Basics of Means End Analysis (MEA) in Artificial Intelligence (AI)
Means end analysis (MEA) is an important concept in artificial intelligence (AI) because it enhances problem resolution. MEA solves problems by defining the goal and establishing the right action plan. This technique is used in AI programs to limit search. <!--more--> This article explains how MEA works and provides the algorithm steps used to implement it. It also provides an example of how a problem is solved using means end analysis. This article also explains how this technique is used in real-life applications.
Introduction to MEA and problem-solving in AI
Problem-solving in artificial intelligence is the application of heuristics, root cause analysis, and algorithms to provide solutions to AI problems.
It is an effective way of reaching a target goal from a problematic state. This process begins with the collection of data relating to the problem. This data is then analyzed to establish a suitable solution.
Means end analysis is a technique used to solve problems in AI programs. This technique combines forward and backward strategies to solve complex problems. With these mixed strategies, complex problems can be tackled first, followed by smaller ones.
In this technique, the system evaluates the differences between the current state or position and the target or goal state. It then decides the best action to be undertaken to reach the end goal.
How MEA works
Means end analysis uses the following processes to achieve its objectives:
- First, the system evaluates the current state to establish whether there is a problem. If a problem is identified, then it means that an action should be taken to correct it.
- The second step involves defining the target or desired goal that needs to be achieved.
- The target goal is split into sub-goals, that are further split into other smaller goals.
- This step involves establishing the actions or operations that will be carried out to achieve the end state.
- In this step, all the sub-goals are linked with corresponding executable actions (operations).
- After that is done, intermediate steps are undertaken to solve the problems in the current state. The chosen operators will be applied to reduce the differences between the current state and the end state.
- This step involves tracking all the changes made to the actual state. Changes are made until the target state is achieved.
The following image shows how the target goal is divided into sub-goals, that are then linked with executable actions.
Algorithm steps for Means End Analysis
The following are the algorithmic steps for means end analysis:
- Conduct a study to assess the status of the current state. This can be done at a macro or micro level.
- Capture the problems in the current state and define the target state. This can also be done at a macro or micro level.
- Make a comparison between the current state and the end state that you defined. If these states are the same, then perform no further action. This is an indication that the problem has been tackled. If the two states are not the same, then move to step 4.
- Record the differences between the two states at the two aforementioned levels (macro and micro).
- Transform these differences into adjustments to the current state.
- Determine the right action for implementing the adjustments in step 5.
- Execute the changes and compare the results with the target goal.
- If there are still some differences between the current state and the target state, perform course correction until the end goal is achieved.
Example of problem-solving in Means End Analysis
Let’s assume that we have the following initial state.
We want to apply the concept of means end analysis to establish whether there are any adjustments needed. The first step is to evaluate the initial state, and compare it with the end goal to establish whether there are any differences between the two states.
The following image shows a comparison between the initial state and the target state.
The image above shows that there is a difference between the current state and the target state. This indicates that there is a need to make adjustments to the current state to reach the end goal.
The goal can be divided into sub-goals that are linked with executable actions or operations.
The following are the three operators that can be used to solve the problem.
1. Delete operator: The dot symbol at the top right corner in the initial state does not exist in the goal state. The dot symbol can be removed by applying the delete operator.
2. Move operator: We will then compare the new state with the end state. The green diamond in the new state is inside the circle while the green diamond in the end state is at the top right corner. We will move this diamond symbol to the right position by applying the move operator.
3. Expand operator: After evaluating the new state generated in step 2, we find that the diamond symbol is smaller than the one in the end state. We can increase the size of this symbol by applying the expand operator.
After applying the three operators above, we will find that the state in step 3 is the same as the end state. There are no differences between these two states, which means that the problem has been solved.
Applications of Means End Analysis
Means end analysis can be applied in the following fields:
Organizational planning
Means end analysis is used in organizations to facilitate general management. It helps organizational managers to conduct planning to achieve the objectives of the organization. The management reaches the desired goal by dividing the main goals into sub-goals that are linked with actionable tasks.
Business transformation
This technique is used to implement transformation projects. If there are any desired changes in the current state of a business project, means end analysis is applied to establish the new processes to be implemented. The processes are split into sub-processes to enhance effective implementation.
Gap analysis
Gap analysis is the comparison between the current performance and the required performance. Means end analysis is applied in this field to compare the existing technology and the desired technology in organizations. Various operations are applied to fill the existing gap in technology.
Conclusion
This article has provided an overview of means end analysis and how it works. This is an important technique that makes it possible to solve complex problems in AI programs.
To summarize:
- We have gained an overview of problem-solving in artificial intelligence. We have also gained an understanding of means end analysis.
- We have learned the various steps taken in means end analysis to reach the desired state.
- We have gained an overview of the algorithm steps for means end analysis.
- We have learned an example of problem-solving in means end analysis.
- We have gone through some of the applications of means end analysis.
Happy learning!
Peer Review Contributions by: Peter Kayere
