GNU/Linux AI & Alife HOWTO, John Eikenberry [best books to read for self development TXT] 📗

a reasoning engine based on the Rete

pattern matching algorithm. LISA also provides the ability to

reason over ordinary CLOS objects.

 

NICOLE

 

� Web site: nicole.sourceforge.net

 

NICOLE (Nearly Intelligent Computer Operated Language Examiner)

is a theory or experiment that if a computer is given enough

combinations of how words, phrases and sentences are related to

one another, it could talk back to you. It is an attempt to

simulate a conversation by learning how words are related to

other words. A human communicates with NICOLE via the keyboard

and NICOLE responds back with its own sentences which are

automatically generated, based on what NICOLE has stored in it’s

database. Each new sentence that has been typed in, and NICOLE

doesn’t know about, is included into NICOLE’s database, thus

extending the knowledge base of NICOLE.

 

NLTK

 

� Web site: nltk.sourceforge.net

 

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.

 

Otter: An Automated Deduction System

 

� Web site: www-unix.mcs.anl.gov/AR/otter/

 

Our current automated deduction system Otter is designed to

prove theorems stated in first-order logic with equality.

Otter’s inference rules are based on resolution and

paramodulation, and it includes facilities for term rewriting,

term orderings, Knuth-Bendix completion, weighting, and

strategies for directing and restricting searches for proofs.

Otter can also be used as a symbolic calculator and has an

embedded equational programming system.

 

PVS

 

� Web site: pvs.csl.sri.com/

 

PVS is a verification system: that is, a specification language

integrated with support tools and a theorem prover. It is

intended to capture the state-of-the-art in mechanized formal

methods and to be sufficiently rugged that it can be used for

significant applications. PVS is a research prototype: it

evolves and improves as we develop or apply new capabilities,

and as the stress of real use exposes new requirements.

 

SNePS

 

� Web site: www.cse.buffalo.edu/sneps/

 

The long-term goal of The SNePS Research Group is the design and

construction of a natural-language-using computerized cognitive

agent, and carrying out the research in artificial intelligence,

computational linguistics, and cognitive science necessary for

that endeavor. The three-part focus of the group is on knowledge

representation, reasoning, and natural-language understanding

and generation. The group is widely known for its development of

the SNePS knowledge representation/reasoning system, and Cassie,

its computerized cognitive agent.

 

Soar

 

� Web site: sitemaker.umich.edu/soar

 

Soar has been developed to be a general cognitive architecture.

We intend ultimately to enable the Soar architecture to:

 

� work on the full range of tasks expected of an intelligent

agent, from highly routine to extremely difficult, open-ended

problems

 

� represent and use appropriate forms of knowledge, such as

procedural, declarative, episodic, and possibly iconic

 

� employ the full range of problem solving methods

 

� interact with the outside world and

 

� learn about all aspects of the tasks and its performance on

them.

 

In other words, our intention is for Soar to support all the

capabilities required of a general intelligent agent.

 

TCM

 

� Web site: wwwhome.cs.utwente.nl/~tcm/

 

� FTP site: ftp.cs.utwente.nl/pub/tcm/

 

TCM (Toolkit for Conceptual Modeling) is our suite of graphical

editors. TCM contains graphical editors for Entity-Relationship

diagrams, Class-Relationship diagrams, Data and Event Flow

diagrams, State Transition diagrams, Jackson Process Structure

diagrams and System Network diagrams, Function Refinement trees

and various table editors, such as a Function-Entity table

editor and a Function Decomposition table editor. TCM is easy

to use and performs numerous consistency checks, some of them

immediately, some of them upon request.

 

Yale

 

� Web site: yale.sf.net/

 

� Alt Web site: rapid-i.com/

 

YALE (Yet Another Learning Environment) is an environment for

machine learning experiments. Experiments can be made up of a

large number of arbitrarily nestable operators and their setup

is described by XML files which can easily created with a

graphical user interface. Applications of YALE cover both

research and real-world learning tasks.

