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Problem Solving in Artificial Intelligence
- Search Algorithms in AI
- A* Search Algorithm
- Uniform-Cost Search (Dijkstra for large Graphs)
- Introduction to Hill Climbing | Artificial Intelligence
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- Print all possible paths from top left to bottom right of a mXn matrix
- Unique paths in a Grid with Obstacles
- Unique paths covering every non-obstacle block exactly once in a grid
- Depth First Search or DFS for a Graph
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The reflex agent of AI directly maps states into action. Whenever these agents fail to operate in an environment where the state of mapping is too large and not easily performed by the agent, then the stated problem dissolves and sent to a problem-solving domain which breaks the large stored problem into the smaller storage area and resolves one by one. The final integrated action will be the desired outcomes.
On the basis of the problem and their working domain, different types of problem-solving agent defined and use at an atomic level without any internal state visible with a problem-solving algorithm. The problem-solving agent performs precisely by defining problems and several solutions. So we can say that problem solving is a part of artificial intelligence that encompasses a number of techniques such as a tree, B-tree, heuristic algorithms to solve a problem.
We can also say that a problem-solving agent is a result-driven agent and always focuses on satisfying the goals.
There are basically three types of problem in artificial intelligence:
1. Ignorable: In which solution steps can be ignored.
2. Recoverable: In which solution steps can be undone.
3. Irrecoverable: Solution steps cannot be undo.
Steps problem-solving in AI: The problem of AI is directly associated with the nature of humans and their activities. So we need a number of finite steps to solve a problem which makes human easy works.
These are the following steps which require to solve a problem :
- Problem definition: Detailed specification of inputs and acceptable system solutions.
- Problem analysis: Analyse the problem thoroughly.
- Knowledge Representation: collect detailed information about the problem and define all possible techniques.
- Problem-solving: Selection of best techniques.
Components to formulate the associated problem:
- Initial State: This state requires an initial state for the problem which starts the AI agent towards a specified goal. In this state new methods also initialize problem domain solving by a specific class.
- Action: This stage of problem formulation works with function with a specific class taken from the initial state and all possible actions done in this stage.
- Transition: This stage of problem formulation integrates the actual action done by the previous action stage and collects the final stage to forward it to their next stage.
- Goal test: This stage determines that the specified goal achieved by the integrated transition model or not, whenever the goal achieves stop the action and forward into the next stage to determines the cost to achieve the goal.
- Path costing: This component of problem-solving numerical assigned what will be the cost to achieve the goal. It requires all hardware software and human working cost.
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An Introduction to Problem-Solving using Search Algorithms for Beginners
This article was published as a part of the Data Science Blogathon
In computer science, problem-solving refers to artificial intelligence techniques, including various techniques such as forming efficient algorithms, heuristics, and performing root cause analysis to find desirable solutions.
The basic crux of artificial intelligence is to solve problems just like humans.
Examples of Problems in Artificial Intelligence
In today’s fast-paced digitized world, artificial intelligence techniques are used widely to automate systems that can use the resource and time efficiently. Some of the well-known problems experienced in everyday life are games and puzzles. Using AI techniques, we can solve these problems efficiently. In this sense, some of the most common problems resolved by AI are
- Travelling Salesman Problem
- Tower of Hanoi Problem
- Water-Jug Problem
- N-Queen Problem
- Crypt-arithmetic Problems
- Magic Squares
- Logical Puzzles and so on.
Table of Contents
Problem solving techniques.
- Properties of searching algorithms
Types of search algorithms
Uninformed search algorithms, comparison of various uninformed search algorithms, informed search algorithms, comparison of uninformed and informed search algorithms.
In artificial intelligence, problems can be solved by using searching algorithms, evolutionary computations, knowledge representations, etc.
In this article, I am going to discuss the various searching techniques that are used to solve a problem.
In general, searching is referred to as finding information one needs.
The process of problem-solving using searching consists of the following steps.
- Define the problem
- Analyze the problem
- Identification of possible solutions
- Choosing the optimal solution
Let’s discuss some of the essential properties of search algorithms.
Properties of search algorithms
A search algorithm is said to be complete when it gives a solution or returns any solution for a given random input.
If a solution found is best (lowest path cost) among all the solutions identified, then that solution is said to be an optimal one.
The time taken by an algorithm to complete its task is called time complexity. If the algorithm completes a task in a lesser amount of time, then it is an efficient one.
