rete Algorithm
3333. Planning
3434. Planning FSSP, BSSP
3535. Goal Stack Planning. Sussman's Anomaly
3636. Non-linear planning
3737. Plan Space Planning
3838. GraphPlan
3939. Constraint Satisfaction Problems
4040. CSP continued
4141. Knowledge-based systems
4242. Knowledge-based Systems, PL
4343. Propositional Logic
4444. Resolution Refutation for PL
4545. First-order Logic (FOL)
4646. Reasoning in FOL
4747. Backward chaining
4848. Resolution for FOL
Sl.No Chapter Name Tamil
11. Artificial Intelligence: Introduction
22. Introduction to AI
33. AI Introduction: Philosophy
44. AI Introduction
55. Introduction: Philosophy
66. State Space Search - Introduction
77. Search - DFS and BFS
88. Search DFID
99. Heuristic Search
1010. Hill climbing
1111. Solution Space Search,Beam Search
1212. TSP Greedy Methods
1313. Tabu Search
1414. Optimization - I (Simulated Annealing)
1515. Optimization II (Genetic Algorithms)
1616. Population based methods for Optimization
1717. Population Based Methods II
1818. Branch and Bound, Dijkstra's Algorithm
1919. A* Algorithm
2121. A* Monotone Property, Iterative Deeping A*
2222. Recursive Best First Search, Sequence Allignment
2323. Pruning the Open and Closed lists
2424. Problem Decomposition with Goal Trees
2525. AO* Algorithm
2626. Game Playing
2727. Game Playing- Minimax Search
2828. Game Playing - AlphaBeta
2929. Game Playing-SSS *
3030. Rule Based Systems
3131. Inference Engines
3232. Rete Algorithm
3333. Planning
3434. Planning FSSP, BSSP
3535. Goal Stack Planning. Sussman's Anomaly
3636. Non-linear planning
3737. Plan Space Planning
3838. GraphPlan
3939. Constraint Satisfaction Problems
4040. CSP continued
4141. Knowledge-based systems
4242. Knowledge-based Systems, PL
4343. Propositional Logic
4444. Resolution Refutation for PL
4545. First-order Logic (FOL)
4646. Reasoning in FOL
4747. Backward chaining
4848. Resolution for FOL
Sl.No Chapter Name Telugu
11. Artificial Intelligence: Introduction
22. Introduction to AI
33. AI Introduction: Philosophy
44. AI Introduction
55. Introduction: Philosophy
66. State Space Search - Introduction
77. Search - DFS and BFS
88. Search DFID
99. Heuristic Search
1010. Hill climbing
1111. Solution Space Search,Beam Search
1212. TSP Greedy Methods
1313. Tabu Search
1414. Optimization - I (Simulated Annealing)
1515. Optimization II (Genetic Algorithms)
1616. Population based methods for Optimization
1717. Population Based Methods II
1818. Branch and Bound, Dijkstra's Algorithm
1919. A* Algorithm
2121. A* Monotone Property, Iterative Deeping A*
2222. Recursive Best First Search, Sequence Allignment
2323. Pruning the Open and Closed lists
2424. Problem Decomposition with Goal Trees
2525. AO* Algorithm
2626. Game Playing
2727. Game Playing- Minimax Search
2828. Game Playing - AlphaBeta
2929. Game Playing-SSS *
3030. Rule Based Systems
3131. Inference Engines
3232. Rete Algorithm
3333. Planning
3434. Planning FSSP, BSSP
3535. Goal Stack Planning. Sussman's Anomaly
3636. Non-linear planning
3737. Plan Space Planning
3838. GraphPlan
3939. Constraint Satisfaction Problems
4040. CSP continued
4141. Knowledge-based systems
4242. Knowledge-based Systems, PL
4343. Propositional Logic
4444. Resolution Refutation for PL
4545. First-order Logic (FOL)
4646. Reasoning in FOL
4747. Backward chaining
4848. Resolution for FOL
1English
2Bengali
3GujaratiNot Available
4Hindi
6MalayalamNot Available
7MarathiNot Available
8Tamil
9Telugu

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1. Artificial Intelligence: Search Methods For Problem Solving Assignment 4 Answers

2. [Week 1, 2] NPTEL Artificial Intelligence : Search Methods For Problem Solving Assignment

3. Artificial Intelligence Search Methods For Problem Soddlving

4. Artificial Intelligence

5. Week 4.pdf

6. Problem Solving Methods In Artificial Intelligence Pdf

1. PDF AI Handbook

A. Overview In Artificial Intelligence the terms problem solving and search refer to a large body of core ideas that deal with deduction, inference, planning, commonsense reasoning, theorem proving, and related processes. Applications ofthese general ideas are found inprograms for natural language understanding, information retrieval, automatic programming,robotics, scene analysis, game ...

2. PDF Fundamentals of Artificial Intelligence Chapter 03: Problem Solving as

Problem formulation: define a representation for states define legal actions and transition functions. Search: find a solution by means of a search process. solutions are sequences of actions. Execution: given the solution, perform the actions. =) Problem-solving agents are (a kind of) goal-based agents.

