Artificial Intelligence Resources (AIResources.info)is a Subject Tracer™ Information Blog developed and created by the Virtual Private Library™. It is designed to bring together the latest resources and sources on an ongoing basis from the Internet for artificial intelligence resources which are listed below. We always welcome suggestions of additional sites and resources to be added to this comprehensive listing and please submit by clicking here. This site has been developed and maintained by Marcus P. Zillman, M.S., A.M.H.A.; Internet expert, author, keynote speaker, and consultant. His latest white papers include Searching the Internet, Academic and Scholar Search Engines and Sources, and Knowledge Discovery Resources 2009. All of his Subject Tracer™ Information Blogs and his white papers are available from WhitePapers.us.

His latest monthly column is available by clicking here. Subscribe to his free monthly Awareness Watch™ Newsletter. Learn more by clicking here. Free online streaming tutorial videos on searching the Internet by Marcus P. Zillman, M.S., A.M.H.A.; available by clicking here.

ARTIFICIAL INTELLIGENCE RESOURCES

20Q.net – An Experiment in Artificial Intelligence
http://www.20q.net/

AboutAI.net
http://www.aboutai.net/

About Intelligence
http://www.aboutintelligence.co.uk/

Adaptive Artificial Intelligence
http://adaptiveai.com/

ADATE System – Automatic Design of Algorithms Through Evolution
http://www-ia.hiof.no/~rolando/index.html

AI Dreams – Forum for the Discussion of Artificial Intelligence and Linked Materials Such as Robotics, Character Design, Movies and New Technology
http://www.aidreams.co.uk/

AI Education Repository
http://www.cs.cofc.edu/~manaris/ai-education-repository/

AI FAQ Index
http://www.faqs.org/faqs/ai-faq/

Ai Forums
http://www.ai-forum.org/

AI Genealogy Project
http://aigp.csres.utexas.edu/~aigp/

AI Hub
http://www.aihub.org/

AI Lab University of Zurich
http://www.ifi.unizh.ch

AI on the Web
http://www.cs.berkeley.edu/~russell/ai.html

AI Repositories and Resource Lists
http://snipurl.com/7gk2

Ai Research
http://www.a-i.com/

AISB – The Society for the Study of Artificial Intelligence and the Simulation of Behaviour
http://www.aisb.org.uk/

AI: The Movie
http://www.aaai.org/AITopics/html/aimovie.html

AITIA Informatikai – Artificial Intelligence, Information Technology, Intelligent Agents
http://www.aitia.ai/

AI Toolkits and SDKs
http://www.gameai.com/toolkits.html

AI Topics
http://www.aaai.org/AITopics/aitopics.html

American Association for Artificial Intelligence (AAAI)
http://www.aaai.org/

Amzi!
http://www.amzi.com/

Applied Soft Computing
http://www.sciencedirect.com/science/journal/15684946

Appreciative Inquiry Commons
http://appreciativeinquiry.cwru.edu/

Artificial Intelligence – a CompInfo Directory
http://www.compinfo-center.com/tpai-t.htm

Artificial Intelligence: A Modern Approach
http://aima.cs.berkeley.edu/

Artificial Intelligence Areas
http://www.isis.vt.edu/~fanjun/text/ai1.html

Artificial Intelligence By Jonathan Crowhurst
http://www.freepint.com/issues/290704.htm#tips

Artificial Intelligence Depot
http://ai-depot.com/Main.html

Artificial Intelligence from the Bible
http://users.adelphia.net/~lilavois/Seven/bible.html

Artificial Intelligence Research
http://www.a-i.com/

Association for Uncertainty in Artificial Intelligence (AUAI)
http://www.auai.org/

AutoStitch – A New Dimension In Automatic Image Stitching
http://www.cs.ubc.ca/~mbrown/autostitch/autostitch.html

Babel – A Language for Talking To Computers
http://babelproject.com/

Bibliographies on Artificial Intelligence
http://liinwww.ira.uka.de/bibliography/Ai/

BLIASoft Knowledge Discovery
http://www.bliasoft.com/Eindex.html

Bot Research
http://www.BotResearch.info/

BrainMaker (California Scientific) Neural Network Software
http://www.calsci.com/index.html

BUBL LINK / Artificial Intelligence
http://bubl.ac.uk/link/a/artificialintelligence.htm