 

WEKA

 

� Web site: lucy.cs.waikato.ac.nz/~ml/

 

WEKA (Waikato Environment for Knowledge Analysis) is an state-of-the-art facility for applying machine learning techniques to

practical problems. It is a comprehensive software “workbench”

that allows people to analyse real-world data. It integrates

different machine learning tools within a common framework and a

uniform user interface. It is designed to support a “simplicity-first” methodology, which allows users to experiment

interactively with simple machine learning tools before looking

for more complex solutions.

3. Connectionism

Connectionism is a technical term for a group of related techniques.

These techniques include areas such as Artificial Neural Networks,

Semantic Networks and a few other similar ideas. My present focus is

on neural networks (though I am looking for resources on the other

techniques). Neural networks are programs designed to simulate the

workings of the brain. They consist of a network of small

mathematical-based nodes, which work together to form patterns of

information. They have tremendous potential and currently seem to be

having a great deal of success with image processing and robot

control.

 

3.1. Connectionist class/code libraries

 

These are libraries of code or classes for use in programming within

the Connectionist field. They are not meant as stand alone

applications, but rather as tools for building your own applications.

 

Software for Flexible Bayesian Modeling

 

� Web site: www.cs.utoronto.ca/~radford/fbm.software.html

 

This software implements flexible Bayesian models for regression

and classification applications that are based on multilayer

perceptron neural networks or on Gaussian processes. The

implementation uses Markov chain Monte Carlo methods. Software

modules that support Markov chain sampling are included in the

distribution, and may be useful in other applications.

 

BELIEF

 

� Web site: www.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/reasonng/probabl/belief/

 

BELIEF is a Common Lisp implementation of the Dempster and Kong

fusion and propagation algorithm for Graphical Belief Function

Models and the Lauritzen and Spiegelhalter algorithm for

Graphical Probabilistic Models. It includes code for

manipulating graphical belief models such as Bayes Nets and

Relevance Diagrams (a subset of Influence Diagrams) using both

belief functions and probabilities as basic representations of

uncertainty. It uses the Shenoy and Shafer version of the

algorithm, so one of its unique features is that it supports

both probability distributions and belief functions. It also

has limited support for second order models (probability

distributions on parameters).

 

bpnn.py

 

� Web site: http://arctrix.com/nas/python/bpnn.py

 

A simple back-propogation ANN in Python.

 

CNNs

 

� Web site: www.isi.ee.ethz.ch/~haenggi/CNNsim.html

 

� Newer Version:

www.isi.ee.ethz.ch/~haenggi/CNNsim_adv_manual.html

 

� Old Page: www.ce.unipr.it/research/pardis/CNN/cnn.html

 

Cellular Neural Networks (CNN) is a massive parallel computing

paradigm defined in discrete N-dimensional spaces.

 

CONICAL

 

� Web site: strout.net/conical/

 

CONICAL is a C++ class library for building simulations common

in computational neuroscience. Currently its focus is on

compartmental modeling, with capabilities similar to GENESIS and

NEURON. A model neuron is built out of compartments, usually

with a cylindrical shape. When small enough, these open-ended

cylinders can approximate nearly any geometry. Future classes

may support reaction-diffusion kinetics and more. A key feature

of CONICAL is its cross-platform compatibility; it has been

fully co-developed and tested under Unix, DOS, and Mac OS.

 

Jet’s Neural Architecture

 

� Web site: www.voltar-confed.org/jneural/

 

Jet’s Neural Architecture is a C++ framework for doing neural

net projects. The goals of this project were to make a fast,

flexible neural architecture that isn’t stuck to one kind of net

and to make sure that end users could easily write useful

applications. All the documentation is also easily readable.

