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beadkennelAI and Robotics

Oct 15, 2013 (3 years and 10 months ago)

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Neural
Network


(

http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html)
:

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way
biological nervous systems, such as the brain, process
information. The key element of this paradigm is
the novel structure of the information processing system. It is composed of a large number of highly
interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like
peo
ple, learn by example. An ANN is configured for a specific application, such as pattern recognition or
data classification, through a learning process. Learning in biological systems involves adjustments to the
synaptic connections that exist between the n
eurones. This is true of ANNs as well.



Pattern
Recognition

(http://en.wikipedia.org/wiki/Pattern_recognition):

In
machine learning
,
pattern recognition

is the assignment of some sort of output value (or
label
) to a
given input value (or
instance
), according to some specific algorithm. An example of pattern recognition
is
classification
, which attempts to assign each input value to one of a given set of
classes

(for example,
determine whether a given email is "spam" or "non
-
spam"). However, pattern recognition is a more
general problem
that encompasses other types of output as well. Other examples are
regression
, which
assigns a real
-
valued output to each input;
sequence labeling
, which assigns a class to each member of a
sequence of values (for example,
part of speech ta
gging
, which assigns a
part of speech

to each word in
an input sentence); and
parsing
, which assigns a
parse tree

to an input sentence, describing the
syntactic
structure

of the sentence.

Pattern recognit
ion algorithms generally aim to provide a reasonable answer for all possible inputs and to
do "fuzzy" matching of inputs. This is opposed to
pattern matching

algorithms, wh
ich look for exact
matches in the input with pre
-
existing patterns. A common example of a pattern
-
matching algorithm is
regular expression

matching, which looks for pat
terns of a given sort in textual data and is included in the
search capabilities of many
text editors

and
word processors
. In contrast to pattern recognition, pattern
matching is generally not considered a type of machine learning, although pattern
-
matching algorithms
(especially with fairly general, carefully tailored patterns) can sometimes succeed in pr
oviding similar
-
quality output to the sort provided by pattern
-
recognition algorithms.


State Space
Search

(http://en.wikipedia.org/wiki/State_space_search)
:

State space search

is a process used in the field of
artificial intelligence

(AI) in which successive
configurations or
states

of an instance are considered, with the goal of finding a
goal state

with a desired
pr
operty.

In AI, problems are often modelled as a
state space
, a
set

of
states

that a problem can be in. The set of
states forms a
graph

where two states are connected if there is an
operation

that can be performed to
transform the first st
ate into the second.

State space search as used in AI differs from traditional
computer science

search

methods because the
state space is
implicit
: the typical state space graph is much too large to generate and store in
memory
.
Instead, nodes are generated as

they are explored, and typically discarded thereafter. A solution to a
combinatorial search

instance may consist of the goal state itself, or of a path from some
i
nitial state

to
the goal state.

The structure of state space search corresponds to the structure of problem being solved in two different
ways. They are,



By making use of some legal operations,it enables to define a problem in order to convert the
given si
tuation into desired solution.



It enables to define the procedure of solving a problem. Usually this procedure is a combination
of known techniques and search.


Declarative
vs.

Procedural Knowledge
Representation:

(http://ai.eecs.umich.edu/cogarch2/prop/declarative
-
procedural.html)
:

Architectures with
declarative

representations
have knowledge in a format that may be manipulated
decomposed and analyzed by its reasoners
. A classic example of a declarative representat
ion is
logic
.
Advantages of declarative knowledge are:


Architectures with
procedural

representations encode
how to achieve a particular result
. Advantages of
procedural knowledge are:


Often times, whether knowledge is viewed as
declarative

or
procedural

is not an intrinsc property of the
knowledge base, but is a function of what is allowed to read from it. Production s
ystems, for example, are
declarative if productions may view themselves, and are procedural it they can.