CGAL – Computational Geometry Algorithms Library
http://www.cgal.org/

ChatterBots
http://www.ChatterBots.info/

Cybernetics and Systems Analysis
http://www.springerlink.com/link.asp?id=106467

Dali
http://www.dalilab.com/index.html

Data Mining Resources
http://www.DataMiningResources.info/

digitalAGORA
http://aut.edu/agora/main.htm

Dr. Dobb’s Journal – AI
http://www.ddj.com/topics/ai/

Edinburgh University, Artificial Intelligence
http://www.dai.ed.ac.uk/

Electronic Transactions on Artificial Intelligence
http://www.etai.info/

Elzware – Information Virtually and Conversational Interfaces
http://www.elzware.com/

Encog – A Neural Network Framework for C# and Java
http://www.heatonresearch.com/encog

Evolutionary Intelligence
http://www.springer.com/engineering/journal/12065

Feng-GUI – Feng Shui For Graphic User Interfaces™
http://www.feng-gui.com/

Framsticks – Artificial Life – 3D Evolution and Simulation
http://www.frams.alife.pl/

Franz, Inc.
http://www.franz.com/

Free Online Directory of Computing (FOLDOC)
http://www.foldoc.org/

Generation5
http://www.generation5.org/

Genetic Algorithms and Artificial Life Resources
http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol2/tcw2/article2.html

Genetic Algorithms Archive
http://www.aic.nrl.navy.mil/galist/

Gnod – Experimental AI Self Adapting System
http://www.gnod.net/

Google™ Directory – Computers – Artificial Intelligence
http://directory.google.com/Top/Computers/Artificial_Intelligence/

GreatMindsWorking.com
http://greatmindsworking.com/

gwap.com – Training Computers by Playing Games
http://www.gwap.com/gwap/

Heuristics and Artificial Intelligence in Finance and Investment
http://www.geocities.com/francorbusetti/

Imagination-Engines
http://www.Imagination-Engines.com/

Institute for Human and Machine Cognition (IHMC)
http://www.ihmc.us/

Institute for Information Technology – Artificial Intelligence Technologies
http://iit-iti.nrc-cnrc.gc.ca/r-d/ai-ia_e.html

Interactive Artificial Intelligence Resources
http://www.mainline.brynmawr.edu/EIAIR/

Interesting AI Demos and Projects
http://www.cs.wisc.edu/~dyer/cs540/demos.html

International Association of Artificial Intelligence and Law (IAAIL)
http://www.iaail.org/

International Journal of Computational Intelligence (IJCI)
http://www.enformatika.org/journals/1304-2386/

International Journal of Computational Intelligence Research (IJCIR)
http://www.ijcir.info

International Journal of Interactive Multimedia and Artificial Intelligence
http://www.imai-software.com/openlab/index.php?q=journal

International Journal on Artificial Intelligence Tools (IJAIT)
http://ejournals.wspc.com.sg/ijait/ijait.shtml

Introduction to Neural Networks for C#
Online Book: http://www.heatonresearch.com/online/introduction-neural-networks-cs-edition-2
Online Course(YouTube): http://www.heatonresearch.com/course/intro-neural-nets-cs

Introduction to Neural Networks for Java
Online Book: http://www.heatonresearch.com/online/introduction-neural-networks-java-edition-2
Online Course(YouTube): http://www.heatonresearch.com/course/intro-neural-nets-java

Java Applets for Neural Network and Artificial Life
http://staff.aist.go.jp/utsugi-a/Lab/Links.html

Joone – Java Object Oriented Neural Engine
http://www.jooneworld.com/

Journal of Artificial General Intelligence (JAGI)
http://journal.agi-network.org/Home/tabid/72/Default.aspx

Journal of Artificial Intelligence Research
http://www.cs.washington.edu/research/jair/home.html

Journal of Artificial Societies and Social Simulation
http://jasss.soc.surrey.ac.uk/JASSS.html

Journal of Interesting Negative Results in Natural Language Processing and Machine Learning (JINR)
http://jinr.site.uottawa.ca/

Journal of Machine Learning Research
http://www.jmlr.org/

KurzweilAI.net
http://www.kurzweilai.net/

Logic Programming Associates – LPA
http://www.lpa.co.uk

Machine Translation
http://www.springerlink.com/link.asp?id=100310

MindPixel: Digital Mind Modeling Project
http://www.mindpixel.com/

Minds and Machines
http://www.springerlink.com/link.asp?id=100314

MIT AI Lab Research Projects
http://www.csail.mit.edu/research/activities/activities.html

NASA Tech Briefs Software Center – Artificial Intelligence and Expert Systems
http://www.techbriefs.com/software/ai.html

Natural Computing: An International Journal
http://www.springerlink.com/link.asp?id=108905

Neuron AI Directory: Artificial Intelligence Resources
http://www.neuron.co.uk/

Neuroph – Java Neural Network Frramework
http://neuroph.sourceforge.net/

OpenAI
http://sourceforge.net/projects/openai/

OpenCyc – Open Source General KnowledgeBase and Commonsense Reasoning Engine
http://www.cyc.com/cyc/opencyc

