artificial intelligence search methods for problem solving pdf

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• Chapter 3 Solving Problems by Searching
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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

Robot navigation

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 

3.4.1 breadth-first search .

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.

3.4.6 Comparing uninformed search algorithms 

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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	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
• 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
• Watch on YouTube
• Assignments
• Download Videos
• Transcripts

Video Transcript:

• Lecture Notes (1)

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Sl.No	Chapter Name	MP4 Download
1	1. Artificial Intelligence: Introduction
2	2. Introduction to AI
3	3. AI Introduction: Philosophy
4	4. AI Introduction
5	5. Introduction: Philosophy
6	6. State Space Search - Introduction
7	7. Search - DFS and BFS
8	8. Search DFID
9	9. Heuristic Search
10	10. Hill climbing
11	11. Solution Space Search,Beam Search
12	12. TSP Greedy Methods
13	13. Tabu Search
14	14. Optimization - I (Simulated Annealing)
15	15. Optimization II (Genetic Algorithms)
16	16. Population based methods for Optimization
17	17. Population Based Methods II
18	18. Branch and Bound, Dijkstra's Algorithm
19	19. A* Algorithm
20	20. Admissibility of A*
21	21. A* Monotone Property, Iterative Deeping A*
22	22. Recursive Best First Search, Sequence Allignment
23	23. Pruning the Open and Closed lists
24	24. Problem Decomposition with Goal Trees
25	25. AO* Algorithm
26	26. Game Playing
27	27. Game Playing- Minimax Search
28	28. Game Playing - AlphaBeta
29	29. Game Playing-SSS *
30	30. Rule Based Systems
31	31. Inference Engines
32	32. Rete Algorithm
33	33. Planning
34	34. Planning FSSP, BSSP
35	35. Goal Stack Planning. Sussman's Anomaly
36	36. Non-linear planning
37	37. Plan Space Planning
38	38. GraphPlan
39	39. Constraint Satisfaction Problems
40	40. CSP continued
41	41. Knowledge-based systems
42	42. Knowledge-based Systems, PL
43	43. Propositional Logic
44	44. Resolution Refutation for PL
45	45. First-order Logic (FOL)
46	46. Reasoning in FOL
47	47. Backward chaining
48	48. Resolution for FOL

Sl.No	Chapter Name	English
1	1. Artificial Intelligence: Introduction
2	2. Introduction to AI
3	3. AI Introduction: Philosophy
4	4. AI Introduction
5	5. Introduction: Philosophy
6	6. State Space Search - Introduction
7	7. Search - DFS and BFS
8	8. Search DFID
9	9. Heuristic Search
10	10. Hill climbing
11	11. Solution Space Search,Beam Search
12	12. TSP Greedy Methods
13	13. Tabu Search
14	14. Optimization - I (Simulated Annealing)
15	15. Optimization II (Genetic Algorithms)
16	16. Population based methods for Optimization
17	17. Population Based Methods II
18	18. Branch and Bound, Dijkstra's Algorithm
19	19. A* Algorithm
20	20. Admissibility of A*
21	21. A* Monotone Property, Iterative Deeping A*
22	22. Recursive Best First Search, Sequence Allignment
23	23. Pruning the Open and Closed lists
24	24. Problem Decomposition with Goal Trees
25	25. AO* Algorithm
26	26. Game Playing
27	27. Game Playing- Minimax Search
28	28. Game Playing - AlphaBeta
29	29. Game Playing-SSS *
30	30. Rule Based Systems
31	31. Inference Engines
32	32. Rete Algorithm
33	33. Planning
34	34. Planning FSSP, BSSP
35	35. Goal Stack Planning. Sussman's Anomaly
36	36. Non-linear planning
37	37. Plan Space Planning
38	38. GraphPlan
39	39. Constraint Satisfaction Problems
40	40. CSP continued
41	41. Knowledge-based systems
42	42. Knowledge-based Systems, PL
43	43. Propositional Logic
44	44. Resolution Refutation for PL
45	45. First-order Logic (FOL)
46	46. Reasoning in FOL
47	47. Backward chaining
48	48. Resolution for FOL