Production System
:

(http://en.wikipedia.org/wiki/Production_system)
:

A
production system

(or
production rule system
) is a computer program typically used to provide some
form of
artificial intelligence
, which consists primarily of a set of rules about behavior. These rules
,
termed
productions
, are a basic
representation

found useful in
automated planning
,
expert systems

and
action selection
. A produc
tion system provides the mechanism necessary to execute productions in order
to achieve some goal for the system.

Productions consist of two parts: a sensory precondition (or "IF" statement) and an action (or "THEN"). If
a production's precondition matches

the current
state

of the world, then the production is said to be
triggered
. If a production's action is
executed
, it is said to have
fired
. A production system also contains a
database, sometimes called
working memory
, which mai
ntains data about current state or knowledge, and
a rule interpreter. The rule interpreter must provide a mechanism for prioritizing productions when more
than one is triggered.



Frame (Artificial Intelligence)
:

(

http
://en.wikipedia.org/wiki/Frame_%28artificial_intelligence%29)
:

Frames

were proposed by
Marvin Minsky

in his 1974 article "A Framework for Representing
Knowledge." A frame is an
artificial intelligence

data structure

used to divide
knowledge

into
substructures by representing "
stereotyped

situations." Frames are connected together to form a complete
idea
.


Frame Structure:

The frame contains information on how to use the frame, what to expect next, and what to do when these
expectations are not met. Some information in the frame is generally unchanged while
other information,
stored in "terminals,"
[
clarification needed
]

usually change. Different frames may share the same terminals.

A frame's terminals are alrea
dy filled with default values, which is based on how the human mind works.
For example, when a person is told "a boy kicks a ball," most people will be able to visualize a particular
ball (such as a familiar
soccer ball
) rather than imagining some abstract ball with no attributes.



S
cripts
:

(http://en.wikipedia.org/wiki/Scripts_%28artificial_intelligence%29)
:

Scripts

were developed in the early AI work by
Roger Schank
,
Robert P. Abelson

and their research
group, and are a method of representing
procedural knowledge
. They are very much like
frames
, except
the values that
fill the slots must be ordered.

The classic example of a script involves the typical sequence of events that occur when a person dines in
a restaurant:
finding a seat, reading the menu, ordering drinks from the waitstaff...

In the script form,
these would be decomposed into
conceptual transitions
, such as
MTRANS

and
PTRANS
, which refer to
mental transitions [of informa
tion]

and
physical transitions [of things]
.



LISP
:

(http://en.wikipedia.org/wiki/Lisp_%28programming_language%29)
:

Lisp

or
LISP

is a family of
computer

programming languages

with a long history and a distinctive, fully
parenthesized syntax. Originally specified in 1958, Lisp is the second
-
oldest
high
-
level programming
language

in widespread use today; only
Fortran

is older (by one year). Like Fortran, Lisp has changed a
gre
at deal since its early days, and a number of
dialects

have existed over its history. Today, the most
widely known general
-
purpose Lisp dialects are
Common Lisp

and
Scheme
.

Lisp was originally created as a practical mathematical notation for computer programs, influenced by the
notation of
Alonzo Ch
urch
's
lambda calculus
. It quickly became the favored programming language for
artificial intelligence

(AI) research. As one of the earliest programming languages, Lisp pioneered many
ideas in
computer science
, including
tree data structures
,
automatic storage management
,
dynamic typing
,
and the
self
-
hosting

compil
er
.

The name
LISP

derives from "LISt Processing".
Linked lists

are one of Lisp languages' major
data
s
tructures
, and Lisp
source code

is itself made up of lists. As a result, Lisp programs can manipulate
source code as a data structure, giving rise to the
macro

systems that allow programmers to create new
syntax or even new
domain
-
spe
cific languages

embedded in Lisp.


The interchangeability of code and data also gives Lisp its instantly recognizable syntax. All program
code is written as
s
-
expressions
, or parenthesized lists. A function call or syntactic form is written as a list
with the function or operator's name first, and the arguments following; for instance, a function f that
takes three arguments might be called using
(f arg1 arg2 arg3)
.