It is the maximum storage or memory taken by the algorithm at any time while searching.
These properties are also used to compare the efficiency of the different types of searching algorithms.
Now let’s see the types of the search algorithm.
Based on the search problems, we can classify the search algorithm as
- Uninformed search
- Informed search
The uninformed search algorithm does not have any domain knowledge such as closeness, location of the goal state, etc. it behaves in a brute-force way. It only knows the information about how to traverse the given tree and how to find the goal state. This algorithm is also known as the Blind search algorithm or Brute -Force algorithm.
The uninformed search strategies are of six types.
- Breadth-first search
- Depth-first search
- Depth-limited search
- Iterative deepening depth-first search
- Bidirectional search
- Uniform cost search
Let’s discuss these six strategies one by one.
1. Breadth-first search
It is of the most common search strategies. It generally starts from the root node and examines the neighbor nodes and then moves to the next level. It uses First-in First-out (FIFO) strategy as it gives the shortest path to achieving the solution.
BFS is used where the given problem is very small and space complexity is not considered.
Now, consider the following tree.
Here, let’s take node A as the start state and node F as the goal state.
The BFS algorithm starts with the start state and then goes to the next level and visits the node until it reaches the goal state.
In this example, it starts from A and then travel to the next level and visits B and C and then travel to the next level and visits D, E, F and G. Here, the goal state is defined as F. So, the traversal will stop at F.
The path of traversal is:
A —-> B —-> C —-> D —-> E —-> F
Let’s implement the same in python programming.
Advantages of BFS
- BFS will never be trapped in any unwanted nodes.
- If the graph has more than one solution, then BFS will return the optimal solution which provides the shortest path.
Disadvantages of BFS
- BFS stores all the nodes in the current level and then go to the next level. It requires a lot of memory to store the nodes.
- BFS takes more time to reach the goal state which is far away.
2. Depth-first search
The depth-first search uses Last-in, First-out (LIFO) strategy and hence it can be implemented by using stack. DFS uses backtracking. That is, it starts from the initial state and explores each path to its greatest depth before it moves to the next path.
DFS will follow
Root node —-> Left node —-> Right node
Now, consider the same example tree mentioned above.
Here, it starts from the start state A and then travels to B and then it goes to D. After reaching D, it backtracks to B. B is already visited, hence it goes to the next depth E and then backtracks to B. as it is already visited, it goes back to A. A is already visited. So, it goes to C and then to F. F is our goal state and it stops there.
A —-> B —-> D —-> E —-> C —-> F
The output path is as follows.
Advantages of DFS
- It takes lesser memory as compared to BFS.
- The time complexity is lesser when compared to BFS.
- DFS does not require much more search.
Disadvantages of DFS
- DFS does not always guarantee to give a solution.
- As DFS goes deep down, it may get trapped in an infinite loop.
3. Depth-limited search
Depth-limited works similarly to depth-first search. The difference here is that depth-limited search has a pre-defined limit up to which it can traverse the nodes. Depth-limited search solves one of the drawbacks of DFS as it does not go to an infinite path.
DLS ends its traversal if any of the following conditions exits.
It denotes that the given problem does not have any solutions.
Cut off Failure Value
It indicates that there is no solution for the problem within the given limit.
Now, consider the same example.
Let’s take A as the start node and C as the goal state and limit as 1.
The traversal first starts with node A and then goes to the next level 1 and the goal state C is there. It stops the traversal.
A —-> C
If we give C as the goal node and the limit as 0, the algorithm will not return any path as the goal node is not available within the given limit.
If we give the goal node as F and limit as 2, the path will be A, C, F.
Let’s implement DLS.
When we give C as goal node and 1 as limit the path will be as follows.
Advantages of DLS
- It takes lesser memory when compared to other search techniques.
Disadvantages of DLS
- DLS may not offer an optimal solution if the problem has more than one solution.
- DLS also encounters incompleteness.
4. Iterative deepening depth-first search
Iterative deepening depth-first search is a combination of depth-first search and breadth-first search. IDDFS find the best depth limit by gradually adding the limit until the defined goal state is reached.
Let me try to explain this with the same example tree.
Consider, A as the start node and E as the goal node. Let the maximum depth be 2.
The algorithm starts with A and goes to the next level and searches for E. If not found, it goes to the next level and finds E.
The path of traversal is
A —-> B —-> E
Let’s try to implement this.