Open Directory – Computers: Artificial Intelligence
http://dmoz.org/Computers/Artificial_Intelligence/

Pascal Costanza’s Highly Opinionated Guide to Lisp
http://p-cos.net/lisp/guide.html

PCAI Artificial Intelligence Magazine
http://www.pcai.com/

PCAI Artificial Intelligence Yahoo Groups Mailing List
http://groups.yahoo.com/group/pcai/

Prediction Markets
http://www.PredictionMarkets.com/

Pyro – Python Robotics
http://pyrorobotics.org/

SIGART AI Web Resources Directory
http://www.acm.org/sigart/ai/

Sigma Knowledge Engineering Environment
http://sigmakee.sourceforge.net/

Singularity Institute for Artificial Intelligence
http://www.singinst.org/

SRI’s Artificial Intelligence Center
http://www.ai.sri.com/

Structure and Interpretation of Computer Programs – Video Lectures by Hal Abelson and Gerald Jay Sussman
http://www.swiss.ai.mit.edu/classes/6.001/abelson-sussman-lectures/

Suggested Upper Merged Ontology (SUMO)
http://www.ontologyportal.org/

Summarizer Ranks Sentences
http://www.trnmag.com/Stories/2005/042005/Summarizer_ranks_sentences_042005.html

Swarm Intelligence Resources
http://glmrc.ie.ncit.edu.tw/simon/SIHompage.htm

Tangent Research Intelligent Systems and Robotics R&D
http://tangentresearch.com/research/ai/

The A.I. Honor Society, National Chapter at GWU
http://www.gwu.edu/~aisoc/

The Artificial Intelligence Directory
http://www.ai-directory.com/

The Game AI Page: Building Artificial Intelligence into Games
http://www.gameai.com/

The Haley Enterprise
http://www.Haley.com/

The IDC Story: The First Successful AI Based Multi-Expert System In Arizona
http://snipurl.com/7ucv

Things That Think
http://ttt.media.mit.edu/index.html

Thinking Machine 4
http://turbulence.org/spotlight/thinking/index.html

Transactions of the Japanese Society for Artificial Intelligence (TJSAI)
http://www.jstage.jst.go.jp/browse/tjsai

University of Arizona Artificial Intelligence Lab
http://ai.bpa.arizona.edu/index.html

What Is Artificial Intelligence?
http://www-formal.stanford.edu/jmc/whatisai/whatisai.html

WPI AIRG — AI Webliography
http://www.cs.wpi.edu/Research/airg/AI-hotlist.html

WWW Virtual Library – Artificial Intelligence
http://vl.fmnet.info/ai/

Yahoo Directory – Artificial Intelligence
http://snipurl.com/7glb

Current Subject Tracer™ Information Blogs:

Accessibility Resources
http://www.AccessibilityResources.info/

Agriculture Resources
http://www.AgricultureResources.info/

Artificial Intelligence Resources
http://www.AIResources.info/

Astronomy Resources
http://www.AstronomyResources.info/

Auction Resources
http://www.AuctionResources.info/

Biological Informatics
http://www.biologicalinformatics.info/

Biotechnology Resources
http://www.BiotechnologyResources.info/

Bot Research
http://www.botresearch.info/

Business Intelligence Resources
http://www.biresources.info/

ChatterBots
http://www.ChatterBots.info/

Data Mining Resources
http://www.DataMiningResources.info/

Deep Web Research
http://www.deepwebresearch.info/

Directory Resources
http://www.DirectoryResources.info/

eCommerce Resources
http://www.eCommerceResources.info/

Elder Resources
http://www.ElderResources.info/

Employment Resources
http://www.EmploymentResources.info/

Entrepreneurial Resources
http://www.EntrepreneurialResources.info/

Financial Sources
http://www.FinancialSources.info/

Finding People
http://www.FindingPeople.info/

Games Resources
http://www.GamesResources.info/

Genealogy Resources
http://www.GenealogyResources.info/

Grant Resources
http://www.GrantResources.info/

Green Files
http://www.GreenFiles.info/

Grid, Distributed and Cloud Computing Resources
http://www.GridResources.info/

Healthcare Resources
http://www.healthcareresources.info/

Information Futures Markets
http://www.InformationFuturesMarkets.com/

Information Quality Resources
http://www.InformationQualityResources.info/

International Trade Resources
http://www.InternationalTradeResources.info/

Internet Alerts
http://www.InternetAlerts.info/

Internet Demographics
http://www.internetdemographics.info/

Internet Experts
http://www.internetexperts.info/

Internet Hoaxes
http://www.internethoaxes.info/

Journalism Resources
http://www.JournalismResources.info/

Knowledge Discovery
http://www.knowledgediscovery.info/

Military Resources
http://www.MilitaryResources.info/

New Economy Analytics, Resources and Alerts
http://www.NewEconomyAnalytics.com/

Outsourcing/Offshoring Information and Resources
http://www.OutsourcingOffshore.us/