 

Joone

 

� Web site: www.jooneworld.com

 

Joone is a neural net framework to create, train and test neural

nets. The aim is to create a distributed environment based on

JavaSpaces both for enthusiastic and professional users, based

on the newest Java technologies. Joone is composed of a central

engine that is the fulcrum of all applications that already

exist or will be developed. The neural engine is modular,

scalable, multitasking and tensile. Everyone can write new

modules to implement new algorithms or new architectures

starting from the simple components distributed with the core

engine. The main idea is to create the basis to promote a

zillion of AI applications that revolve around the core

framework.

 

Matrix Class

 

� FTP site: ftp.cs.ucla.edu/pub/

 

A simple, fast, efficient C++ Matrix class designed for

scientists and engineers. The Matrix class is well suited for

applications with complex math algorithms. As an demonstration

of the Matrix class, it was used to implement the backward error

propagation algorithm for a multilayer feedforward artificial

neural network.

 

Neural Networks at your Fingertips

 

� Web site: www.neural-networks-at-your-fingertips.com

 

A set of ANSI C packages that illustrate Adaline networks, backpropagation, the Hopfield model, BAM, Boltzman, CPN, SOM, and

ART1. Coded in portable, self-contained ANSI C. With complete

example applications from a variety of well-known application

domains.

 

NEURObjects

 

� Web site: www.disi.unige.it/person/ValentiniG/NEURObjects/

 

NEURObjects is a set of C++ library classes for neural

networks development. The main goal of the library consists in

supporting researchers and practitioners in developing new

neural network methods and applications, exploiting the

potentialities of object-oriented design and programming.

NEURObjects provides also general purpose applications for

classification problems and can be used for fast prototyping of

inductive machine learning applications.

 

Pulcinella

 

� Web site: iridia.ulb.ac.be/pulcinella/

 

Pulcinella is written in CommonLisp, and appears as a library of

Lisp functions for creating, modifying and evaluating valuation

systems. Alternatively, the user can choose to interact with

Pulcinella via a graphical interface (only available in Allegro

CL). Pulcinella provides primitives to build and evaluate

uncertainty models according to several uncertainty calculi,

including probability theory, possibility theory, and Dempster-Shafer’s theory of belief functions; and the possibility theory

by Zadeh, Dubois and Prade’s. A User’s Manual is available on

request.

 

scnANNlib

 

� Web site: www.sentinelchicken.org/projects/scnANNlib/

 

SCN Artificial Neural Network Library provides a programmer with

a simple object-oriented API for constructing ANNs. Currently,

the library supports non-recursive networks with an arbitrary

number of layers, each with an arbitrary number of nodes.

Facilities exist for training with momentum, and there are plans

to gracefully extend the functionality of the library in later

releases.

 

UTCS Neural Nets Research Group Software

 

� Web site: nn.cs.utexas.edu/pages/software/software.html

 

A bit different from the other entries, this is a reference to a

collection of software rather than one application. It was all

developed by the UTCS Neural Net Research Group. Here’s

a summary of the packages available:

 

� Natural Language Processing

 

� MIR - Tcl/Tk-based rapid prototyping for sentence

processing

 

� SPEC - Parsing complex sentences

 

� DISCERN - Processing script-based stories, including

 

� PROC - Parsing, generation, question answering

 

� HFM - Episodic memory organization

 

� DISLEX - Lexical processing

 

� DISCERN - The full integrated model

 

� FGREPNET - Learning distributed representations

 

� Self-Organization

 

� LISSOM - Maps with self-organizing lateral connections.

 

� FM - Generic Self-Organizing Maps

 

� Neuroevolution

 

� Enforced SubPopulations (ESP) for sequential decision

tasks

 

� Non-Markov Double Pole Balancing

 

� Symbiotic, Adaptive NeuroEvolution (SANE; predecessor of

ESP)

 

� JavaSANE - Java software package for applying SANE to

new tasks

 

� SANE-C - C version, predecessor of JavaSANE

 

� Pole Balancing - Neuron-level SANE on the