The path generated is as follows.
Advantages of IDDFS
- IDDFS has the advantages of both BFS and DFS.
- It offers fast search and uses memory efficiently.
Disadvantages of IDDFS
- It does all the works of the previous stage again and again.
5. Bidirectional search
The bidirectional search algorithm is completely different from all other search strategies. It executes two simultaneous searches called forward-search and backwards-search and reaches the goal state. Here, the graph is divided into two smaller sub-graphs. In one graph, the search is started from the initial start state and in the other graph, the search is started from the goal state. When these two nodes intersect each other, the search will be terminated.
Bidirectional search requires both start and goal start to be well defined and the branching factor to be the same in the two directions.
Consider the below graph.
Here, the start state is E and the goal state is G. In one sub-graph, the search starts from E and in the other, the search starts from G. E will go to B and then A. G will go to C and then A. Here, both the traversal meets at A and hence the traversal ends.
E —-> B —-> A —-> C —-> G
Let’s implement the same in Python.
The path is generated as follows.
Advantages of bidirectional search
- This algorithm searches the graph fast.
- It requires less memory to complete its action.
Disadvantages of bidirectional search
- The goal state should be pre-defined.
- The graph is quite difficult to implement.
6. Uniform cost search
Uniform cost search is considered the best search algorithm for a weighted graph or graph with costs. It searches the graph by giving maximum priority to the lowest cumulative cost. Uniform cost search can be implemented using a priority queue.
Consider the below graph where each node has a pre-defined cost.
Here, S is the start node and G is the goal node.
From S, G can be reached in the following ways.
S, A, E, F, G -> 19
S, B, E, F, G -> 18
S, B, D, F, G -> 19
S, C, D, F, G -> 23
Here, the path with the least cost is S, B, E, F, G.
Let’s implement UCS in Python.
The optimal output path is generated.
Advantages of UCS
- This algorithm is optimal as the selection of paths is based on the lowest cost.
Disadvantages of UCS
- The algorithm does not consider how many steps it goes to reach the lowest path. This may result in an infinite loop also.
Now, let me compare the six different uninformed search strategies based on the time complexity.
This is all about uninformed search algorithms.
Let’s take a look at informed search algorithms.
The informed search algorithm is also called heuristic search or directed search. In contrast to uninformed search algorithms, informed search algorithms require details such as distance to reach the goal, steps to reach the goal, cost of the paths which makes this algorithm more efficient.
Here, the goal state can be achieved by using the heuristic function.
The heuristic function is used to achieve the goal state with the lowest cost possible. This function estimates how close a state is to the goal.
Let’s discuss some of the informed search strategies.
1. Greedy best-first search algorithm
Greedy best-first search uses the properties of both depth-first search and breadth-first search. Greedy best-first search traverses the node by selecting the path which appears best at the moment. The closest path is selected by using the heuristic function.
Consider the below graph with the heuristic values.
Here, A is the start node and H is the goal node.
Greedy best-first search first starts with A and then examines the next neighbour B and C. Here, the heuristics of B is 12 and C is 4. The best path at the moment is C and hence it goes to C. From C, it explores the neighbours F and G. the heuristics of F is 8 and G is 2. Hence it goes to G. From G, it goes to H whose heuristic is 0 which is also our goal state.
A —-> C —-> G —-> H
Let’s try this with Python.
The output path with the lowest cost is generated.
The time complexity of Greedy best-first search is O(b m ) in worst cases.
Advantages of Greedy best-first search
- Greedy best-first search is more efficient compared with breadth-first search and depth-first search.
Disadvantages of Greedy best-first search
- In the worst-case scenario, the greedy best-first search algorithm may behave like an unguided DFS.
- There are some possibilities for greedy best-first to get trapped in an infinite loop.
- The algorithm is not an optimal one.
Next, let’s discuss the other informed search algorithm called the A* search algorithm.
2. A* search algorithm
A* search algorithm is a combination of both uniform cost search and greedy best-first search algorithms. It uses the advantages of both with better memory usage. It uses a heuristic function to find the shortest path. A* search algorithm uses the sum of both the cost and heuristic of the node to find the best path.
Consider the following graph with the heuristics values as follows.
Let A be the start node and H be the goal node.
First, the algorithm will start with A. From A, it can go to B, C, H.
Note the point that A* search uses the sum of path cost and heuristics value to determine the path.
