Transcript Slide 1
ARTIFICIAL INTELLIGENCE
[INTELLIGENT AGENTS PARADIGM]
INTRODUCTION
Professor Janis Grundspenkis Riga Technical University Faculty of Computer Science and Information Technology Institute of Applied Computer Systems Department of Systems Theory and Design E-mail: [email protected]
Why Would You Study Artificial Intelligence? (1)
• Artificial intelligence is quickly emerging from the laboratory and is venturing into the
commercial marketplace
. Its impact on society is growing rapidly: in speech and language technology, strategic planning and diagnosis, process and system control, vision and authentication systems, information retrieval and data-mining and many other contexts. The many
new realizations
continually redefine which applications we can achieve and push existing technology to its limits
Why Would You Study Artificial Intelligence? (2)
• Reasoning with knowledge is a central issue.
The mere fact that
knowledge is power
the importance of AI indisputable makes • Due to the
rapidly expanding role of AI
in our current and future society, there is an urgent need for
academically trained people
with the variety of backgrounds who are familiar with the fundamentals of AI, aware of its reasonable expectations, and have practical experience in solving AI problems
Text Books
• Russell S., Norvig P.
Artificial Intelligence.
A Modern Approach
, Pearson Education, 2010 • Wooldridge M.
An Introduction to MultiAgent Systems
, John Wiley and Sons, 2009 • Hadzic M., et al.
Ontology-Based Multi Agent Systems
, Springer-Verlag, 2009
What Is Artificial Intelligence? (1)
WHAT IS INTELLIGENCE?
• It is only a word that people use to name those
unknown processes
with which our brains solve problems we call hard
(Minsky)
• Working definitions of what intelligence is must necessarily change through the years. We deal with a
moving target
which makes it difficult to explain just what it is we do
What Is Artificial Intelligence? (2)
• • In principle, we should be able to build intelligent machines someday because
our brains themselves are machines
!
• One problem is that we know very little about
how the brain actually works
• Even though we do not understand how the brain performs many mental skills, we can still work toward making machines that do
the same or similar things Artificial Intelligence
is simple the name we give to that kind of research
Different Approaches to AI (1)
•
SYSTEMS THAT ACT LIKE HUMANS
– The act of
creating machines perform functions
that that require intelligence when performed by people
(Kurzweil, 1990)
– The study of how to make
computers do things
at which, at the moment, people are better
(Rich and Knight, 1991)
Different Approaches to AI (2)
•
SYSTEMS THAT THINK LIKE HUMANS
– The existing new effort to make computer think …
machines with minds
, in the full and literal sense
(Haugeland, 1985)
– The automation of activities that we associate with
human thinking
, activities such as decision-making, problem solving, learning …
(Bellman, 1978)
Different Approaches to AI (3)
•
SYSTEMS THAT THINK RATIONALLY
– The study of
mental faculties
through the use of computational models
(Charniak and McDermont, 1985)
– The study of the
computations
that make it possible to perceive, reason and act
(Winston, 1992)
Different Approaches to AI (4)
•
SYSTEMS THAT ACT RATIONALLY
–
Computational intelligence
is the study of the design of
intelligent agents
(Poole et al., 1998)
– AI … is concerned with
intelligent behavior
in artifacts
(Nilsson, 1998)
Acting Humanly (1)
•
THE TURING TEST APPROACH
– The
Turing test
, proposed by Alan Turing (1950), was designed to provide a satisfactory operational definition of intelligence – The computer would need to possess the
following capabilities
: • Natural language processing • Knowledge representation • Automated reasoning • Machine learning
Acting Humanly (2)
•
THE TOTAL TURING TEST
–The computer additionally would need the
following capabilities
:
•
Computer vision
•
Robotics
Thinking Humanly
• •
THE COGNITIVE MODELING APPROACH
– We need to get inside the actual working of human minds • Through
introspection
thoughts as they go by - trying to catch our own • Through
psychological experiments
sufficiently precise theory of the mind to have a
COGNITIVE SCIENCE
brings together computer models from AI and experimental techniques from psychology
Thinking Rationally
• •
THE "LAWS OF THOUGHT" APPROACH
– Aristotle
syllogisms
provided patterns for argument structures that always yielded correct conclusions when given correct premises – Logicians in the 19th century developed a
precise notation
for statements about all kinds of things in the world and about the relations among them
TWO MAIN OBSTACLES TO THIS APPROACH
– It is not easy to take
informal knowledge
and state it in the formal terms required by logical notation – There is a big
difference
between being able to solve a problem "in principle" and doing so in practice
Acting Rationally
•
THE RATIONAL AGENT APPROACH
– The
agent
is just something that acts (agents comes from the Latin
agere
, “to do”) – A
rational agent
is one that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outcome • ALL THE SKILLS NEEDED FOR THE TURING TEST ARE THERE TO ALLOW RATIONAL ACTIONS • THE STUDY OF AI AS RATIONAL AGENT DESIGN IS MORE GENERAL APPROACH
Two Complementary Views of AI
• One as an
engineering discipline
concerned with the creation of intelligent machines • One as an
empirical science
concerned with the computational modeling of human intelligence • Former characterizes
modern AI
, while the later characterizes
modern cognitive science
Specialties Which Originated in AI
• Robotics • Pattern Recognition • Expert Systems • Automatic Theorem Proving • Cognitive Psychology • Word Processing • Machine Vision • Knowledge Engineering • Computational Linguistics • Symbolic Applied Mathematics • Intelligent Agent Paradigm • Programming Paradigms
Paradigm Shift (1)
• The science of artificial intelligence from its inception through to the present day is based on – the reliance on
logic
knowledge –
logical inference
as a way of representing (logical reasoning) as the primary mechanism for intelligent reasoning • This way of looking at knowledge, language, and thought reflects the
rationalist tradition
of western philosophy • It also reflects the underlying assumptions of
Turing test
, practically its emphasis on symbolic reasoning, as a test of intelligence, and the belief that a straightforward comparison with human behavior was adequate to confirming machine intelligence
Paradigm Shift (2)
• The later half of the twentieth century has seen numerous challenges to rationalist philosophy – various forms of mathematics)
philosophical relativism
question the objective basis of language, science, society, and thought (Wittgenstein’s, Husserl’s, Heidegger’s philosophy; Godel’s and Turing’s views on the very foundations of – post-modern thought has changed our understanding of meaning and value in the arts and society
Paradigm Shift (3)
• New (alternative) models of intelligence –
neural models
of intelligence emphasize the brain’s ability to adapt to the world in which it is situated by modifying the relationships between individual neurons – work in
artificial life
and
genetic algorithms
applies the principles of biological evolution to the problems of finding solutions to difficult problems –
social systems
provide another metaphor for intelligence in that they exhibit global behavior that enable them to solve problems that would confound any of their individual members
Paradigm Shift (4)
TWO THEMES
• First theme is that the
view of intelligence
is rooted in culture and society, and, as a consequence,
emergent
• Second theme is that intelligence is reflected by the
collective behaviors
of large number of very simple interacting semi-autonomous individuals, or
agents
Paradigm Shift (5)
THE MAIN THEMES SUPPORTING AN AGENT-ORIENTED AND EMERGENT VIEW OF INTELLIGENCE
• Agents are
autonomous
• Agents are
situated
or semi-autonomous in their
environments
• Agents are
interactional
(they may be seen as a society) • The society of agents is
structured
(individual agents are coordinated with other agents in the overall problem solving) • The phenomenon of intelligence in the environment is
emergent
(overall
cooperative result
of the society of agents can be viewed as
greater
contributors) than the sum of its individual