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Summary of Introduction to AI: A general approach

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  • Introduction Of Artificial Intelligence
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  • Tilburg University (UVT)
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  • Artificial Intelligence

This summary contains all the relevant chapters from the book: Artificial Intelligence: A General Approach. The relevant chapters are the relevant Chapters according to the 6ECTS course Introduction to AI at Tilburg University as a part of the Cognitive Science & Artificial Intelligence, Liberal ...

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  • 1, 3 t/m 5, 13, 14
  • November 25, 2021
  • 34
  • 2021/2022
  • Summary

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  • artificial intelligence
  • search methods
  • deterministic problem solving

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More summaries for

  • SOLUTIONS & INSTRUCTOR MANUAL Artificial Intelligence: A Modern Approach, 4th Edition (2020) by Peter Norvig and Stuart Russell, Chapters 1 – 28
  • Solution Manual for Artificial Intelligence: A Modern Approach 4th Edition, (Stuart Russell, 2020), All Chapters
  • Solution Manual for Artificial Intelligence: A Modern Approach 4th Edition by Stuart Russell, Peter Norvig, All Chapters.
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  • Tilburg University (UVT)
  • Cognitive Science And Artificial Intelligence
  • Introduction Of Artificial Intelligence
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Summary Artificial Intelligence: A Modern Approach
Chapter 1
Artificial intelligence attempts not only to understand how we think, but also to build
intelligent entities.

Rationalities are ideal performances; a system is rational if it does the ‘right thing’ given
what it knows. Human-centered approach is part of an empirical science, involving
observations and hypotheses about the human behaviour. A rationalist approach involves a
combination of mathematics and engineering. Various groups are helping each other.
The Turing Test is a test in which a computer passes when a human interrogator, after
posing some written questions, cannot tell whether the written responses come from a
person or from a computer. The computer needs the following capabilities for this:
- Natural language processing: to enable to communicate in English
- Knowledge representation: to store what it knows or hears
- Automated reasoning: to use the stored information to answer questions and draw
conclusions
- Machine learning: to adapt to new circumstances

Total Turing Test includes a video signal so the interrogator can test the subject’s perceptual
abilities, as well as the opportunity for the interrogator to pass physical objects “through the
hatch”. To pass this test the computer needs:
- Computer vision: to perceive objects
- Robotics: to manipulate object and move about
Those 6 disciplines compose most of AI.

To do this, we need to get inside the human mind. There are three ways to do this:
- Introspection (trying to catch our own thoughts)
- Psychological experiments (observing a person in action)
- Brain imaging (observing the brain in action)
Cognitive science brings together computer models from AI and experimental techniques
from psychology to construct precise and testable theories of the human mind. It is based
on experimental investigation of actual humans or animals.

Aristotle’s syllogisms provided patterns for argument structures that always yielded correct
conclusions when given correct premises – their study initiated the field called logic. Logicist
tradition within AI hopes to build on programs to create intelligent systems.
However, there are two main obstacles:
- 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 solving a problem ‘in principle’ and solving it in
practice

An agent is something that acts, but computer agents are expected to do more: operate
autonomously, perceives their environment, persist over a prolonged time period, adapts to
change and pursues goals. A rational agent is one that acts so as to achieve the best
(expected) outcome.

,In the ‘laws of thought’ approach to AI, the emphasis was on correct inferences. Correct
inferences are part of being a rational agent, but not all of rationality.
The rational agent approach has two advantages:
- It is more general than the ‘laws of thought’
- It is more amenable to scientific development
The standard of rationality is mathematically well defined and completely general.
Achieving perfect rationality is not feasible in complicated environments; the demands are
too high. Limited rationality is acting appropriately when there is not enough time to do all
the computations one might like.

The mind operates according to logical rules. To build physical systems that emulate some
of those rules, the mind itself is such a physical system.
One problem with a purely physical conception of the mind is that it seems to leave little
room for free will.

Rationalism is the power of reasoning in understanding the world. Dualism is that there is a
part of the human mind that is outside of nature, exempt from physical laws. Materialism
holds that the brain’s operation according to the laws of physics constitutes the mind. Free
will is simple the way that the perception of available choices appears to the choosing
entity.
Empiricism movement is characterised by a dictum of John Locke: “Nothing is in the
understanding, which was not first in the senses.” Now there is an induction: general rules
are acquired by exposure to repeated associations between their elements.
Logical positivism combines rationalism and empiricism. Confirmation theory attempted to
analyse the acquisition of knowledge from experience.
There is a connection between knowledge and action, intelligence requires action as well as
reasoning. By understanding how actions are justified can we understand how to build an
agent whose actions are justifiable. Actions are justified by a logical connection between
goals and knowledge of the action’s outcome.

Goal-based analysis is useful but does not say what to do when several actions will achieve
the goal or when no action will achieve it completely.