Here, from A to B, the sum of cost and heuristics is 1 + 3 = 4.
From A to C, it is 2 + 4 = 6.
From A to H, it is 7 + 0 = 7.
Here, the lowest cost is 4 and the path A to B is chosen. The other paths will be on hold.
Now, from B, it can go to D or E.
From A to B to D, the cost is 1 + 4 + 2 = 7.
From A to B to E, it is 1 + 6 + 6 = 13.
The lowest cost is 7. Path A to B to D is chosen and compared with other paths which are on hold.
Here, path A to C is of less cost. That is 6.
Hence, A to C is chosen and other paths are kept on hold.
From C, it can now go to F or G.
From A to C to F, the cost is 2 + 3 + 3 = 8.
From A to C to G, the cost is 2 + 2 + 1 = 5.
The lowest cost is 5 which is also lesser than other paths which are on hold. Hence, path A to G is chosen.
From G, it can go to H whose cost is 2 + 2 + 2 + 0 = 6.
Here, 6 is lesser than other paths cost which is on hold.
Also, H is our goal state. The algorithm will terminate here.
Let’s try this in Python.
The output is given as
The time complexity of the A* search is O(b^d) where b is the branching factor.
Advantages of A* search algorithm
- This algorithm is best when compared with other algorithms.
- This algorithm can be used to solve very complex problems also it is an optimal one.
Disadvantages of A* search algorithm
- The A* search is based on heuristics and cost. It may not produce the shortest path.
- The usage of memory is more as it keeps all the nodes in the memory.
Now, let’s compare uninformed and informed search strategies.
Uninformed search is also known as blind search whereas informed search is also called heuristics search. Uniformed search does not require much information. Informed search requires domain-specific details. Compared to uninformed search, informed search strategies are more efficient and the time complexity of uninformed search strategies is more. Informed search handles the problem better than blind search.
Search algorithms are used in games, stored databases, virtual search spaces, quantum computers, and so on. In this article, we have discussed some of the important search strategies and how to use them to solve the problems in AI and this is not the end. There are several algorithms to solve any problem. Nowadays, AI is growing rapidly and applies to many real-life problems. Keep learning! Keep practicing!
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Problem Solving Techniques in Artificial Intelligence (AI)
Problem-solving is commonly known as the method to reach the desired goal or find a solution to a given situation. In computer science, problem-solving refers to artificial intelligence techniques, including various techniques such as forming efficient algorithms, heuristics, and performing root cause analysis to find desirable solutions. Problem-solving in Artificial Intelligence usually refers to researching a solution to a problem by performing logical algorithms, utilizing polynomial and differential equations, and executing them using modeling paradigms. There can be various solutions to a single problem, which are achieved by different heuristics. Also, some problems have unique solutions. It all rests on the nature of the given problem.
Table of contents:
Examples of Problems in Artificial Intelligence
What is a reflex agent, problem solving techniques, searching algorithms, types of uninformed searching algorithms, evolutionary computation, genetic algorithms, applications of ai in real world, why problem solving is important in ai.
- Problem formulation in AI
Problem-solving agents in artificial intelligence
Steps of problem solving in ai, ai methods of problem solving.
Developers worldwide are using artificial intelligence to automate systems for efficient utilization of time and resources. Some of the most common problems encountered in day-to-day life are games and puzzles. These can be solved efficiently by using artificial intelligence algorithms. Ranging from mathematical puzzles including crypto-arithmetic and magic squares, logical puzzles including Boolean formulas and N-Queens to popular games like Sudoku and Chess, these problem-solving techniques are used to form a solution for all these. Therefore, some of the most prevalent problems that artificial intelligence has resolved are the following:
- N-Queen problem
- Tower of Hanoi Problem
- Travelling Salesman Problem
- Water-Jug Problem
There are five primary agents used in Artificial Intelligence based on their capability of perceiving intelligence. These agents are the following:
- Simple Reflex Agents
- Model-Based Reflex Agents
- Goal-Based Agents
- Utility-Based Agents
- Learning Agents
These agents prove helpful in the mapping of states and actions. While solving a complex problem, these agents often fail to comprehend the next step adequately; thus, problem-solving agents solve such scenarios. These agents use techniques like B-tree and heuristic algorithms to solve problems using artificial intelligence.
Artificial Intelligence is beneficial for solving complex problems due to its efficient methods of solving. Following are some of the standard problem-solving techniques used in AI. You can explore about other problem-solving techniques apart from searching.