Privacy Resources
http://www.PrivacyResources.info/

Reference Resources
http://www.ReferenceResources.info/

Research Resources
http://www.researchresources.info/

RestStress™
http://www.RestStress.com/

Script Resources
http://www.ScriptResources.info/

ShoppingBots
http://www.ShoppingBots.info/

Social Informatics
http://www.SocialInformatics.net/

Statistics Resources
http://www.statisticsresources.info/

Student Research
http://www.studentresearch.info/

Theology Resources
http://www.TheologyResources.info/

Tutorial Resources
http://www.TutorialResources.info/

World Wide Web Reference
http://www.WWWReference.info/

© 2009 Marcus P. Zillman, M.S., A.M.H.A.

For example, CareEngine can alert physicians to potential adverse drug interactions, overlooked lab tests or the availability of beneficial drugs, according to ActiveHealth.

Instead, these Creativity Machine are untrained artificial intelligence computer known as a neural network. The neurons in the computer brain are mathematical entities instead of brain cells. The creative process starts by tweaking the mathematical connections between the neurons. That set off a cascade of calculations that were translated as sounds.

jKilavuz is a path engine for Java. It consists of an extensible set of tools for collecting pathfind data and finding and executing paths. Despite the complexness of subject it is designed for ease of use.

"Like the human brain itself, these intelligent applications lend themselves to parallel processing. 80-cores would change the game in a broader sense than just hardware – it will enable new application possibilities."

The debate in language acquisition is around how much specific information about language is hard-wired into the brain of the infant, and how much is something that can be explained by relatively general purpose learning systems.

Here are some interesting posts made recently.

Machine Learning in Games

Game developers are increasingly keen to try ML techniques, but it does take some know-how and experience.

• The Secret to Building Game AI that Learns Realistically
• Alternatives to Online Learning for Actor Behaviors
• Game AI: Beauty and the Beast

Hierarchical Planning & Behavior Trees

Planners have proven themselves very effective in recent games, and hierarchical planners are the next logical step.

• Understanding Behavior Trees
• A Technical Overview of Game::AI++
• The Motivation Behind Game::AI++

Conference Coverage

AiGameDev.com also has summaries of recent events, very useful for catching up with what went on…

• AIIDE ‘07 Conference Coverage
• Pushing the Limits of Game AI Technology
• Apply AI 2007 Roundtable Report

Be sure to check out the site/blog if you’re interested in game AI!

Motivation

Assume your program has data samples and needs to draw certain conclusions about each of them. This is the case when mining for information in large databases, or trying to find an appropriate behaviour for a particular situation. You can apply decision trees to this problem as long as:

• The data samples each have attributes, either discrete or continuous values. For example, a symbol indicating if it was overcast or not, and the humidity level.
• The predictions must also be discrete or continuous values. For example, the estimated temperature or a prediction of whether it will rain or not.

Decision trees assume that it’s possible to check the attributes of the data samples multiple times, and use this information to refine the predicted value.

Description

A decision tree is based on a hierarchical data-structure, where each branch in the tree represents a condition. This condition typically evaluates to a boolean, with true/false each corresponding to a child node. However, it’s also possible to have more than two child nodes — for example if ranges are used as conditions. The leaves in the tree store the predictions for the target value(s).

A simple traversal algorithm is also necessary to traverse the tree, evaluating each decision and then selecting the appropriate sub-tree to enter. The algorithm is then applied recursively until a leaf node is reached. This process is rather efficient as very little memory is required to store a tree, and only simple conditional checks need to be evaluated while predicting.

Application

A decision tree can be used in almost any classification problem (to predict discrete symbols) or regression problem (to predict continuous values). They are particularly useful in data-mining because of their efficiency. However, such decision trees cannot solve all types of problems because of assumptions of the hierarchical model.

In practice, to apply the traversal algorithm successfully, a good decision tree is needed. This can be obtained in multiple ways:

1. The decision tree may be edited by an expert.
2. The decision tree can be induced from data samples.

Since expert solutions may take time to develop and tune, supervised learning is often used.

Resources

• See papers about the decision tree in the resources section on AI Depot.