In AI, the leap to a formal science requires a level of mathematical formalisation in three
fundamental areas: logic, computational, probability.
Formal logic is derived from ancient Greece and works out details of propositions.
The limits what could be done with logic and computation: algorithms could be used for
logical deduction.
The incompleteness theorem showed that in any formal theory as strong as Peano
arithmetic, there are true statements that are undecidable in the sense that they have no
proof within the theory. This fundamental result can also be interpreted as showing that
some functions on the integers cannot be represented by an algorithm. This motivated
Turing to try to characterize exactly which functions are computable. The notion of a
computation or effective procedure really cannot be given a formal definition.
Decidability and computability are important to an understanding of computation, the
notion of tractability has an even greater impact. A problem is called intractable if the time

,required to solve instances of the problem grows exponentially with the size of the
instances.

The distinction between polynomial and exponential growth:
Exponential growth means that even moderately large instances cannot be solved in any
reasonable time. One should strive to divide the overall problem of generating intelligent
behaviour intro tractable subproblems rather than intractable ones.
NP-completeness showed the existence of large classes of canonical combinatorial search
and reasoning problems that are NP-complete.

Smith was the first to treat economics as science, using the idea that economics can be
thought of as consisting of individual agents maximizing their own economic well-being.
Economists say people make choices that lead to preferred outcomes (utility).
Decision theory combines probability theory with utility theory and provides a formal and
complete framework for decisions made under uncertainty.
In game theory a rational agent should adopt policies that are randomized. It does not offer
an unambiguous prescription for selecting actions.
How to make rational decisions when payoffs from actions are not immediate but result
from several actions taken in sequence?

Neuroscience is the study of the nervous system.
Mapping can change over the course of a few weeks. Measurement of brain activity began
in 1929. Functional magnetic resonance imaging is giving neuroscientists unprecedentedly
detailed images of brain activity, enabling measurements that correspond to ongoing
cognitive processes. Individual neurons can be stimulated electrically, chemically or
optically.
A collection of simple cells can lead to thought, action and consciousness / brains cause
minds.
Mysticism = minds operate in some mystical realm that is beyond physical science.

Wundt insisted on carefully controlled experiments in which his workers would perform a
perceptual or associative task while introspecting on their thought processes. The subjective
nature of the data made it unlikely that an experimenter would ever disconfirm his or her
own theories. This led to the behaviourism movement: it rejected any theory involving
mental processes on the grounds that introspection could not provide reliable evidence.
Only studying the stimulus and response.

Cognitive psychology views the brain as an information-processing device. Perception
involved a form of unconscious logical inference.
Three steps of a knowledge-based agent:
- The stimulus must be translated into an internal representation
- The representation is manipulated by cognitive processes to derive new internal
representations
- These are retranslated back into action
The development of computer modelling led to the creation of the field of cognitive science.

, A cognitive theory should be like a computer program; it should describe a detailed
information-processing mechanism whereby some cognitive function might be
implemented.

For AI to succeed we need: intelligence and an artifact. The computer is the artifact of
choice.
Each generation of computer hardware has brought an increase in speed and capacity and a
decrease in price. Performance doubled every 18 months. Future increases in power will
come from massive parallelism- a curious convergence with the properties of the brain.
AI also awed debt to the software side of computer science, which has supplied the
operating systems, programming languages and tools needed to write modern programs.
Work in AI has pioneered many ideas that have made their way back to mainstream
computer science.

The first self-controlling machine was a water clock with a regulator that maintained a
constant flow rate. This changed what an artifact could do. Before this only living things
could modify their behaviour in response to changes in the environment.
The central figure of the creation of control theory was Norbert Wiener:
- the possibility of artificially intelligent machines
- intelligence could be created by the use of homeostatic devices containing
appropriate feedback loops to achieve stable adaptive behaviour

Modern control theory (stochastic optimal control) has as its goal the design of systems that
maximize an objective function over time.
AI and control theories are two different fields because:
- control theory lends themselves to systems that are describable by fixed sets of
continuous variables
- AI was founded in part as a way to escape from these perceived limitations
Behaviourist theory did not address the notion of creativity in language.
Modern linguistics and AI were born at about the same time, intersecting a hybrid field
called computational linguistics or natural language processing. Understanding language
requires an understanding of the subjects matter and context, not just an understanding of
the structure of sentences. Knowledge representation was ties to language and informed by
research in linguistics, which was connected to decades of work on the philosophical
analysis of language.

The first work of AI drew on 3 sources:
- Knowledge of the basic physiology and functions of neurons in the brain
- A formal analysis of propositional logic
- Turing’s theory of computation
A model of artificial neurons in which each neuron is characterized as being ‘on’ or ‘off’, with
a switch to ‘on’ occurring in response to stimulation by a sufficient number of neighbouring
neurons. The state of a neuron was conceived of as ‘factually equivalent to a proposition
which proposed its adequate stimulus.

AI from the start embraced the idea of duplicating human faculties such as creativity, self-
improvement and language use. Methodology is important to AI because AI is a branch of

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