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LDP/howto/linuxdoc/AI-Alife-HOWTO.sgml
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@ -60,8 +60,10 @@ GNU/Linux specifically in mind.
<ref id="Evocosm">, <ref id="Critterding">, <ref id="MRPT">,
<ref id="PyBrain">, <ref id="peach">, <ref id="brain">, <ref id="FREVO">,
<ref id="Vowpal Wabbit">, <ref id="ERESYE">, <ref id="Recast">,
<ref id="EAP">, <ref id="GenePool">, <ref id="Milk">, <ref id="OpenCog">
and <ref id="brain-simulator">.
<ref id="EAP">, <ref id="GenePool">, <ref id="Milk">, <ref id="OpenCog">,
<ref id="Pattern">, <ref id="CognitiveFoundry">, <ref id="clasp">,
<ref id="timbl">, <ref id="MBT">, <ref id="scikits.learn">,
<ref id="Biogenesis"> and <ref id="brain-simulator">.
<p>Changed the name of the "Traditional" section to
<ref id="Symbolic Systems (GOFAI)">. Added new section,
@ -252,14 +254,15 @@ LICENSE
<sect>Symbolic Systems (GOFAI)
<label id="Symbolic Systems (GOFAI)">
<p>
Traditionally AI was based around the ideas of logic, rule
systems, linguistics, and the concept of rationality. At its
roots are programming languages such as Lisp and Prolog.
Expert systems are the largest successful example of this
paradigm. An expert system consists of a detailed knowledge
base and a complex rule system to utilize it. Such systems
have been used for such things as medical diagnosis support
and credit checking systems.
Traditionally AI was based around the ideas of logic, rule systems,
linguistics, and the concept of rationality. At its roots are programming
languages such as Lisp and Prolog though newer systems tend to use more
popular procedural languages. Expert systems are the largest successful
example of this paradigm. An expert system consists of a detailed
knowledge base and a complex rule system to utilize it. Such systems have
been used for such things as medical diagnosis support and credit checking
systems.
<sect1>AI class/code libraries
@ -400,6 +403,27 @@ LICENSE
of chess applications. The main purpose of the CIL project is
to get AI researchers interested in using Lisp to work in the
chess domain.
<label id="clasp">
<tag/clasp/
<itemize>
<item>Web site: <htmlurl
url="http://www.cs.uni-potsdam.de/clasp/">
</itemize>
clasp is an answer set solver for (extended) normal logic programs. It
combines the high-level modeling capacities of answer set programming
(ASP) with state-of-the-art techniques from the area of Boolean
constraint solving. The primary clasp algorithm relies on
conflict-driven nogood learning, a technique that proved very
successful for satisfiability checking (SAT). Unlike other learning ASP
solvers, clasp does not rely on legacy software, such as a SAT solver
or any other existing ASP solver. Rather, clasp has been genuinely
developed for answer set solving based on conflict-driven nogood
learning. clasp can be applied as an ASP solver (on LPARSE output
format), as a SAT solver (on simplified DIMACS/CNF format), or as a PB
solver (on OPB format).
<label id="ConceptNet">
@ -689,11 +713,9 @@ LICENSE
<tag/OpenCyc/
<itemize>
<item>Web site: <htmlurl
url="http://www.opencyc.org/"
name="www.opencyc.org">
url="http://www.opencyc.org/">
<item>Alt Web site: <htmlurl
url="http://sourceforge.net/projects/opencyc/"
name="sourceforge.net/projects/opencyc/">
url="http://sourceforge.net/projects/opencyc/">
</itemize>
OpenCyc is the open source version of Cyc, the largest and most
@ -701,6 +723,21 @@ LICENSE
ontology based on 6000 concepts and 60000 assertions about them.
<label id="Pattern">
<tag/Pattern/
<itemize>
<item>Web site: <htmlurl
url="http://www.clips.ua.ac.be/pages/pattern">
</itemize>
Pattern is a web mining module for the Python programming language. It
bundles tools for data retrieval (Google + Twitter + Wikipedia API, web
spider, HTML DOM parser), text analysis (rule-based shallow parser,
WordNet interface, syntactical + semantical n-gram search algorithm,
tf-idf + cosine similarity + LSA metrics) and data visualization (graph
networks).
<label id="PowerLoom">
<tag/PowerLoom/
<itemize>
@ -1318,30 +1355,6 @@ LICENSE
extending the knowledge base of NICOLE.
<label id="NLTK">
<tag/NLTK/
<itemize>
<item>Web site: <htmlurl
url="http://www.nltk.org/"
name="www.nltk.org">
</itemize>
NLTK, the Natural Language Toolkit, is a suite of Python libraries and
programs for symbolic and statistical natural language processing.
NLTK includes graphical demonstrations and sample data. It is
accompanied by extensive documentation, including tutorials that
explain the underlying concepts behind the language processing tasks
supported by the toolkit.
