Means Ends Analysis: Fundamentals and Applications

By Fouad Sabry

What Is Means Ends Analysis

Means-ends analysis, more often known as MEA, is a method for solving problems that is widely used in artificial intelligence (AI) for the purpose of limiting search in AI programs.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Means-Ends Analysis

Chapter 2: Causal Layered Analysis

Chapter 3: Knowledge Representation and Reasoning

Chapter 4: Automated Reasoning

Chapter 5: Intelligent Control

Chapter 6: Cognitive Load

Chapter 7: Mathematical Proof

Chapter 8: Polytely

Chapter 9: Gap Analysis

Chapter 10: Hill Climbing

(II) Answering the public top questions about means ends analysis.

(III) Real world examples for the usage of means ends analysis in many fields.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of means ends analysis.

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• Language: English
• Publisher: One Billion Knowledgeable
• Release date: Jun 29, 2023

Book preview

Chapter 1: Means–ends analysis

Means–ends analysis, often known as MEA, is a problem-solving method that is widely used in the field of artificial intelligence (AI), specifically for the purpose of restricting search in AI algorithms.

It is also a strategy that has been used as a tool for creative thinking at least since the 1950s and is most usually discussed in engineering texts that cover design processes. There is also a connection between MEA and the means–ends chain method, which is often used in consumer behavior research. When beginning a mathematical proof, it is also helpful to write down one's ideas in order to clarify them.

Goal-based problem solving is an important aspect of intelligent behavior that is studied in AI. This is a framework in which the solution to a problem can be described by finding a sequence of actions that lead to a desirable goal. Goal-based problem solving is an example of an important aspect of intelligent behavior. It is expected that a goal-seeking system would have connections to its external environment via sensory channels and motor channels. Sensory channels will allow the system to receive information about the environment, while motor channels will allow the system to act on the environment. (The word afferent is used to describe sensory flows that go inward, while the term efferent is used to describe motor orders that travel outward.) In addition to this, the system has some ways of storing in a memory information on the status of the environment (known as afferent information), as well as information regarding activities (efferent information). Building up connections, whether they be basic or complicated, between specific changes in states and specific activities that would bring about these changes is necessary to increase one's ability to achieve their objectives. The process of finding and putting together a series of steps that will get you from where you are now to where you want to be is what we mean when we talk about searching. While it is possible that this tactic might be suitable for the learning and solution of problems by machines, it is not necessarily recommended for use by people (e.g. cognitive load theory and its implications).

In the process of problem-solving, the MEA approach is a method that may be used to control search. When a present state and an ideal state are considered, a course of action is selected that will bring about a narrowing of the gap between the two. The action is carried out on the present state in order to generate a new state, and the procedure is then repeated in a recursive manner to both the newly generated state and the desired state.

It is important to keep in mind that in order for MEA to be successful, the system that seeks goals must have a way to associate with any type of measurable difference the behaviors that are pertinent to minimizing that difference. As a result of the possibility that some of the attempted sequences of actions will be unsuccessful, other sequences will need to be capable of detecting the amount of progress that is being made by the system (the changes in the gaps between the current state and the one that is intended).

When there is information available describing the significance of differences, the difference that is deemed to be the most significant is chosen first to further increase the overall performance of MEA in comparison to that of other brute-force search algorithms. However, even without the ordering of differences according to importance, MEA improves over other search heuristics (again, in the average case) by focusing the problem solving on the actual differences between the current state and that of the goal. This improves MEA's performance over other search heuristics.

In 1961, Allen Newell and Herbert A. Simon released their computer problem-solving software known as General Problem Solver, which was the first time the MEA approach as a problem-solving strategy was presented to the public (GPS). In that particular implementation, the correlation between differences and actions, which are also referred to as operators, is supplied a priori inside the system as knowledge. (Within GPS, this information was shown in the form of a table of links.)

When the action and side-effects of applying an operator are able to be penetrated, the search may be able to choose the appropriate operators by inspecting the operators themselves and do away with the need for a table of connections. This second situation, of which STRIPS, an automated planning computer software, is the typical example, enables task-independent correlation of differences to the operators who minimize them.

Another system that made use of MEA was Prodigy, which was a problem solver that was built as part of a wider learning-assisted automated planning effort that was initiated at Carnegie Mellon University by Jaime Carbonell, Steven Minton, and Craig Knoblock.

A piece of software known as Multilevel Flow Modeling was created by Professor Morten Lind of the