Means ends analysis and Knowledge in the context of Artificial Intelligence.

2. Means-Ends Analysis
Means-Ends Analysis (MEA) is a problem solving technique used commonly in Artificial Intelligence (AI) for
limiting search in AI programs.

3. Problem-solving as search
An important aspect of intelligent behavior as studied in AI is goal-based problem solving, a framework in
which the solution of a problem can be described by finding a sequence of actions that lead to a desirable goal.
A goal-seeking system is supposed to be connected to its outside environment by sensory channels through
which it receives information about the environment and motor channels through which it acts on the
environment. Ability to attain goals depends on building up associations, simple or complex, between
particular changes in states and particular actions that will bring these changes about. Search is the
process of discovery and assembly of sequences of actions that will lead from a given state to a desired
state.

4. How MEA works?
The MEA technique is a strategy to control search in problem-solving. Given a current state and a goal state, an
action is chosen which will reduce the difference between the two. The action is performed on the current state
to produce a new state, and the process is recursively applied to this new state and the goal state.
Note that, in order for MEA to be effective, the goal-seeking system must have a means of associating to
any kind of detectable difference those actions that are relevant to reducing that difference. It must also
have means for detecting the progress it is making (the changes in the differences between the actual and
the desired state), as some attempted sequences of actions may fail and, hence, some alternate sequences
may be tried.
When knowledge is available concerning the importance of differences, the most important difference is
selected first to further improve the average performance of MEA over other brute-force search strategies.
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.

5. Some AI systems using MEA
The MEA technique as a problem-solving strategy was first introduced in 1961 by Allen Newell and Herbert A.
Simon in their computer problem-solving program General Problem Solver (GPS).In that implementation, the
correspondence between differences and actions, also called operators, is provided a priori as knowledge in the
system. (In GPS this knowledge was in the form of table of connections.)
Prodigy, a problem solver developed in a larger learning-assisted automated planning project started
at Carnegie Mellon University by Jaime Carbonell, Steven Minton and Craig Knoblock, is another system
that used MEA.

6. Knowledge in Learning
Knowledge representation and knowledge engineering are central to AI research. Many of the problems
machines are expected to solve will require extensive knowledge about the world. Among the things that AI
needs to represent are: objects, properties, categories and relations between objects; situations, events, states and
time; causes and effects; knowledge about knowledge (what we know about what other people know); and many
other, less well researched domains. A representation of "what exists" is an ontology: the set of objects, relations,
concepts and so on that the machine knows about. The most general are called upper ontologies, which attempt
to provide a foundation for all other knowledge.

7. Among the most difficult problems in knowledge representation are:
Default reasoning and the qualification problem
Many of the things people know take the form of "working assumptions." For example, if a bird comes up in
conversation, people typically picture an animal that is fist sized, sings, and flies. None of these things are true
about all birds. John McCarthy identified this problem in 1969 as the qualification problem: for any
commonsense rule that AI researchers care to represent, there tend to be a huge number of exceptions. Almost
nothing is simply true or false in the way that abstract logic requires. AI research has explored a number of
solutions to this problem.
The breadth of commonsense knowledge
The number of atomic facts that the average person knows is astronomical. Research projects that
attempt to build a complete knowledge base of commonsense knowledge(e.g., Cyc) require enormous
amounts of laborious ontological engineering—they must be built, by hand, one complicated concept at
a time. A major goal is to have the computer understand enough concepts to be able to learn by reading
from sources like the internet, and thus be able to add to its own ontology.

8. The sub-symbolic form of some commonsense knowledge
Much of what people know is not represented as "facts" or "statements" that they could express verbally. For
example, a chess master will avoid a particular chess position because it "feels too exposed" or an art critic can
take one look at a statue and instantly realize that it is a fake. These are intuitions or tendencies that are
represented in the brain non-consciously and sub-symbolically. Knowledge like this informs, supports and
provides a context for symbolic, conscious knowledge. As with the related problem of sub-symbolic reasoning,
it is hoped that situated AI, computational intelligence, or statistical AI will provide ways to represent this kind
of knowledge.

9. PROBLEM REDUCTION
In computability theory and computational complexity theory, a reduction is an algorithm for transforming one
problem into another problem. A reduction from one problem to another may be used to show that the second
problem is at least as difficult as the first. The mathematical structure generated on a set of problems by the
reductions of a particular type generally forms a pre order, whose equivalence class may be used to define
degrees of unsolvability and complexity classes. From the viewpoint of efficient utilization of human knowledge
in complex decision-making problems, the inference procedure under uncertainty is becoming more important
for the problem-reduction method and expert systems.

10. References
 https://en.wikipedia.org/wiki/Means-ends_analysis
 https://notes.blogspot.com/problem-reduction-with-
ao-algorithm.html