The heuristic method helps comprehend a problem and devises a solution based purely on experiments and trial and error methods. However, these heuristics do not often provide the best optimal solution to a specific problem. Instead, these undoubtedly offer efficient solutions to attain immediate goals. Therefore, the developers utilize these when classic methods do not provide an efficient solution for the problem. Since heuristics only provide time-efficient solutions and compromise accuracy, these are combined with optimization algorithms to improve efficiency.
Example: Travelling Salesman Problem
The most common example of using heuristic is the Travelling Salesman problem. There is a provided list of cities and their distances. The user has to find the optimal route for the Salesman to return to the starting city after visiting every city on the list. The greedy algorithms solve this NP-Hard problem by finding the optimal solution. According to this heuristic, picking the best next step in every current city provides the best solution.
Searching is one of the primary methods of solving any problem in AI. Rational agents or problem-solving agents use these searching algorithms to find optimal solutions. These problem-solving agents are often goal-based and utilize atomic representation. Moreover, these searching algorithms possess completeness, optimality, time complexity, and space complexity properties based on the quality of the solution provided by them.
Types of Searching Algorithms
There are following two main types of searching algorithms:
These algorithms use basic domain knowledge and comprehend available information regarding a specified problem as a guideline for optimal solutions. The solutions provided by informed search algorithms are more efficient than uninformed search algorithms.
Types of informed Search Algorithms
There are following main two types of informed search algorithms:
- Greedy Search
These algorithms do not have the privilege of using basic domain knowledge, such as the desired goal’s closeness. It contains information regarding traversing a tree and identifying leaf and goal nodes . Uninformed search also goes by the name of blind search because while traversing, there is no specific information about the initial state and test for the goal. This search goes through every node till reaching the desired destination.
There are the following main five types of uninformed search algorithms:
- Breadth-First Search
- Depth First Search
- Uniform Cost Search
- Iterative Deepening Depth First Search
- Bidirectional Search
This problem-solving method utilizes the well-known evolution concept. The theory of evolution works on the principle of survival of the fittest. It states that the organism which can cope well with their environment in a challenging or changing environment and reproduce, their future generations gradually inherit the coping mechanism, generating the diversity in new child organisms. In this way, the new organisms are not mere copies of the old ones but have the mixes of characteristics that go along with that harsh environment. Humans are the most prominent example of the evolution process that has evolved and diversified because of the accumulation of favorable mutations over countless generations.
In AI, the evolution concept refers to the trial and error technique:
- Randomly change the old solution to come up with the new one. Does it give better results? If yes, then keep and throw away the rest of the solutions. If not, then discard it.
- Go to point 1.
The evolution theory is the basis of genetic algorithms. These algorithms use the direct random search method. The developers calculate the fit function to cross the two fittest options to create a favorable child. The developers gather the population data and then evaluate each member to calculate everyone’s fitness. It is estimated by how well each member fits with the desired requirement. Then the developers use various selection methods to keep the best members. Some of the ways are the following:
- Rank Selection
- Tournament Selection
- Steady Selection
- Roulette Wheel Selection (Fitness Proportionate Selection)
Although genetic algorithms do not always work best, they do not break easily, and the inputs change slightly. The developers commonly use genetic algorithms to generate a high-level solution to optimization and search problems by relying on bio-inspired operations such as mutation, crossover, and selection.
The problem-solving techniques help in improving the performance of programs. The AI-based searching algorithms provide high precision and maximum accuracy to results. These algorithms are faster than others in execution and offer multiple searching methods depending upon the problem faced. Implementing heuristics allows the devising to conceptually more straightforward algorithms with cheaper computational costs compared to using optimal algorithms. Evolutionary computations also help in optimization and search problems. Overall, these techniques are the basis for solving high-level problems in AI such as chess algorithms, and hill-climbing problems.
One of the most critical advantages of AI is its ability to sift through large quantities of data in a limited period. These assist researchers in identifying areas of focus for their studies. A recent ground-breaking breakthrough on the condition of Amyotrophic Lateral Sclerosis (ALS) was made thanks to a collaboration between Barrow Neurological Institute and IBM Watson Health, an artificial intelligence firm. IBM Watson, an artificial intelligence computer, studied tens of thousands of research papers and identified new genes related to ALS.
