Archive for the ‘bots’ Category
Spammers and artificial intelligence
Image spam was the beginning but not the end of spammers using artificial intelligence to get the spam out.
Since then use of distorted or obfuscated text, text images, video and audio files are being used by spammers. Bot nets are a larger threat still in the spammer’s tools boxes.
Newer tools in the fight against spam include: CAPTCHA { Completely automated public Turing test to tell computers and humans apart }; blocking email containing links to specific known problem sites; Cloudmark’s finger printing technology; NLP { Natural language processing}; white and black listing; and end user feedback.
Some say that the war between spammers and mail filters may well help drive artificial intelligence development in coming years.
More information:
Breaking Google Captchas for some Extra Cash
Turing Test
Natural Language Processing
Artificial intelligence scopes out spam
Spammers establishing use of artificial intelligence
AISK {Artificial intelligence spam killer} homepage ( code available through source forge )
Application of Biological Metaphors for Identifying and Killing Spam ( algorithm available )
Agents, Bots, and Spiders
‘Bot’ is short for ‘robot’ and refers to s software robot. A bot goes out into the Internet and pulls back data. Bots are used to handle repetitive tasks, to index the web for search engines, by games to be your avatar and by any program that sends out ‘robots’ to perform tasks. Java and Python are the preferred language for them.
A spider is a specialized bot. A spider starts on a given web-page and is restricted to a given domain or set of domains. The spider traverses the area by collecting links on the page and going from link to link.
Information gathering agents are all over the Internet now. They are the spiders that index web pages for you to find. There are bots that traverse databases and pullout information to serve up to users. Since computers do not understand language teaching bots to gather and sort information is quite a challenge. Several techniques are discussed here.
WebMate uses multiple TF-idF vectors each one in a different domain of interest to the user, as well as ‘Trigger Pair’ word searches in documents, and WebMate learns from watching the user. It runs between the browser and the HTTP server monitoring transactions. WebMate learns the classifications rather than have the user select and feed them to the program. The program learns incrementally and changes as the users interests change. The learning algorithm is run whenever the user flags a document as useful.
Information gathering agents may use the SIMS architecture. Each agent is a specialist in a different subject. These agents use KQML as the communication language between them, and LOOM as the knowledge representation language.
One agent is created, then others are instantiated to become experts in different areas of knowledge (flight schedules, hotel locations and rates, etc) and an area of domain (type of database, physical location, etc). A network of these agents is then put together in an acyclic graph.
More information:
Towards a Game Agent (pdf)
Alice
Alpha Works
BotSpot
See also:
Multi Agent Textbook available online and pdf
Cognitive code develops software personal assistant using Lua
FBI’s bot roast finds compromised computers
AI promoting ‘Flatland’
Personal agents may reach your phone before they reach your computer