Sl.No	Chapter Name	Bengali
1	1. Artificial Intelligence: Introduction
2	2. Introduction to AI
3	3. AI Introduction: Philosophy
4	4. AI Introduction
5	5. Introduction: Philosophy
6	6. State Space Search - Introduction
7	7. Search - DFS and BFS
8	8. Search DFID
9	9. Heuristic Search
10	10. Hill climbing
11	11. Solution Space Search,Beam Search
12	12. TSP Greedy Methods
13	13. Tabu Search
14	14. Optimization - I (Simulated Annealing)
15	15. Optimization II (Genetic Algorithms)
16	16. Population based methods for Optimization
17	17. Population Based Methods II
18	18. Branch and Bound, Dijkstra's Algorithm
19	19. A* Algorithm
20	20. Admissibility of A*
21	21. A* Monotone Property, Iterative Deeping A*
22	22. Recursive Best First Search, Sequence Allignment
23	23. Pruning the Open and Closed lists
24	24. Problem Decomposition with Goal Trees
25	25. AO* Algorithm
26	26. Game Playing
27	27. Game Playing- Minimax Search
28	28. Game Playing - AlphaBeta
29	29. Game Playing-SSS *
30	30. Rule Based Systems
31	31. Inference Engines
32	32. Rete Algorithm
33	33. Planning
34	34. Planning FSSP, BSSP
35	35. Goal Stack Planning. Sussman's Anomaly
36	36. Non-linear planning
37	37. Plan Space Planning
38	38. GraphPlan
39	39. Constraint Satisfaction Problems
40	40. CSP continued
41	41. Knowledge-based systems
42	42. Knowledge-based Systems, PL
43	43. Propositional Logic
44	44. Resolution Refutation for PL
45	45. First-order Logic (FOL)
46	46. Reasoning in FOL
47	47. Backward chaining
48	48. Resolution for FOL

Sl.No	Chapter Name	Hindi
1	1. Artificial Intelligence: Introduction
2	2. Introduction to AI
3	3. AI Introduction: Philosophy
4	4. AI Introduction
5	5. Introduction: Philosophy
6	6. State Space Search - Introduction
7	7. Search - DFS and BFS
8	8. Search DFID
9	9. Heuristic Search
10	10. Hill climbing
11	11. Solution Space Search,Beam Search
12	12. TSP Greedy Methods
13	13. Tabu Search
14	14. Optimization - I (Simulated Annealing)
15	15. Optimization II (Genetic Algorithms)
16	16. Population based methods for Optimization
17	17. Population Based Methods II
18	18. Branch and Bound, Dijkstra's Algorithm
19	19. A* Algorithm
20	20. Admissibility of A*
21	21. A* Monotone Property, Iterative Deeping A*
22	22. Recursive Best First Search, Sequence Allignment
23	23. Pruning the Open and Closed lists
24	24. Problem Decomposition with Goal Trees
25	25. AO* Algorithm
26	26. Game Playing
27	27. Game Playing- Minimax Search
28	28. Game Playing - AlphaBeta
29	29. Game Playing-SSS *
30	30. Rule Based Systems
31	31. Inference Engines
32	32. Rete Algorithm
33	33. Planning
34	34. Planning FSSP, BSSP
35	35. Goal Stack Planning. Sussman's Anomaly
36	36. Non-linear planning
37	37. Plan Space Planning
38	38. GraphPlan
39	39. Constraint Satisfaction Problems
40	40. CSP continued
41	41. Knowledge-based systems
42	42. Knowledge-based Systems, PL
43	43. Propositional Logic
44	44. Resolution Refutation for PL
45	45. First-order Logic (FOL)
46	46. Reasoning in FOL
47	47. Backward chaining
48	48. Resolution for FOL