The finding provides ALS researchers with new knowledge to create new drug targets and treatments to fight one of the world’s most deadly diseases.
Another promising application of AI in healthcare is its ability to predict drug treatment results. For example, cancer patients are often given the same medication and monitored to see if it is successful. AI uses data to predict which patients would benefit from a specific medication, resulting in a highly customized approach that saves time and money.
Transforming Learning Process
Students at Georgia Tech University in the United States were shocked to learn that their supportive teaching assistant had been a robot all along. Following some teething issues, the robot began answering students’ questions with 97 percent accuracy.
After conducting studies, the university discovered that one of the leading causes of student dropout is a lack of support.
People learn in various ways, at different speeds, and from different locations. Artificial intelligence can usher in a world in which humans learn in a far more personalized manner. However, no educational system can afford a tutor for every student that AI might help. Artificial tutors, designed to look and sound as human as possible, may lead the way in providing individualized education.
The ability to analyze massive data, much like in healthcare, may help change wildlife conservation. Humans can see where animals go and what habitats need to preserve by monitoring their movements, for example. This research uses computer power to determine Montana’s best locations for creating wildlife corridors for wolverines and grizzly bears. Wildlife corridors are long stretches of protected land that connect biologically significant regions, allowing animals to travel safely through the wilderness.
Given the high-profile crashes involving self-driving cars this year, AI in this area has the potential to reduce fatalities and injuries on the highways significantly.
According to a Private University study, self-driving cars would not only minimize traffic-related deaths and injuries, but they may also change lifestyles. AI may have more time to work or entertain and may have more options for where humans base themselves. Per the report, Self-driving cars and shared transportation can influence where people choose to live due to increased comfort and reduced cognitive load.
Decoding any type of problem needs specific organized measures to be observed. Identical is the matter of solving issues by AI. The following are the details:
- Goal –In this phase as soon as a crisis appears, the AI agent puts a goal or a mark. This needs the agent to thoroughly examine and clarify the issue. This is a vital action as if the goal for the issue is poorly developed then all the actions carried out to achieve the goal would be useless.
- Problem Description –This is one major stage of problem-solving. Whenever a problem occurs, then the agent chooses what measures must be carried out to run to the developed goal. This is accomplished in the subsequent actions:
- Describing the State –A state area can be described as a group of all the accurate conditions in which an agent can be joined when discovering a key to the crisis.
- Specifying Primary State –For an agent to begin cracking the issue, it must begin from a state. The primary state from where the agent begins performing is directed to the primary state.
- Collect Details –Now the agent collects data and utilizes the data needed by it to fix the issue. These details will be collected with one-time incidents as well as present pieces of knowledge.
- Designing the Changes –Some issues are undersized and so these can be deciphered efficiently. But most of the time issues will be such where sound planning and implementation are required. Hence this needs appropriate data structures and managing processes well in advance.
Problem Formulation in AI
It is one of the basic stages of problem-solving that determines what measure should be brought to fulfill the developed goal. Problem formulation is the stage in problem description that is utilized to comprehend and choose a course of activity that must be evaluated to reach a goal. If there is more than one method an agent can attain its objective, then it generates intricacy in terms of truly reaching the goal as there would be too numerous measures and courses that the AI entity can carry to achieve the goal that it induces chaos and a tremendous decline in the efficiency. Problem formulation can be accomplished in many stages such as the description of the initial condition of the agent, choosing probable steps that the agent can bear, and design of transition standards to define the efforts of the agent.
Here the issue is split into sub-issues. The effects of the different measures carried out in cracking the last sub-problem are delivered to the following subproblem and the integrated outcome of the subproblems ushers to the definitive solution. This needs appropriate planning and implementation of changes.
- Testing with the Goal State –In this phase, the outcomes generated from the agent are analogized with that of the objective state. If the goal has been achieved, then the agents block any additional activities and the issue arrives at the final state. But if the goal is not achieved then the agent persists to discover activities to run to the goal.
- Estimating the Expense of Path carried –Whenever an agent carries a course to decipher a situation it permits a numeric value (or price) to that course. These prices are then estimated by utilizing a price function. The estimated consequence is hence employed in the agent’s implementation action. The solution which is achieved with the minimum or most subordinate price of the path is called the perfect solution .
Performance benchmark is one of the vital things in AI problem solving which determines the value of the algorithm utilized to fix the issue. There are four methods in which the execution of an algorithm is calculated. These are as follows:
- Totality – Totality calculates the algorithm’s assurance to discover the solution for the issue if there is any solution for it.