NLTK is ideally suited to students who are learning NLP (natural
language processing) or conducting research in NLP or closely related
areas, including empirical linguistics, cognitive science, artificial
intelligence, information retrieval, and machine learning. NLTK has
been used successfully as a teaching tool, as an individual study tool,
and as a platform for prototyping and building research systems.
<label id="Otter">
<tag/Otter: An Automated Deduction System/
<itemize>
@ -3427,6 +3440,19 @@ LICENSE
to theoretical or evolutionary biology and dynamic systems.
<label id="Biogenesis">
<tag/Biogenesis/
<itemize>
<item>Web site: <htmlurl
url="http://biogenesis.sourceforge.net/">
</itemize>
Biogenesis is an artificial life program that simulates the processes
involved in the evolution of organisms. It shows colored segment based
organisms that mutate and evolve in a 2D environment. Biogenesis is
based on Primordial Life.
<label id="breve">
<tag/breve/
<itemize>
@ -5391,6 +5417,20 @@ name="www.csee.umbc.edu/tkqml/">
<descrip>
<label id="CognitiveFoundry">
<tag/CognitiveFoundry/
<itemize>
<item>Web site: <htmlurl
url="http://foundry.sandia.gov/">
</itemize>
The Cognitive Foundry is a modular Java software library for the
research and development of cognitive systems. It contains many
reusable components for machine learning, statistics, and cognitive
modeling. It is primarily designed to be easy to plug into applications
to provide adaptive behaviors.
<label id="CompLearn">
<tag/CompLearn/
<itemize>
@ -5467,6 +5507,29 @@ name="www.csee.umbc.edu/tkqml/">
learning, milk supports k-means clustering and affinity propagation.
<label id="NLTK">
<tag/NLTK/
<itemize>
<item>Web site: <htmlurl
url="http://www.nltk.org/"
name="www.nltk.org">
</itemize>
NLTK, the Natural Language Toolkit, is a suite of Python libraries and
programs for symbolic and statistical natural language processing.
NLTK includes graphical demonstrations and sample data. It is
accompanied by extensive documentation, including tutorials that
explain the underlying concepts behind the language processing tasks
supported by the toolkit.
NLTK is ideally suited to students who are learning NLP (natural
language processing) or conducting research in NLP or closely related
areas, including empirical linguistics, cognitive science, artificial
intelligence, information retrieval, and machine learning. NLTK has
been used successfully as a teaching tool, as an individual study tool,
and as a platform for prototyping and building research systems.
<label id="peach">
<tag/peach/
<itemize>
@ -5530,6 +5593,22 @@ name="www.csee.umbc.edu/tkqml/">
makes PyBrain a powerful tool for real-life tasks.
<label id="MBT">
<tag/MBT/
<itemize>
<item>Web site: <htmlurl
url="http://ilk.uvt.nl/mbt/">
</itemize>
MBT is a memory-based tagger-generator and tagger in one. The
tagger-generator part can generate a sequence tagger on the basis of a
training set of tagged sequences; the tagger part can tag new
sequences. MBT can, for instance, be used to generate part-of-speech
taggers or chunkers for natural language processing. It has also been
used for named-entity recognition, information extraction in
domain-specific texts, and disfluency chunking in transcribed speech.
<label id="MLAP book samples">
<tag/MLAP book samples/
<itemize>
@ -5541,6 +5620,53 @@ name="www.csee.umbc.edu/tkqml/">
algorithms from the book "Machine Learning: An Algorithmic Perspective"
by Stephen Marsland. All code is written in python.
<label id="scikits.learn">
<tag/scikits.learn/
<itemize>
<item>Web site: <htmlurl
url="http://scikit-learn.sourceforge.net/">
</itemize>
scikits.learn is a Python module integrating classic machine learning
algorithms in the tightly-knit world of scientific Python packages
(numpy, scipy, matplotlib). It aims to provide simple and efficient
solutions to learning problems that are accessible to everybody and
reusable in various contexts: machine-learning as a versatile tool for
science and engineering.
<label id="timbl">
<tag/timbl/
<itemize>
<item>Web site: <htmlurl
url="http://ilk.uvt.nl/timbl/">
</itemize>
The Tilburg Memory Based Learner, TiMBL, is a tool for NLP research,
and for many other domains where classification tasks are learned from
examples. It is an efficient implementation of k-nearest neighbor
classifier.
TiMBL's features are:
<itemize>
<item>Fast, decision-tree-based implementation of k-nearest neighbor
lassification;
<item>Implementations of IB1 and IB2, IGTree, TRIBL, and TRIBL2
algorithms;
<item>Similarity metrics: Overlap, MVDM, Jeffrey Divergence, Dot
product, Cosine;
<item>Feature weighting metrics: information gain, gain ratio,
chi squared, shared variance;
<item>Distance weighting metrics: inverse, inverse linear,
exponential decay;
<item>Extensive verbosity options to inspect nearest neighbor sets;
<item>Server functionality and extensive API;
<item>Fast leave-one-out testing and internal cross-validation;
<item>and Handles user-defined example weighting.
</itemize>
</descrip>
<sect1>Applications