Joseph Wilk

We can think of JavaScript running within a clients browser as a robotic agent. It has an environment in which it can sense things. The ability to look at the environment and make decisions based on plans.

So whys that useful, well why is a robot useful? You can produce many different complex plans and give them to the robot and forget about it while it does the work potentially over and over again. If we are really lucky the robot can demonstrate some intelligence and deal with uncertainty.

Well I tried out a small part of this idea to build a server side service which delivered plans in JavaScript to the client. The JavaScript planning agent followed the plans. Its not a intelligent robot but this is just a prototype. The plans where focused on validation conditions that a user needed to get through to post a form.

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Latent Semantic Analysis (LSA) is a mathematical method that tries to bring out latent relationships within a collection of documents. Rather than looking at each document isolated from the others it looks at all the documents as a whole and the terms within them to identify relationships.

An example of LSA:
Using a search engine search for “sand“.

Documents are returned which do not contain the search term “sand” but contains terms like “beach”.

LSA has identified a latent relationship, “sand” is semantically close to “beach”.

There are some very good papers which describing LSA in detail:

• An introduction to LSA: http://lsa.colorado.edu/papers/dp1.LSAintro.pdf
• Creating your own LSA space: http://www.andrew.cmu.edu/user/jquesada/pdf/bookSpacesRev1.pdf
• Latent Semantic analysis: http://en.wikipedia.org/wiki/Latent_semantic_indexing

This is an implementation of LSA in Python (2.4+). Thanks to scipy its rather simple!

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A vector space search involves converting documents into vectors. Each dimension within the vectors represents a term. If a document contains that term then the value within the vector is greater than zero.

Here is an implementation of Vector space searching using python (2.4+). (more…)

The event calculus planner used within my thesis was based on Dr. Murray Shanahan’s ASLDICN (Abductive SLD with Integrity constraints and proof by Negation) planner with compound action support. This planner is an adaptation from one published in one of Dr. Shanahan’s research papers

http://casbah.ee.ic.ac.uk/%7Empsha/planners.html

The original planner only supports the generation of a single plan. I needed to support conditional planning. I wanted the planner to generate multiple plans representing the different ways of reaching the goal. The problem was how to convert the planner to generate all possible plans. Importantly ensuring that this does not cause infinite looping and no redundant plan solutions are generated.

My version of the planner add the following features:

• Conditional Planning
• Impossible Predicate
• Occured And NotOccured predicates

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Prolog can be run as CGI by using a PHP wrapper script which invokes the Prolog engine from within PHP. Prolog can be invoked indicating Prolog files to load and goals to initially achieve once loaded.

Executing the following in PHP can spawn a process which runs Prolog.

$cgiOutput = `sicstus --goal $goal. -l "$cgiPrologScriptToLoad"`;

This specific example is for Sicstus but most Prolog command lines have a similar format. Another possiblity is to setup Prolog as CGI, since any langauge can be CGI. I was running my code on a windows box and found it impossible for Prolog to direct the content to the command line and capture it for returning. If you’re going the unix route you may want to look at PiLLoWs guide.

For form postings you can catch the post in PHP or a scripting language and create a prolog formated file which is passed to the prolog script when invoked.

You may want to have Prolog maintain state. This can be achieved through using a database. The database that I have used is Berkeley DB which SICStus has built in support for.

Download PDF Thesis http://www.doc.ic.ac.uk/teaching/projects/Distinguished04/JosephWilk.pdf

This project took the HTML form systems as a model and built a Workflow Management System that uses artificial intelligence planning methodologies and Event Calculus workflow specifications to try to overcome some of the problems of Workflow Management Systems. Logic, server side languages and planning all rolled into one.

The development of the Workflow Management System with AI uncovered interesting issues in modelling situations in the Event Calculus and the
problems that need to be overcome to use AI with workflow. The problems and
solutions developed in the project cover a wide spectrum of domains, looking at logic
programming, server-side languages and getting the two to talk to each other. Areas
covered include such interesting topics as typing of HTML to new frameworks for
Prolog running as CGI.

Achievements

• Workflow specification language
  Using the Event Calculus and extensions to specify workflow.
• [HTML form typing | HTML typing in Prolog]
  A typing engine for ensuring that the HTML form element specifications are
  correct when used in workflow specifications.
• A Visualisation tool for Event Calculus plans
  A tool that generates Scalable Vector Graphic graphs for Event Calculus
  plans.
• A HTML/PHP iWFMS engine
  Using the plans generated from the workflow specifications to support the
  running and management of a system.
• A JavaScript plan execution engine
  Facilitates the following of workflow plans in a scripting language that runs
  while the user is viewing and interacting with a web page.
• Logic programming running as Common Gateway Interface (CGI)
  A framework for the use of high-level declarative programming languages
  functioning as CGI.
• [Logic programming and server-side language interaction model | Interaction support between PHP and Prolog]
  An Interaction model allowing server-side languages used for generating web
  pages to interact with logic programming languages.
• A Hospital model working example
  An example of how the specification can be utilised for a real world scenario in
  a hospital. Providing the full functionality within the iWFMS to run and manage
  this system.

Problem

The exipry of content within caching architectures is only identified when a user makes a request for expired data. Hence a % of the visitors to the site will not be able to take advantage of caching.Many different caching architectures are used within a typical dynamic site. Hence the solution needs to be cache agnostic.

Architecture

Emmao bot was the name given to the python program which is used to keep the cache hot. 

Figure 1: Emmaobot Server UML Model

Solution

Emmao bot has been built to act as a user agent and request pages. mod_python is used to make the apache children log their requests in a special format. Emmao bot is running in the background as a daemon process. It can be run from the webhead or an alternative server. It examines the special apache log files and adds events for when these expiry. Lib event is used to manage these events. Pages have different rankings based on analysis as emmao runs. It uses this to ensure that the most important/popular/heavy pages never expiry. Also if there is a limit on the number of pages to focus on, rank can be used to decided which pages to ignore.

The Cost

Although the number of pages that emmao bit manages can be set to limit load on the webserver, there is still an increase in traffic due to Emmao Bot.
In live production environments with Emmao bot managing 10,000 pages I have not found the peformance outway the benfit of reducing maximum user fetch time.

Links

LibEvent http://monkey.org/~provos/libevent

ModPython http://www.modpython.org/

Task

Use Squid to manage a cache for a website where there are member users (logged in to site) and public users. Squid must cache both member views of a page and public views.

Squid needs to check the authentication of the user and decided whether it should redirect them to a cache for members or for public users. There are only two discrete sets of users and any content that is specific to users if handled via AJAX.

• Squid will be operating as a transparent proxy.
• Usernames/Passwords are stored within a MSSQL database.
• Squid is hosted on a unix box along with Apache

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This project looked at dynamically generating suggestion tags for content. To simplify the task some constraints where introduced.

• The content which will be tagged is news articles with HTML markup.
• Only English content.

I used the following HTML page to experiment on with suggestion tags: http://news.bbc.co.uk/1/hi/entertainment/6624223.stm

To help evaluate the tagging methods I asked a sample of people to suggest what they thought the best tags would be. They came up with:

paris, hilton, paris hilton, jail, jail sentence, drink-driving

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