[Chapter 1: Introduction]
Artificial Intelligence (CSC 355)
Arjun Singh Saud
Central Department of Computer Science & Information Technology
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Tribhuvan University
What is Artificial Intelligence?
Artificial Intelligence is a branch of Science which deals with helping machines to find
solutions to complex problems in a more human-like fashion. This generally involves
borrowing characteristics from human intelligence, and applying them as algorithms in a
computer friendly way. A more or less flexible or efficient approach can be taken
depending on the requirements established, which influences how artificial the intelligent
behavior appears.
AI is generally associated with Computer Science, but it has many important links with
other fields such as Maths, Psychology, Cognition, Biology and Philosophy, among many
others. Our ability to combine knowledge from all these fields will ultimately benefit our
progress in the quest of creating an intelligent artificial being.
Different definitions of AI are given by different books/writers. These definitions can be
divided into two dimensions.
Systems that think like humans Systems that think rationally
Systems that act like humans
Systems that act rationally
Top dimension is concerned with thought processes and reasoning, where as bottom
dimension addresses the behavior.
The definition on the left measures the success in terms of fidelity of human
performance, whereas definitions on the right measure an ideal concept of intelligence,
which is called rationality.
Human-centered approaches must be an empirical science, involving hypothesis and
experimental confirmation. A rationalist approach involves a combination of
mathematics and engineering.
Acting Humanly: The Turing Test Approach
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The Turing test, proposed by Alan Turing (1950) was designed to convince the people
that whether a particular machine can think or not. He suggested a test based on
indistinguishability from undeniably intelligent entities- human beings.
The computer passes the test if a human interrogator after posing some written
questions, can not tell whether the written response come from human or not.
To pass a Turing test, a computer must have following capabilities:
Natural Language Processing: Must be able to communicate in English
successfully
Knowledge representation: To store what it knows and hears.
Automated reasoning: Answer the Questions based on the stored information.
Machine learning: Must be able to adapt in new circumstances.
Turing test avoid the physical interaction with human interrogator. Physical simulation of
human beings is not necessary for testing the intelligence.
The total Turing test includes video signals and manipulation capability so that the
interrogator can test the subjects perceptual abilities and object manipulation ability. To
pass the total Turing test computer must have following additional capabilities:
Computer Vision: To perceive objects
Robotics: To manipulate objects and move
Thinking Humanly: Cognitive modeling approach
Make the machines with mind. Cognition means the action or process of acquiring
knowledge and understanding through thought, experience and senses. To make a
machine that think like human brain, scientific theories of internal brain activities
(cognitive model) are required. Two ways of doing this is:
Predicting and testing human behavior (cognitive science)
Identification from neurological data (Cognitive neuroscience)
Once we have precise theory of mind, it is possible to express the theory as a computer
program. But unfortunately until up to now there is no precise theory about thinking
process of human brain. Therefore it is not possible to make the machines that think like
human brain
Thinking rationally: The laws of thought approach
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Aristotle was one of the first who attempt to codify the right thinking that is irrefutable
reasoning process. He gave Syllogisms that always yielded correct conclusion when
correct premises are given.
For example:
Ram is a man
Man is mortal
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Ram is mortal
Let
p(x) x is man
q(x) x is mortal
then above statement can be written as
p(x) => q(x)
Man is mortal
p(Ram)
Ram is man
Then from modus ponens q(x) ia also true. That is Ram is mortal
This study initiated the field of logic. The logicist tradition in AI hopes to create
intelligent systems using logic programming.
Problems:
It is not easy to take informal knowledge and state in the formal terms required by logical
notation, particularly when knowledge is not 100% certain.
Solving problem principally is different from doing it in practice. Even problems with
certain dozens of fact may exhaust the computational resources of any computer unless it
has some guidance as which reasoning step to try first.
Acting Rationally: The rational Agent approach:
Agent is something that acts. Computer agent is expected to have following attributes:
Autonomous control
Perceiving their environment
Persisting over a prolonged period of time
Adapting to change
And capable of taking on anothers goal
Rational behavior means doing the right thing. The right thing is that which is expected to
maximize goal achievement, given the available information. Rational Agent is one that
acts so as to achieve the best outcome or, when there is uncertainty, the best expected
outcome. In this approach the emphasis is given to correct inferences.
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Advantages:
It is more general than laws of thought approach, because correct inference is just
one of several mechanisms for achieving rationality.