• Minimax Search
• Decision Tree

You can subscribe to articles like these by signing up for the combined artificial intelligence feed. Be sure to let me know (using the form below) if you have a particular topic at heart so we can bump it up the list of things to write about.

On a related note, I’m exploring ways to liven up this kind of content and drag it kicking and screaming into the world of Web 2.0. I would appreciate your feedback on the subject:

 Loading …

Any comments are also welcome on the subject.

Several research groups have tried to combine the two principles. However, this has been shown to be difficult. Rune Moeller Jensen has, in co-operation with American colleagues from Carnegie Mellon University, developed a method called state-set-branching, in which the two principles can be combined. "We could show that these search algorithms were more efficient than previous algorithms", he says.

Now Hurwitz and Tshilidzi Marwala, also at Witwatersrand, have developed a virtual player that has taught itself to bluff at a card game called lerpa. Their artificial intelligence bot, named Aiden, is based on a neural network algorithm that usually forecasts stock market fluctuations.

Then the researchers decided to play Aiden against three other similarly trained bots to see what would happen. "They began to develop their own personalities – either aggressive or conservative – depending on their past successes,"

The contest has also generated several academic papers, Mr. Bennett said. It has turned out to be more exciting than officials expected.

""Artificial intelligence (AI) researchers claimed that the mind is a computational process. When they discovered the rules of the process, a computer that followed the same rules would be conscious and intelligent. This claim, often called "strong AI", is not falsifiable. In the past two decades, mainstream AI has focused on falsifiable claims, such as "a neural net can be trained to read handwritten addresses from postal envelopes." In the older terminology, this was called ‘weak AI’, but it has transformed AI into a strong scientific enterprise.""

• The current state is (at least partly) available to the program. For example, the program knows the position of the pieces on the game board.
• It’s possible to search through successor states in the future. The program knows how the pieces end up when a move is made.
• There’s a way to evaluate the utility of certain states. For instance, knowing when about the winning/loosing conditions.

Since this is a competitive setting, you can assume that the others pick actions resulting in a worse outcome for your program. Then, minimax works by taking the predictions of future states, and working back to determine the utility of predecessor states. When your program has estimated the utility of the next states, it can easily select the best.

Description

Minimax is a policy to select optimal utility actions assuming the worst from other programs. Typically, minmax is combined with a depth-first search algorithm that traverses the game tree, predicting what the opponents are likely to do and what your program should do. As such, there are two different levels in the search tree: 1) for the opponents and 2) for team-members.

1. The level in the game tree dealing with opponents is known as MIN, since they will typically minimize the utility of the outcome state.
2. Conversely, the search tree’s level dealing with team-members is known as MAX, since they will typically maximize the utility of the outcome state.

The minimax search, in effect, minimizes the maximum possible loss, or looking at it the other way, maximizes the minimum gain.

Application

Minimax is applied to many games with great success, especially when the branching factor is small (i.e. there are few options each turn), when the situation is deterministic, and the state is fully observable.

However, in practice, there are significant challenges to applying Minimax to a logic game. Notably, the game tree is usually too large to search until the end, so good estimator functions are required for any state (and not just terminal states). This is not a trivial problem, and significantly affects the outcome of the algorithm. Most state estimation functions are hand written for each problem by experts.

The pseudo-code for a minimax search looks like this:

def minimax(node, depth)
    if node.is_terminal() or depth == 0:
         return node.calculate_heuristic()
    if node.is_opponent():
         a = +inf
         for child in node.get_children():
             a = min(a, minimax(child, depth-1))
    else:
        a = -inf
        for child in node.get_children():
             a = max(a, minimax(child, depth-1))
    return a

On the bright side, alpha-beta pruning can be implemented easily to improve the efficiency of the search.

Theory

In classical statistical decision theory, an estimator is used to estimate a parameter . Also assume a risk function , usually specified as the integral of a loss function. Given this framework, is called “minimax” if it satisfies:

Resources

• See papers on minimax search in the resources section on AI Depot.

The Numenta Platform for Intelligent Computing (NuPIC) is free for academic and non-commercial research use, but there is so far no definitive information on commercial licensing costs. As a result of this, even though I like the ideas behind NuPIC, I spend relatively little time playing with the examples. I very much enjoyed Jeff Hawkin’s book On Intelligence (Amazon purchase link) and I will probably devote a lot more time to experimenting with NuPIC when all of the licensing issues are nailed down.

There are now references to other lined data sources – you will notice this as soon as you start experimenting with the browser based interface.

If you have not tried OpenCyc before, it is a free version of the Cyc product. OpenCyc provides an ontology of the “real world” and many facts and relations dealing with what might be called “common sense” knowledge.