3. PDF Solving problems by searching

The process of looking for a sequence of actions that reaches the goal is called search. A search algorithm takes a problem as input and returns a solution in the form of an action sequence. A problem can be defined formally by (5) components: (1) The initial state from which the agent starts.

4. PDF Artificial Intelligence V03: Problem solving through search

Three missionaries and 3 cannibals are on one side of a river, along with a boat that can hold one or two people. Find a way to get everyone to the other side, without ever leaving a group of missionaries in one place. outnumbered by the cannibals in that place.

5. PDF Chapter 3 Solving problems by searching

Else pick some search node N from Q. If state(N) is a goal, return N (we have reached the goal) Otherwise remove N from Q. Find all the children of state(N) not in visited and create all the one-step extensions of N to each descendant. Add the extended paths to Q, add children of state(N) to Visited. Go to step 2.

6. (PDF) Search In Artificial Intelligence Problem Solving

Abstract. In this paper, search methods/ techniques in problem solving using artificial intelligence (A.I) are surveyed. An overview of the definitions, dimensions and development of A.I in the ...

7. PDF Problem Solving and Search

6.825 Techniques in Artificial Intelligence Problem Solving and Search Problem Solving • Agent knows world dynamics • World state is finite, small enough to enumerate • World is deterministic • Utility for a sequence of states is a sum over path The utility for sequences of states is a sum over the path of the utilities of the

8. PDF Artificial Intelligence: Search Methods D. Kopec and T.A. Marsland

Search is inherent to the problems and methods of artificial intelligence (AI). That is because AI problems are intrinsically complex. Efforts to solve problems with computers which humans can routinely solve by employing innate cognitive abilities, pattern recognition, perception and experience, invariably must turn to considerations of search.

9. PDF Search Techniques for Artiﬁcial Intelligence

ves.Breadth-first Search: Implementation IdeaThe algorithm will. the algorithm running consists of three steps. Remove the first path from the list of paths.Generate a new path for every possible follow-up state of the stat. the list of newly generated paths to the endof the list of paths (to ensure paths ar.

10. (PDF) Search Strategies for Artificial Intelligence Problem Solving and

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.

11. Chapter 3 Solving Problems by Searching

3.3 Search Algorithms. A search algorithm takes a search problem as input and returns a solution, or an indication of failure. We consider algorithms that superimpose a search tree over the state-space graph, forming various paths from the initial state, trying to find a path that reaches a goal state.

12. Artificial Intelligence Search Methods For Problem Solving

In this first course on AI we study a wide variety of search methods that agents can employ for problem solving. In a follow up course - AI: Knowledge Representation and Reasoning - we will go into the details of how an agent can represent its world and reason with what it knows. These two courses should lay a strong foundation for ...

13. (PDF) Problem solving using artificial intelligence techniques

This paper reviews the area of problem solving in the field of Artificial Intelligence. This includes problem representation for computation, "weak" methods of searching for a problems solution ...

14. NOC

These two courses should lay a strong foundation for artificial intelligence, which the student can build upon. A third short course - AI: Constraint Satisfaction Problems - presents a slightly different formalism for problem solving, one in which the search and reasoning processes mentioned above can operate together.

15. PDF Artificial Intelligence : Search Methods for Problem Solving

decisions to be able to identify the ones that work. In this ﬁrst course on AI we study a wide variety of search methods that agents can employ for problem solving. In a follow up course - AI: Knowledge Representation and Reasoning - we will go into the details of how an agent can represent its world and reason with what it knows. These ...

16. Artificial Intelligence

Artificial Intelligence _ Search Methods for Problem Solving - Course - Free download as PDF File (.pdf), Text File (.txt) or read online for free. Scribd is the world's largest social reading and publishing site.

17. PDF AI-Search-Methods-for-Problem-Solving/AI_Search_Methods_Notes.pdf at

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18. [PDF] Problem-solving methods in artificial intelligence

A problem-solving framework in which aspects of mathematical decision theory are incorporated into symbolic problem-resolution techniques currently predominant in artificial intelligence is described, illustrated by application to the classic monkey and bananas problem. Expand. 135. PDF.

19. NOC

In this first course on AI we study a wide variety of search methods that agents can employ for problem solving. In a follow up course - knowledge representation and reasoning - we will go into the details of how an agent can represent its world and reason with what it knows. These two courses should lay a strong foundation for artificial ...

20. ARTIFICIAL INTELLIGENCE PROBLEM SOLVING AND SEARCH

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 ...

21. PDF X Artificial Intelligence Search Methods For Problem Solving (course)

1/23/2021 Artiﬁcial Intelligence Search Methods For Problem Solving - - Unit 14 - Week 11 BEGIN GROUP

22. (PPT) Artificial Intelligence: Session 3 Problem Solving Agent and

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.

23. GitHub

Artificial Intelligence Search Methods for Problem Solving Nptel Week 1 Assignment Answers 2024. ... Problem Solving Through Programming in C Nptel Week 1 Assignment Answers 2024. Link: ...

24. NPTEL :: Computer Science and Engineering

Sl.No Chapter Name English; 1: 1. Artificial Intelligence: Introduction: Download Verified; 2: 2. Introduction to AI: Download Verified; 3: 3. AI Introduction: Philosophy