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9/18/16

Artificial Intelligence Research Fields.

Introduction.

In this article we'll cover the four main areas of the AI Research.


Inference.

One of earliest & still developed current in the AI Research is 'automatic inference mechanisms'.

Based on the 'formal logic' achievments, these strive for 'effective deduction algorithms'.

It's about mechanical formulation of logical consequences of knowledge base, with the use of the 'inference rules'.

This field used many formal systems, as:
- propositional caclulus,
- first order predicate calculus,
- non-classic logics.

Automatic inference methods are base for the expert systems, and for the statement proving systems.


Search.

Issues of searching large spaces in an effective manner have uses in Artificial Intelligence Research.

Problem-solving paradigm used in this field assumes that a problem is characterized by a possible states space with a certain number of distinct end states that represent acceptable solutions and by operators set that allow to move through this space.

Finding (best) solution can be reduced to finding (best according to a certain criteria) a sequence of operators that guide from initial problem state into one of end states.

Search is about finding optimal solution using as small as possible costs, including memory use cost & computing time cost; In a case of board games this might be about finding a move that maximizes a chance of victory, considering the actual situation on board & possible player moves. This leads to searching the 'game tree' constructed by considering possible player moves, then possible opponent moves, etc.

Both in searching for problem solution, as well as in board games, exhaustive search (in a complete states search space or in a complete game tree) is beyond our means (with an exception of trivially small problem spaces or boards). That's why heuristic methods are developed for searching, which do not guarantee effectiveness increase in a pessimistic case, but significiantly improve effectiveness on the average. These are based on the use of 'heuristic functions', carefully designed by a system's constructor for numeric estimation of states' quality (based on the distance from the acceptable end state) or for numeric estimation of a game situation (considering the chances of a win).


Planning.

Planning is to a certain extent a result of joining automatic inference mechanisms with the problem solving.

From a 'planning system' we expect to find - in most basic case - a plan of a problem solution in a manner that is more effective than when using the 'search method', even with heuristics. A 'planning system' can search the space using the knowledge about individual operator effects that is provided to it. This knowledge, contained usually in a certain logical formality (for example, in a subset of a predicate language), describes problem state changes that come into effect after using a certain operator. This enables inference about states reachable from initial state using different operator sequences. Occasionally this eliminates the need for search, and in most cases significiantly reduces the search scope at least.

It's said about the Intelligent Planning Systems, that these 'infer about their own actions'.


Example:

We can have possible logical formulas (individually representable for example as expression trees or a series of 0's & 1's) represented as a finite state automaton graph, with each node as a possible state (possible logical formula). This finite state automaton graph has certain 'operator transitions', that transform a single formula or many formulas into another formula(s) - moving from a certain graph state into another graph state. A knowledge of our operators can describe certain 'shortcuts' in this state graph, which can make searching for the acceptable end state more effective. A single 'operator' can define zero, one or many 'shortcuts' as well.


Learning.

AI Learning Field is described in an article: Artificial Intelligence Learning Aspects, can be used to construct a Weak AI, that 'behaves rationally' to fulfill it's task.

Systems that learn can adapt to situation, can be autonomous in action.

There are connections & common points between AI Learning & other AI Research Fields, mostly with 'automatic inference' & 'heuristic search'.


See also, if You wish or need, ... : Finite State Automatons and Regular Expression Basics.


Source: [52].

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