- Optimality – This step is utilized to calculate the tracking methods which discover an optimal solution to the presented issue.
- Duration Complexity – This calculates the portion of time the algorithm abides to run till the key for the assigned issue.
- Space Intricacy – This is the standard that is employed to specify the quantity of area (in memory) that the algorithm needs to execute the quest.
Based on the kind of problem, various agents utilize various methods like trees, heuristics, b-trees, etc mentioned above to achieve the goal of the problem. Basically, the steps involved are:
- Goal Formulation
Problem-solving in AI is prevalent day by day. Computer researchers are utilizing Artificial Intelligence to automate their jobs in day-to-day practice to preserve their actions and duration. Games and riddles are real-time models of a current situation. These can be fixed by utilizing the most practical AI algorithms. Mathematical riddles such as magic squares and crypto arithmetic are being utilized in the most widespread competitions such as Chess. Problem-solving in AI is being utilized to embark on these all issues and make their solutions most efficacious.
Artificial Intelligence is helping developers in creating perfect software and design the software as per the conditions. The dead-end issues are also can be decrypted now with the service of Artificial Intelligence algorithms. In problem-solving AI utilize some of the agents. Various agents assist to specify movements and conditions. But they usually become ineffective when it comes to decrypting difficult issues of the real world.
Based on the issue and their functioning environment, various kinds of problem-solving agents are described and utilized at an atomic class without any inner condition perceptible with a problem-solving algorithm. The problem-solving agent functions specifically by determining issues and various solutions. So we can express that problem solving is a piece of artificial intelligence that contains several methods such as a tree, B-tree, and heuristic algorithms to fix an issue.
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- Problem Solving in games such as “ Sudoku ” can be an example. It can be done by building an artificially intelligent system to solve that particular problem. To do this, one needs to define the problem statements first and then generating the solution by keeping the conditions in mind.
- Travelling Salesman Problem.
- Tower of Hanoi Problem.
- Water-Jug Problem.
- N-Queen Problem.
- In general, searching refers to as finding information one needs.
- Searching is the most commonly used technique of problem solving in artificial intelligence.
- The searching algorithm helps us to search for solution of particular problem.
- Problems are the issues which comes across any system. A solution is needed to solve that particular problem.
Steps : Solve Problem Using Artificial Intelligence
- Defining The Problem : The definition of the problem must be included precisely. It should contain the possible initial as well as final situations which should result in acceptable solution.
- Analyzing The Problem : Analyzing the problem and its requirement must be done as few features can have immense impact on the resulting solution.
- Identification Of Solutions : This phase generates reasonable amount of solutions to the given problem in a particular range.
- Choosing a Solution : From all the identified solutions, the best solution is chosen basis on the results produced by respective solutions.
- Implementation : After choosing the best solution, its implementation is done.
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- What is problem solving agent in Artificial...
What is problem solving agent in Artificial Intelligence?
Could someone tell me what is the problem-solving agent in Artificial Intelligence?
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Problem-solving agent in Artificial Intelligence is goal-based agents that focus on goals, is one embodiment of a group of algorithms, and techniques to solve a well-defined problem in the area of Artificial Intelligence. And these agents are different from reflex agents who just have to map states into actions and can’t map when storing and learning both are bigger. The different stages that Problem-solving agents perform, to arrive at a desired state or solution are:
- Articulating or expressing the desired goal and the problem is tried upon, clearly.
- Explore and examine
- Find the solution from the various algorithms on the table.
- The final step is Execution!
You would know more of these jargons of AI once you are well through into the domain. Considering the job prospects, just knowing a technology doesn’t have any impact on the interview, how well do you know? Are you well versed in the technology? And the work experience you have, things like these often matter. And for these, I would recommend you to enroll in and end-to-end AI course from Intellipaat to help. Also, I would like you to watch our YouTube video on Artificial Intelligence for Beginners to help you get a better understanding.
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Searching is one of the classic areas of AI.
A problem is a tuple $(S, s, A, \rho, G, P)$ where
Example: A water jug problem
You have a two-gallon jug and a one-gallon jug; neither have any measuring marks on them at all. Initially both are empty. You need to get exactly one gallon into the two-gallon jug. Formally:
A graphical view of the transition function (initial state shaded, goal states outlined bold):
And a tabular view:
To solve this problem, an agent would start at the initial state and explore the state space by following links until it arrived in a goal state. A solution to the water jug problem is a path from the initial state to a goal state .