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One way to act rationally is to reason logically to the conclusion and act on that
conclusion. On the other hand there are also some ways of acting rationally that can not
be said to involve inference. For Example, recoiling from a host stove is a reflex action
that is usually more successful than a slower action taken after careful deliberation.
It is more amenable to scientific development than are approaches based on
human behavior or human thought because the standard of rationality is clearly
defined and completely general.
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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 threedimensional 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,
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Applications 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. An example is advising whether to accept a proposed
credit card purchase. Information is available about the owner of the credit card,
his record of payment and also about the item he is buying and about the
establishment from which he is buying it (e.g., about whether there have been
previous credit card frauds at this establishment).
Foundations of AI
Different fields have contributed to AI in the form of ideas, viewpoints and techniques.
Philosophy:
Logic, reasoning, mind as a physical system, foundations of learning, language and
rationality.
Mathematics:
Formal representation and proof algorithms, computation, undecidability, intractability,
probability.
Psychology:
adaptation, phenomena of perception and motor control.
Economics:
formal theory of rational decisions, game theory.
Linguistics:
Knowledge representation, grammar
Neuroscience:
Physical substrate for mental activities
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Control theory:
Homeostatic systems, stability, optimal agent design
Brief history of AI
What happened after WWII?
1943: Warren Mc Culloch and Walter Pitts: a model of artificial boolean neurons to
perform computations.
First steps toward connectionist computation and learning (Hebbian learning).
Marvin Minsky and Dann Edmonds (1951) constructed the first neural network
computer
1950: Alan Turings Computing Machinery and Intelligence
First complete vision of AI.
The birth of AI (1956)
- Dartmouth Workshop bringing together top minds on automata theory, neural nets and
the study of intelligence.
Allen Newell and Herbert Simon: The logic theorist (first nonnumeric thinking
program used for theorem proving)
For the next 20 years the field was dominated by these participants.
Great expectations (1952-1969)
Newell and Simon introduced the General Problem Solver.
Imitation of human problem-solving
Arthur Samuel (1952-)investigated game playing (checkers ) with great success.
John McCarthy(1958-) :
Inventor of Lisp (second-oldest high-level language)
Logic oriented, Advice Taker (separation between knowledge and reasoning)
Marvin Minsky (1958 -)
Introduction of microworlds that appear to require intelligence to solve: e.g.
blocks-world.
Anti-logic orientation, society of the mind.
Collapse in AI research (1966 - 1973)
Progress was slower than expected.
Unrealistic predictions.
Some systems lacked scalability.
Combinatorial explosion in search.
Fundamental limitations on techniques and representations.
Minsky and Papert (1969) Perceptrons.
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AI revival through knowledge-based systems (1969-1970)
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General-purpose vs. domain specific
- E.g. the DENDRAL project (Buchanan et al. 1969)
First successful knowledge intensive system.
Expert systems
- MYCIN to diagnose blood infections (Feigenbaum et al.)
- Introduction of uncertainty in reasoning.
Increase in knowledge representation research.
- Logic, frames, semantic nets,
AI becomes an industry (1980 - present)
R1 at DEC (McDermott, 1982)
Fifth generation project in Japan (1981)
American response
Puts an end to the AI winter.
Connectionist revival (1986 - present): (Return of Neural Network)
Parallel distributed processing (RumelHart and McClelland, 1986); backprop.
AI becomes a science (1987 - present)
In speech recognition: hidden markov models
In neural networks
In uncertain reasoning and expert systems: Bayesian network formalism
The emergence of intelligent agents (1995 - present)
The whole agent problem: How does an agent act/behave embedded in real
environments with continuous sensory inputs
Knowledge and its importance
Knowledge is information that helps us to solve problems in particular domain, to
predict what will happen next and to explain why and how something has happened?
Knowledge contains facts, procedures and judgmental rules. Knowledge is important
in AI for making intelligent machines. Key issues confronting the designer of AI
system are:
Knowledge acquisition: Gathering the knowledge from the problem domain to solve
the AI problem.
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Knowledge manipulation: Large volume of knowledge has no meaning until up to it
is processed to deduce the hidden aspects of it. Knowledge is manipulated to draw
conclusions from knowledgebase.
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Knowledge representation: Expressing the identified knowledge into some
knowledge representation language such as propositional logic, predicate logic etc.
