ARTIFICIAL INTELLIGENCE

ARTIFICIAL INTELLIGENCE

AI “Languages”

• Artificial Intelligence (AI) has several languages available that can be used to create the backbone or core to the “brain.”
• These programs are used to “simulate intelligent processes such as learning, reasoning and understanding symbolic information in context.” 
• The early days of computer language design primarily used computers to perform various calculations utilizing only numbers. 
• Languages like FORTRAN (FORmula TRANslation) were designed specifically for working with numbers and computations. 
• After some time it was found that symbols could be used to relate and manipulate symbols.
•  Eventually, the idea of symbolic computation was used to help define algorithms that could process any type of information and therefore, possibly be used to simulate human intelligence. 
• As the use of symbolic computation grew and algorithms became increasing more complex, it was found that new languages needed to be developed because it had grown beyond just number computations (Neumann, 2002).

Robotics

• Robotics is one field within artificial intelligence.
• It involves mechanical, usually computer-controlled, devices to perform tasks that require extreme precision or tedious or hazardous work by people. 
• Traditional Robotics uses Artificial Intelligence planning techniques to program robot behaviors and works toward robots as technical devices that have to be developed and controlled by a human engineer. 
• The Autonomous Robotics approach suggests that robots could develop and control themselves autonomously. 
• These robots are able to adapt to both uncertain and incomplete information in constantly changing environments.
•  This is possible by imitating the learning process of a single natural organism or through Evolutionary Robotics, which is to apply selective reproduction on populations of robots.
•  It lets a simulated evolution process develop adaptive robots. 

Branches of AI

Logical Ai

• What a program knows about the world in general the facts of the specific situation in which it must act, and its goals are all represented by sentences of some mathematical logical language.
• The program decides what to do by inferring that certain actions are appropriate for achieving its goals.

Search

• AI programs often examine large numbers of possibilities, e.g. moves in a chess game or inferences by a theorem proving program.
• Discoveries are continually made about how to do this more efficiently in various domains.

Pattern Recognition

• When a program makes observations of some kind, it is often programmed to compare what it sees with a pattern.
•  For example, a vision program may try to match a pattern of eyes and a nose in a scene in order to find a face.
•  More complex patterns, e.g. in a natural language text, in a chess position, or in the history of some event are also studied.
• These more complex patterns require quite different methods than do the simple patterns that have been studied the most.

Representation

• Facts about the world have to be represented in some way. Usually languages of mathematical logic are used.

Inference

• From some facts, others can be inferred.
• Mathematical logical deduction is adequate for some purposes, but new methods of non-monotonic inference have been added to logic since the 1970s.
•  The simplest kind of non-monotonic reasoning is default reasoning in which a conclusion is to be inferred by default, but the conclusion can be withdrawn if there is evidence to the contrary.
• Ordinary logical reasoning is monotonic in that the set of conclusions that can the drawn from a set of premises is a monotonic increasing function of the premises. 
• Circumscription is another form of non-monotonic reasoning.

Common Sense Knowledge And Reasoning

• This is the area in which AI is farthest from human-level, in spite of the fact that it has been an active research area since the 1950s.
• While there has been considerable progress, e.g. in developing systems of non-monotonic reasoning and theories of action, yet more new ideas are needed.
•  The Cyc system contains a large but spotty collection of common sense facts.

Learning From Experience

• Programs do that.
• The approaches to AI based on connectionism and neural nets specialize in that. 
• There is also learning of laws expressed in logic. [Mit97] is a comprehensive undergraduate text on machine learning.
• Programs can only learn what facts or behaviors their formalisms can represent, and unfortunately learning systems are almost all based on very limited abilities to represent information.

Planning

• Planning programs start with general facts about the world (especially facts about the effects of actions), facts about the particular situation and a statement of a goal.
• From these, they generate a strategy for achieving the goal.
• In the most common cases, the strategy is just a sequence of actions.

Epistemology

• This is a study of the kinds of knowledge that are required for solving problems in the world.

Ontology

• Ontology is the study of the kinds of things that exist.
• In AI, the programs and sentences deal with various kinds of objects, and we study what these kinds are and what their basic properties are. Emphasis on ontology begins in the 1990s.

Heuristics

• A heuristic is a way of trying to discover something or an idea imbedded in a program. The term is used variously in AI. 
• Heuristic functions are used in some approaches to search to measure how far a node in a search tree seems to be from a goal. 
• Heuristic predicates that compare two nodes in a search tree to see if one is better than the other, i.e. constitutes an advance toward the goal, may be more useful.

Genetic Programming

Genetic programming is a technique for getting programs to solve a task by mating random Lisp programs and selecting fittest in millions of generations. 

Applications of AI

Game Playing

• You can buy machines that can play master level chess for a few hundred dollars. 
• There is some AI in them, but they play well against people mainly through brute force computation--looking at hundreds of thousands of positions.
• To beat a world champion by brute force and known reliable heuristics requires being able to look at 200 million positions per second.

Speech Recognition

• In the 1990s, computer speech recognition reached a practical level for limited purposes. 
• Thus United Airlines has replaced its keyboard tree for flight information by a system using speech recognition of flight numbers and city names.
•  It is quite convenient. On the the other hand, while it is possible to instruct some computers using speech, most users have gone back to the keyboard and the mouse as still more convenient.

Understanding Natural Language

• Just getting a sequence of words into a computer is not enough. Parsing sentences is not enough either. 
• The computer has to be provided with an understanding of the domain the text is about, and this is presently possible only for very limited domains.

Computer Vision

• The world is composed of three-dimensional objects, but the inputs to the human eye and computers' TV cameras are two dimensional. 
• Some useful programs can work solely in two dimensions, but full computer vision requires partial three-dimensional information that is not just a set of two-dimensional views. 
• At present there are only limited ways of representing three-dimensional information directly, and they are not as good as what humans evidently use.

Expert Systems

• A ``knowledge engineer'' interviews experts in a certain domain and tries to embody their knowledge in a computer program for carrying out some task. 
• How well this works depends on whether the intellectual mechanisms required for the task are within the present state of AI. 
• When this turned out not to be so, there were many disappointing results. 
• One of the first expert systems was MYCIN in 1974, which diagnosed bacterial infections of the blood and suggested treatments.
•  It did better than medical students or practicing doctors, provided its limitations were observed. 
• Namely, its ontology included bacteria, symptoms, and treatments and did not include patients, doctors, hospitals, death, recovery, and events occurring in time. Its interactions depended on a single patient being considered. 
• Since the experts consulted by the knowledge engineers knew about patients, doctors, death, recovery, etc., it is clear that the knowledge engineers forced what the experts told them into a predetermined framework. In the present state of AI, this has to be true. 
• The usefulness of current expert systems depends on their users having common sense.

Heuristic Classification

• One of the most feasible kinds of expert system given the present knowledge of AI is to put some information in one of a fixed set of categories using several sources of information.