Sl.No	Chapter Name	Tamil
1	1. Artificial Intelligence: Introduction
2	2. Introduction to AI
3	3. AI Introduction: Philosophy
4	4. AI Introduction
5	5. Introduction: Philosophy
6	6. State Space Search - Introduction
7	7. Search - DFS and BFS
8	8. Search DFID
9	9. Heuristic Search
10	10. Hill climbing
11	11. Solution Space Search,Beam Search
12	12. TSP Greedy Methods
13	13. Tabu Search
14	14. Optimization - I (Simulated Annealing)
15	15. Optimization II (Genetic Algorithms)
16	16. Population based methods for Optimization
17	17. Population Based Methods II
18	18. Branch and Bound, Dijkstra's Algorithm
19	19. A* Algorithm
20	20. Admissibility of A*
21	21. A* Monotone Property, Iterative Deeping A*
22	22. Recursive Best First Search, Sequence Allignment
23	23. Pruning the Open and Closed lists
24	24. Problem Decomposition with Goal Trees
25	25. AO* Algorithm
26	26. Game Playing
27	27. Game Playing- Minimax Search
28	28. Game Playing - AlphaBeta
29	29. Game Playing-SSS *
30	30. Rule Based Systems
31	31. Inference Engines
32	32. Rete Algorithm
33	33. Planning
34	34. Planning FSSP, BSSP
35	35. Goal Stack Planning. Sussman's Anomaly
36	36. Non-linear planning
37	37. Plan Space Planning
38	38. GraphPlan
39	39. Constraint Satisfaction Problems
40	40. CSP continued
41	41. Knowledge-based systems
42	42. Knowledge-based Systems, PL
43	43. Propositional Logic
44	44. Resolution Refutation for PL
45	45. First-order Logic (FOL)
46	46. Reasoning in FOL
47	47. Backward chaining
48	48. Resolution for FOL

Sl.No	Chapter Name	Telugu
1	1. Artificial Intelligence: Introduction
2	2. Introduction to AI
3	3. AI Introduction: Philosophy
4	4. AI Introduction
5	5. Introduction: Philosophy
6	6. State Space Search - Introduction
7	7. Search - DFS and BFS
8	8. Search DFID
9	9. Heuristic Search
10	10. Hill climbing
11	11. Solution Space Search,Beam Search
12	12. TSP Greedy Methods
13	13. Tabu Search
14	14. Optimization - I (Simulated Annealing)
15	15. Optimization II (Genetic Algorithms)
16	16. Population based methods for Optimization
17	17. Population Based Methods II
18	18. Branch and Bound, Dijkstra's Algorithm
19	19. A* Algorithm
20	20. Admissibility of A*
21	21. A* Monotone Property, Iterative Deeping A*
22	22. Recursive Best First Search, Sequence Allignment
23	23. Pruning the Open and Closed lists
24	24. Problem Decomposition with Goal Trees
25	25. AO* Algorithm
26	26. Game Playing
27	27. Game Playing- Minimax Search
28	28. Game Playing - AlphaBeta
29	29. Game Playing-SSS *
30	30. Rule Based Systems
31	31. Inference Engines
32	32. Rete Algorithm
33	33. Planning
34	34. Planning FSSP, BSSP
35	35. Goal Stack Planning. Sussman's Anomaly
36	36. Non-linear planning
37	37. Plan Space Planning
38	38. GraphPlan
39	39. Constraint Satisfaction Problems
40	40. CSP continued
41	41. Knowledge-based systems
42	42. Knowledge-based Systems, PL
43	43. Propositional Logic
44	44. Resolution Refutation for PL
45	45. First-order Logic (FOL)
46	46. Reasoning in FOL
47	47. Backward chaining
48	48. Resolution for FOL

Sl.No	Language	Book link
1	English
2	Bengali
3	Gujarati	Not Available
4	Hindi
5	Kannada	Not Available
6	Malayalam	Not Available
7	Marathi	Not Available
8	Tamil
9	Telugu