There are an infinite number of solutions. Sometimes we are interested in the solution with the smallest path cost; more on this later.
Awww Man.... Why are we studying this?
Even if they’re not completely right, there are still zillions of problems that can be formulated in problem spaces, e.g.
State finding vs. action sequence finding.
A fundamental distinction:
Offline vs. Online Problems
In an online problem, the agent doesn’t even know what the state space is, and has to build a model of it as it acts. In an offline problem, percepts don’t matter at all. An agent can figure out the entire action sequence before doing anything at all .
Offline Example : Vacuum World with two rooms, cleaning always works, a square once cleaned stays clean. States are 1 – 8, goal states are 1 and 5.
Sensorless (Conformant) Problems
The agent doesn’t know where it is. We can use belief states (sets of states that the agent might be in). Example from above deterministic, static, single-agent vacuum world:
Note the goal states are 1 and 5. If a state 15 was reachable, it would be a goal too.
Contingency Problem: The agent doesn’t know what effect its actions will have. This could be due to the environment being partially observable, or because of another agent. Ways to handle this:
Example: Partially observable vacuum world (meaning you don’t know the status of the other square) in which sucking in a clean square may make it dirty.
Can also model contingency problems is with "AND-OR graphs".
Example: find a winning strategy for Nim if there are only five stones in one row left. You are player square. You win if it is player circle’s turn with zero stones left.
In general then, a solution is a subtree in which
If the tree has only OR nodes, then the solution is just a path.
Hey, we know what a problem is, what a problem space is, and even what a solution is, but how exactly do we search the space ? Well there are zillions of approaches:
Types of Problem Solving Tasks
Agents may be asked to be
An algorithm is
Example: The water jug problem with 4 and 3 gallon jugs. Cost is 1 point per gallon used when filling, 1 point to make a transfer, 5 points per gallon emptied (since it makes a mess). The search tree might start off like this:
Search trees have
The complexity of most search algorithms can be written as a function of one or more of $b$, $d$ and $m$.
In general though there may be more states than there are fundamental particles in the universe. But we need to find a solution. Usually is helpful to
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Home » Machine Learning/Artificial Intelligence
Problem Solving in Artificial Intelligence
In this article, you will study about the problem-solving approach in Artificial Intelligence . You will learn how an agent tackles the problem and what steps are involved in solving it? Submitted by Monika Sharma , on May 29, 2019
The aim of Artificial Intelligence is to develop a system which can solve the various problems on its own. But the challenge is, to understand a problem, a system must predict and convert the problem in its understandable form. That is, when an agent confronts a problem, it should first sense the problem, and this information that the agent gets through the sensing should be converted into machine-understandable form. For this, a particular sequence should be followed by the agent in which a particular format for the representation of agent's knowledge is defined and each time a problem arises, the agent can follow that particular approach to find a solution to it .
The steps involved in solving a problem (by an agent based on Artificial Intelligence ) are:
1) Define a problem
Whenever a problem arises, the agent must first define a problem to an extent so that a particular state space can be represented through it. Analyzing and defining the problem is a very important step because if the problem is understood something which is different than the actual problem, then the whole problem-solving process by the agent is of no use.
2) Form the state space
Convert the problem statement into state space. A state space is the collection of all the possible valid states that an agent can reside in. But here, all the possible states are chosen which can exist according to the current problem. The rest are ignored while dealing with this particular problem.
3) Gather knowledge
collect and isolate the knowledge which is required by the agent to solve the current problem. This knowledge gathering is done from both the pre-embedded knowledge in the system and the knowledge it has gathered through the past experiences in solving the same type of problem earlier.
4) Planning-(Decide data structure and control strategy)
A problem may not always be an isolated problem. It may contain various related problems as well or some related areas where the decision made with respect to the current problem can affect those areas. So, a well-suited data structure and a relevant control strategy must be decided before attempting to solve the problem.
5) Applying and executing
After all the gathering of knowledge and planning the strategies, the knowledge should be applied and the plans should be executed in a systematic way so s to reach the goal state in the most efficient and fruitful manner.
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1) Define a problem · 2) Form the state space · 3) Gather knowledge · 4) Planning-(Decide data structure and control strategy) · 5) Applying and